Bjak

About the Role A1 is building a proactive AI chat app for everyday users to bring intelligence to conversations, errands, organising and workflows. Unlike traditional chat-based applications, our product focuses on achieving high reliability for long-running workflows, persistent context, and real-world task completion. The system must handle multi-step reasoning, interact with external tools, and remain reliable despite non-deterministic model behavior. As Technical Lead, Machine Learning, you own the execution layer of A1's intelligence. You translate research direction into reliable, scalable, production-grade ML systems. This role sits at the intersection of research, infrastructure, and product. You are responsible for making models trainable, deployable, observable, and performant under real-world constraints. What You'll Do Own end-to-end ML system execution: data pipelines, training workflows, evaluation systems, inference architecture, and deployment. Fine-tune and adapt models using state-of-the-art methods such as LoRA, QLoRA, SFT, DPO, and distillation. Architect and operate scalable inference systems, balancing latency, cost, and reliability. Design and maintain data systems for high-quality synthetic and real-world training data. Implement evaluation pipelines covering performance, robustness, safety, and bias, in partnership with research leadership. Own production deployment, including GPU optimization, memory efficiency, latency reduction, and scaling policies. Collaborate closely with application engineering to integrate ML systems cleanly into backend, mobile, and desktop products. Make pragmatic trade-offs and ship improvements quickly, learning from real usage. Work under real production constraints: latency, cost, reliability, and safety Outcomes Research and models reliably translate into production-ready solutions with clear performance and quality targets. ML pipelines, training loops, and inference systems are stable, efficient, and maintainable. Production issues are detected, debugged, and resolved quickly, minimizing user impact. Team members are supported, aligned, and able to deliver high-impact ML work with minimal friction. Iterations on models and systems are measurable, safe, and improve user experience over time. Tech Stack Python PyTorch / JAX GPU-based training and inference system Ideal Experience You have built or shipped real ML systems used by people, not just demos. You are comfortable working with large models and understanding their failure modes. You write strong, production-grade code and care about system correctness. You are self-directed, pragmatic, and take full ownership of outcomes. You communicate clearly and collaborate well in small, high-trust teams.

Bjak seeks a Technical Lead, Machine Learning in the United Kingdom to own the execution layer of their AI chat application. You will translate research direction into reliable, scalable ML systems, managing workflows, and ensuring models are training-ready and performant. Ideal candidates have experience building ML systems, a strong understanding of Python and frameworks like PyTorch or JAX, and a hands-on approach to system design and integration. This role combines research, product, and infrastructure aspects to enhance user interaction with the application.

About the Role A1 is building a proactive AI chat app for everyday users to bring intelligence to conversations, errands, organising and workflows. Unlike traditional chat-based applications, our product focuses on achieving high reliability for long-running workflows, persistent context, and real-world task completion. The system must handle multi-step reasoning, interact with external tools, and remain reliable despite non-deterministic model behavior. As a Senior Member of Technical Staff, Machine Learning, you are an independent owner of critical ML subsystems in production. You take ambiguous problems, design practical solutions, and ship systems that operate reliably at scale. This is a hands on, high impact role focused on depth. Focus Build core ML systems that power a proactive, long horizon AI product. Own work end to end: data preparation, training, evaluation, inference, and iteration. Turn research ideas into working systems that run reliably in production. Debug model failures and system issues using real production signals. Iterate quickly: ship, measure outcomes, refine, and repeat. Collaborate closely with research, product, and engineering to deliver real user impact. Mentor and review work from other ML engineers through example and technical judgment. Work under real production constraints: latency, cost, reliability, and safety Tech Stack Python PyTorch / JAX GPU based training and inference systems Ideal Experience You have built and shipped ML systems used by real users. You understand how modern ML models behave - and misbehave - in production. You write strong, production quality code and think in systems, not scripts. You take ownership, work independently, and push work across the finish line. You learn fast, communicate clearly, and improve through iteration. Outcomes ML models and systems in production consistently meet accuracy, latency, reliability, and efficiency targets. Complex production issues are monitored, debugged, and resolved with minimal disruption. Training, inference, and data pipelines are robust, scalable, and maintainable over time. Drives measurable improvements in ML systems based on real world signals and user feedback. Provides mentorship and technical guidance to peers, raising the overall ML engineering standard. Collaborates cross functionally to ensure ML features integrate seamlessly into products and meet business goals. How We Work The best products today in the world were built by small, world class teams. We are a high talent density and hands on team. We make decisions collectively, move at rapid speed, striking a balance between shipping high quality work and learning. Joining our team requires the ability to bring structure, exercise judgment, and execute independently. Our goal is to put in hands of our users a truly magical product Interview process If there appears to be a fit, we'll reach to schedule 3, but no more than 4 interviews. Applications are evaluated by our technical team members. Interviews will be conducted via virtual meetings and/or onsite. We value transparency and efficiency, so expect a prompt decision. If you've demonstrated the exceptional skills and mindset we're looking for, we'll extend an offer to join us. This isn't just a job offer; it's an invitation to be part of a team that's bringing AI to have practical benefits to billions globally.

About the Role A1 is building a proactive AI system that goes beyond responding to prompts - it maintains context across conversations, plans actions, and carries work forward over time. You will own the research and intelligence direction of this system. Your role is to define how AI reasons, evaluates, and improves in a product used with high frequency. What You'll Do Set and evolve the research direction for A1's core intelligence, including context representation, memory, reasoning, planning, and orchestration. Decide when to design new model architectures versus adapting or leveraging frontier open source or commercial models. Define evaluation frameworks that measure real world usefulness, robustness, safety, and long term behavior - not benchmark vanity. Own alignment, safety, and guardrail strategy as first class product concerns. Guide exploration of frontier techniques such as: retrieval augmented training mixture of experts distillation multi agent orchestration multimodal systems Shape early product intelligence direction in close partnership with product and application engineering. Set the technical bar for research rigor, judgment, and taste across the organization. Requirements Deep experience building or evolving real machine learning systems used in production. Strong technical judgment around model behavior, failure modes, and long horizon trade offs. A builder's mindset: you care about systems that work in the real world, not just ideas. Comfortable making irreversible or high impact decisions with incomplete information. Obsession with evaluation, correctness, and how systems behave over time. High ownership mentality - you operate as a founder, not a manager. If you are looking to focus primarily on publishing, incremental benchmarks, or managing a large research organization, this role will not be a fit. Tech Stack Python PyTorch / JAX GPU based training and inference system

About the Role A1 is building a proactive AI chat app for everyday users to bring intelligence to conversations, errands, organising and workflows. Unlike traditional chat-based applications, our product focuses on achieving high reliability for long-running workflows, persistent context, and real world task completion. The system must handle multi step reasoning, interact with external tools, and remain reliable despite non deterministic model behaviour. You will own how this system behaves on desktop environments. Your work focuses on reliability, performance, and real time behaviour in production desktop applications. Focus Build and maintain cross platform desktop applications using Electron. Design responsive and scalable UIs for real time collaboration. Implement desktop specific functionality including file system access, native notifications, auto updates, and deep linking. Integrate AI powered features (chat, agents, AI assistance) via backend APIs. Optimize startup time, memory usage, and runtime performance. Profile and reduce Electron overhead. Manage large local state and message history efficiently. Ensure smooth real time updates (messages, typing indicators, presence). Maintain stability across macOS and Windows environments. Ideal Experiences Proven software engineering experience. Hands on experience building production Electron applications. Strong proficiency in JavaScript and TypeScript. Experience with React or similar UI frameworks. Solid understanding of the desktop application lifecycle. Experience with IPC communication. Experience working with local storage (SQLite, IndexedDB, filesystem). Experience with WebSockets or other real time transport mechanisms. Strong debugging and performance profiling skills. Familiarity with native OS behaviours on macOS or Windows. Tech Stack Electron Node.js Typescript SQL & noSQL

Bjak is looking for an AI Research Lead in the United Kingdom to shape the research direction of an advanced AI system. You will define how AI reasons and evaluates in a production environment. The role involves guiding exploration of cutting-edge techniques and establishing evaluation frameworks. Candidates should have deep experience with machine learning systems, a strong technical mindset, and a focus on real-world impact. Familiarity with Python and frameworks like PyTorch is essential.

About the Role A1 is building a proactive AI system that users rely on daily across conversations, tools, and workflows. As an Android Developer, AI Apps, you own the Android client experience, how AI feels, behaves, and performs on mobile devices. This is not a thin client role. You will build a production Android application where AI interactions are core to the product, and performance, reliability, and clarity matter. Focus Build and maintain production Android apps using Kotlin. Integrate AI-powered features (chat, vision, voice, recommendations) via backend APIs. Design UX patterns for AI interactions, including streaming responses, retries, and partial results. Optimize performance, memory usage, and responsiveness for AI-heavy flows. Implement analytics, logging, and feedback capture to support AI evaluation and iteration. Collaborate closely with backend and ML engineers on API contracts and system behavior. Ensure app stability, security, and scalability in production environments. Ideal Experiences 3+ years of Android development experience using Kotlin. Hands-on experience integrating AI features (e.g. LLM, vision, speech APIs). Strong understanding of asynchronous programming (Coroutines, Flow). Familiarity with REST or gRPC APIs and structured data formats. Strong debugging and performance profiling skills. Comfort building in environments with latency, partial failure, and non-deterministic behavior. Experience with MLKit or light on-device inference. Published production apps on the Google Play Store. Outcomes Stable, smooth, and reliable real-world use android applications. Performance is optimized: responsive, low-latency, and efficient on memory and CPU. Production issues are detected early, monitored effectively, and resolved with clear root cause analysis. Tech Stack Kotlin / Java SQL / noSQL TensorFlow Lite (on device inference)

About the Role A1 is building a proactive AI chat app for everyday users to bring intelligence to conversations, errands, organising and workflows. Unlike traditional chat-based applications, our product focuses on achieving high reliability for long-running workflows, persistent context, and real-world task completion. The system must handle multi-step reasoning, interact with external tools, and remain reliable despite non-deterministic model behavior. As Technical Lead, Machine Learning, you own the execution layer of A1's intelligence. You translate research direction into reliable, scalable, production-grade ML systems. This role sits at the intersection of research, infrastructure, and product. You are responsible for making models trainable, deployable, observable, and performant under real-world constraints. What You'll Do Own end-to-end ML system execution: data pipelines, training workflows, evaluation systems, inference architecture, and deployment. Fine-tune and adapt models using state-of-the-art methods such as LoRA, QLoRA, SFT, DPO, and distillation. Architect and operate scalable inference systems, balancing latency, cost, and reliability. Design and maintain data systems for high-quality synthetic and real-world training data. Implement evaluation pipelines covering performance, robustness, safety, and bias, in partnership with research leadership. Own production deployment, including GPU optimization, memory efficiency, latency reduction, and scaling policies. Collaborate closely with application engineering to integrate ML systems cleanly into backend, mobile, and desktop products. Make pragmatic trade-offs and ship improvements quickly, learning from real usage. Work under real production constraints: latency, cost, reliability, and safety Outcomes Research and models reliably translate into production-ready solutions with clear performance and quality targets. ML pipelines, training loops, and inference systems are stable, efficient, and maintainable. Production issues are detected, debugged, and resolved quickly, minimizing user impact. Team members are supported, aligned, and able to deliver high-impact ML work with minimal friction. Iterations on models and systems are measurable, safe, and improve user experience over time. Tech Stack Python PyTorch / JAX GPU-based training and inference system Ideal Experience You have built or shipped real ML systems used by people, not just demos. You are comfortable working with large models and understanding their failure modes. You write strong, production-grade code and care about system correctness. You are self-directed, pragmatic, and take full ownership of outcomes. You communicate clearly and collaborate well in small, high-trust teams.

About the Role A1 is building a proactive AI system that understands context across conversations, plans actions, and carries work forward over time. You will be responsible for turning research direction into working, production-grade ML systems. This role owns the execution layer of A1's intelligence - training pipelines, inference systems, evaluation tooling, and deployment. Focus Build and own end-to-end ML pipelines spanning data, training, evaluation, inference, and deployment. Fine-tune and adapt models using state-of-the-art methods such as LoRA, QLoRA, SFT, DPO, and distillation. Architect and operate scalable inference systems, balancing latency, cost, and reliability. Design and maintain data systems for high-quality synthetic and real-world training data. Implement evaluation pipelines covering performance, robustness, safety, and bias, in partnership with research leadership. Own production deployment, including GPU optimization, memory efficiency, latency reduction, and scaling policies. Collaborate closely with application engineering to integrate ML systems cleanly into backend, mobile, and desktop products. Make pragmatic trade-offs and ship improvements quickly, learning from real usage. Work under real production constraints: latency, cost, reliability, and safety Requirements Strong background in deep learning and transformer-based architectures. Hands on experience training, fine tuning, or deploying large scale ML models in production. Proficiency with at least one modern ML framework (e.g. PyTorch, JAX), and ability to learn others quickly. Experience with distributed training and inference frameworks (e.g. DeepSpeed, FSDP, Megatron, ZeRO, Ray). Strong software engineering fundamentals - you write robust, maintainable, production grade systems. Experience with GPU optimization, including memory efficiency, quantization, and mixed precision. Comfort owning ambiguous, zero to one ML systems end to end. A bias toward shipping, learning fast, and improving systems through iteration. Ideal Experience Experience with LLM inference frameworks such as vLLM, TensorRT LLM, or FasterTransformer. Contributions to open source ML or systems libraries. Background in scientific computing, compilers, or GPU kernels. Experience with RLHF pipelines (PPO, DPO, ORPO). Experience training or deploying multimodal or diffusion models. Experience with large scale data processing (Apache Arrow, Spark, Ray). How We Work The best products today in the world were built by small, world class teams. We are a high talent density and hands on team. We make decisions collectively, move at rapid speed, striking a balance between shipping high quality work and learning. Joining our team requires the ability to bring structure, exercise judgment, and execute independently. Our goal is to put in hands of our users a truly magical product Interview process If there appears to be a fit, we'll reach to schedule 3, but no more than 4 interviews. Applications are evaluated by our technical team members. Interviews will be conducted via virtual meetings and/or onsite. We value transparency and efficiency, so expect a prompt decision. If you've demonstrated the exceptional skills and mindset we're looking for, we'll extend an offer to join us. This isn't just a job offer; it's an invitation to be part of a team that's bringing AI to have practical benefits to billions globally.

Bjak is seeking a Technical Lead in Machine Learning to oversee the execution layer of their AI chat app. This role involves managing ML systems, ensuring they are trainable and deployable under real-world constraints. The ideal candidate will have experience building scalable and efficient ML pipelines, along with expertise in languages like Python and libraries such as PyTorch and JAX. Join Bjak to help deliver high-impact solutions in a dynamic environment.

Bjak is looking for a software engineer to help build a proactive AI chat application aimed at enhancing everyday workflows. You will focus on creating stable and performant desktop applications using Electron. Ideal candidates should have strong expertise in JavaScript and TypeScript, as well as experience with building production Electron applications. Your role will involve designing efficient UIs and optimizing performance across macOS and Windows environments.

About the Role A1 is building a proactive AI system that understands context across conversations, plans actions, and carries work forward over time. As an Applied AI Engineer, you will turn model capabilities into real product behavior. You will own problems end-to-end, from shaping model behavior, to building the systems around it, to ensuring it performs reliably in production. This role sits at the intersection of machine learning, systems, and product, focusing on making AI actually work for users, not just in demos, but in real-world usage. Focus Build and ship AI features end-to-end (model system user experience) Design and iterate on prompts, tools, memory, and agent workflows Turn raw model outputs into structured, reliable, and predictable behaviors Debug issues across the full stack (model, orchestration, infra, UX) Optimize for latency, cost, and production reliability Develop lightweight evaluation frameworks to measure real-world performance Work closely with product and engineering to translate ambiguous problems into working systems Tech Stack Python PyTorch / JAX LLMs (OpenAI style APIs, LLaMA, Qwen, etc.) Inference / serving (e.g. vLLM) Vector DB Ideal Experience Strong foundation in machine learning and modern neural network architectures. Hands on experience with training, fine tuning, or deploying ML models Ability to write clean, production quality code Comfort working across abstraction layers (model infra product) Strong problem solving skills in ambiguous, fast moving environments Bias toward shipping, iteration, and continuous improvement Outcomes ML models in production meet expected accuracy, latency, and reliability targets. Production issues are identified quickly, debugged effectively, and root causes addressed. Data pipelines, training loops, and inference systems are robust, reproducible, and maintainable. Collaborates effectively with engineers, product, and research teams to deliver reliable ML powered features. Iterations on models and systems are driven by real world signals and measurable improvements.

Bjak is seeking an Applied AI Engineer to develop and implement proactive AI capabilities. This role focuses on bridging machine learning and product functionality, ensuring robust AI behavior for real-world applications. Candidates should have a strong foundation in machine learning, proficiency in tools like Python, PyTorch, and JAX, and the ability to debug issues across various abstraction layers. Join Bjak to drive innovative AI solutions that meet user needs.

Bjak is seeking a Senior Member of Technical Staff in Machine Learning to develop and manage ML subsystems for a proactive AI chat application. This role involves building systems from data preparation to deployment, ensuring they meet operational standards. Strong skills in Python, PyTorch, and JAX are essential. Successful candidates will have experience in building production ML systems and will thrive in a collaborative, fast-paced environment focused on innovative AI solutions.

A leading digital financial platform is seeking a UI/UX Designer to craft intuitive mobile experiences for users. You will design mobile interfaces for iOS and Android apps, focusing on user needs and usability standards. Ideal candidates should have 2-4 years of mobile design experience, a strong portfolio, and proficiency in Figma. This role offers flexible remote work options and opportunities for accelerated career growth within a collaborative team environment.

Bjak is seeking a Senior Engineering Leader for its AI chat application platform. This role demands a strategic owner for application engineering across backend, mobile, and desktop. Key responsibilities include leading a senior applications team, setting architectural directions, and making critical technical decisions to ensure reliable user experiences. Candidates should have a deep understanding of AI product delivery, robust system design skills, and the ability to work across platforms. Join Bjak to turn AI intelligence into a market-leading product.

Bjak in the United Kingdom is seeking a Backend Engineer to build and maintain AI-powered backend systems. You will work on optimizing performance and reliability, designing inference pipelines, and ensuring smooth operations in production. Ideal candidates have strong backend engineering skills, familiarity with AI technologies, and experience managing high-throughput services. This role challenges you to not only ship features but also enhance them based on real production behavior, making a tangible impact on AI-driven user experiences.

About the Role A1 is building a proactive AI chat app for everyday users to bring intelligence to conversations, errands, organising and workflows. Unlike traditional chat-based applications, our product focuses on achieving high reliability for long-running workflows, persistent context, and real-world task completion. The system must handle multi-step reasoning, interact with external tools, and remain reliable despite non-deterministic model behavior. We are looking for a Full Stack Engineer - AI Systems to build the product layer that turns these capabilities into usable, production-grade workflows. This includes designing how agents operate, fail, recover, and deliver consistent value to users. Focus Build end-to-end product features across frontend, backend, and AI integrations Design agent workflows that handle planning, tool use, failure, and recovery across multiple steps. Integrate LLMs, memory, and external tools into systems that behave reliably under real-world conditions Design real-time AI interactions with streaming, partial results, and tight latency constraints Improve system reliability, observability, and fallback mechanisms Collaborate closely with ML, backend, and product teams to ship features end-to-end Continuously iterate based on real usage and failure modes Ideal Experiences Strong experience in full stack engineering (frontend + backend) Solid understanding of system design and API architecture Experience working with LLMs, RAG systems, or AI-powered applications Ability to handle ambiguity and make pragmatic engineering decisions Strong ownership - able to take features from idea to production Comfort working in fast-moving environments with evolving requirements Outcomes Own and ship AI-native product features that move beyond chat into persistent, goal-driven workflows Design and deploy agent workflows that reliably complete multi-step tasks across tools and sessions Reduce latency and improve responsiveness of AI interactions while maintaining output quality Build robust fallback and recovery mechanisms for LLM and tool failures in production environments Improve the success rate and reliability of AI-driven workflows through iteration, evaluation, and monitoring Establish patterns and abstractions for integrating LLMs, memory, and external tools into scalable product systems Contribute to a product experience where AI feels proactive, consistent, and dependable over time Tech Stack Next.js Python NodeJs Pytorch OpenAI / Anthropic / open-source LLMs SQl & noSQL Kubernetes Docker

Bjak is seeking a Full Stack Engineer - AI Systems in the United Kingdom to develop an AI chat application that emphasizes reliability and persistent workflows. Candidates should have strong experience across the full technology stack, including Next.js and Python, and be familiar with integrating AI and LLMs into products. The role demands strong problem-solving skills and ownership of features from inception to production, contributing to a proactive and user-friendly experience.

About the Role A1 is building a proactive AI chat app for everyday users to bring intelligence to conversations, errands, organising and workflows. Unlike traditional chat-based applications, our product focuses on achieving high reliability for long-running workflows, persistent context, and real-world task completion. The system must handle multi-step reasoning, interact with external tools, and remain reliable despite non-deterministic model behavior. Turn intelligence into a real product. You are responsible for the engineering systems, teams, and execution that turn ML capability into reliable, fast, and intuitive user experiences across backend, mobile, and desktop platforms. This role is a senior engineering leader who stays close to the product, makes hard technical decisions, and holds a high bar for quality and delivery. What You'll be Doing Own the application engineering strategy and execution across backend, mobile, and desktop. Lead and grow a small, senior applications team, including backend, mobile, and desktop engineers. Set the architectural direction for AI-powered product workflows, APIs, and client integrations. Ensure AI capabilities are integrated into the product with clear abstractions, predictable behavior, and graceful failure modes. Partner closely with Machine Learning leadership to translate model capability into shippable product features. Make high-impact decisions across latency, cost, reliability, security, and user experience. Establish clear ownership boundaries across backend, mobile, and desktop to prevent architectural drift. Ensure production readiness: observability, monitoring, retries, fallbacks, privacy, and cost controls. Balance speed and discipline - shipping quickly without compromising long-term system quality. What You Will Need Significant experience leading application or product engineering for complex systems. Strong hands on background building and shipping backend systems used in production. Experience delivering AI-powered products, with a deep understanding of model integration trade offs. Excellent system design skills across services, APIs, clients, and data flows. Comfort operating across platforms (backend, mobile, desktop), even if your depth is in one. Proven ability to make high-impact technical decisions under ambiguity. A strong bias toward shipping, iteration, and learning from real world usage. Low ego, strong judgment, and the ability to raise the technical bar for the entire applications organization. Outcomes Reliable, low-latency AI user experiences across backend, mobile, and desktop. Clear architectural boundaries that allow teams to move fast without constant rewrites. Measurable improvements in system performance, stability, and production reliability. Strong observability and operational discipline across all application systems. A small, senior team that ships frequently, owns what they build, and raises the technical bar. Team members are empowered, growing in their roles, and able to make high-impact contributions.