Tether

About the job As a member of our AI research team, you will drive innovation in model compression and efficient deployment for advanced multimodal AI systems, including large language models (LLMs) and vision-language models (VLMs). Your work will focus on reducing model footprint and computational cost while preserving accuracy, enabling high-performance AI to run efficiently across resource-constrained edge devices. You will apply and advance compression techniques such as quantization, knowledge distillation, and pruning to streamline complex multimodal architectures that integrate text, images, and audio. We expect you to have deep expertise in model compression methods and a strong background in multimodal model architectures. You will adopt a hands on, research driven approach to develop, test, and implement novel compression strategies that balance model size, latency, throughput, and accuracy. Your responsibilities include building robust compression pipelines, establishing performance and fidelity metrics, and addressing bottlenecks in production inference. The ultimate goal is to deliver scalable, low memory, low latency AI systems on edge devices (i.e., smartphones) that maintain high fidelity and tangible real world value. Responsibilities Apply low bit quantization to reduce model size and inference latency for generative AI models (LLMs, VLMs, multimodal) while maintaining accuracy and output quality. Leverage knowledge distillation to transfer capabilities from larger teacher models to smaller student models, enabling efficient multimodal reasoning across text, image, and audio inputs. Implement pruning techniques to remove redundant parameters and attention heads, reducing computational overhead without sacrificing task performance. Analyze trade offs between model efficiency (size, latency, memory) and accuracy across quantization, distillation, and pruning methods; propose improvements based on empirical findings. Research and apply mixed precision quantization and other advanced compression strategies (e.g., adaptive pruning schedules, distillation with intermediate feature matching) to optimize the accuracy-performance balance. Stay current with the latest research in model compression, including emerging techniques for multimodal and generative architectures. Document methodologies, experiments, and results clearly to support reproducibility, internal collaboration, and stakeholder communication. Author technical papers and publish findings in top tier conferences (NeurIPS, ICML, ICLR, CVPR, ACL, AAAI) to advance the field of model compression for multimodal AI. Qualifications A degree in Computer Science or related field. Ideally PhD in NLP, Machine Learning, or a related field, complemented by a solid track record in AI R&D (with good publications in A conferences). Experience with PyTorch deep learning frameworks or equivalent frameworks. Hands on experience with model quantization including both Quantization Aware Training (QAT) and Post Training Quantization (PTQ). Research and hands on experience with knowledge distillation for compressing large models into smaller, efficient ones. Research and hands on experience with model pruning for compressing large models into smaller, efficient ones. Solid understanding of neural network architectures and training processes - Including transformers (e.g., LLMs, VLMs), backpropagation, optimization, and fine tuning techniques. Familiarity with C++ is a plus (especially for implementing low level quantization kernels or inference optimizations).

Tether is seeking a Senior Applied ML Engineer in the UK to design and build scalable backend systems for their media intelligence platform. Your role will focus on integrating AI/ML functionalities, ensuring the efficient processing of various media types, and mentoring junior engineers. The position demands extensive experience in backend engineering with a focus on system design and AI/ML integration. Strong proficiency in Python or Node.js is required, along with hands-on experience in cloud services like AWS and GCP. Join Tether to impact how media is analyzed and processed at scale!

Tether is seeking a member for our AI model team to drive innovation in architecture development for cutting-edge AI models. You will enhance intelligence and improve efficiency by working on LLMs and Multi-Modal systems. The ideal candidate should have a deep expertise in model structures and pre-training optimization, substantial experience with distributed frameworks, and a strong publication record in AI research. Join us to explore novel techniques for groundbreaking advancements in AI.

Tether in Greater London is looking for an AI Researcher specializing in model compression for multimodal systems. The role involves innovation in reducing model footprint and enhancing efficiency in deploying advanced AI systems like large language models. Responsibilities include applying quantization and knowledge distillation techniques, while qualifications require a PhD in a relevant field and experience with PyTorch. This position offers the chance to contribute meaningfully in a cutting-edge area of AI research.

About the job We are developing a highly scalable media intelligence platform that processes, analyzes, and structures large volumes of multimedia content across text, image, video, and audio. As a Senior Applied ML Engineer, you will architect and build the core backend systems that power media ingestion, processing workflows, metadata generation, AI-based analysis, semantic search, and retrieval across large media libraries. We are looking for a Senior Applied ML Engineer who can design, implement, optimize, and evaluate a production grade moderation pipeline using open source models. This role requires deep backend engineering expertise, strong system design capability, and practical experience integrating AI/ML systems into production workflows. You will work on complex media processing pipelines, video/audio analysis, OCR, speech to text, embedding generation, vector search, multimodal model integrations, and high throughput asynchronous workloads. You will collaborate closely with engineering leadership to define backend architecture, improve reliability and scalability, and guide other engineers in delivering secure, observable, and high performance systems. Responsibilities Backend Architecture & System Ownership Architect, build, and operate scalable backend services for a media intelligence platform, with a focus on clean, maintainable, and production ready systems. Own critical backend components end to end, from system design and API contracts through implementation, deployment, monitoring, and iteration. Drive architectural decisions across APIs, processing pipelines, distributed compute, storage, search, observability, cloud infrastructure, and model serving workflows. Design data models and storage patterns for media assets, generated metadata, embeddings, processing jobs, model outputs, search indexes, and audit trails. Design high throughput media ingestion and processing pipelines for large volumes of video, audio, image, and text content. Build distributed, event driven workflows for media processing using queues and pub/sub systems such as SQS, Kafka, Pub/Sub, or equivalent technologies. Implement reliable asynchronous processing patterns, including retries, idempotency, dead letter queues, backpressure handling, and fault tolerant job execution. AI/ML Integration & Model Workflows Lead the development and optimization of metadata extraction, content analysis, scene detection, transcription, embedding generation, and multimodal AI inference workflows. Integrate and optimize AI/ML services within backend workflows, including model APIs, embedding pipelines, OCR, speech to text, scene analysis, multimodal inference, batching, caching, and fallback strategies. Collaborate with ML engineers, data scientists, or external model providers to benchmark models, compare quality/latency trade offs, and safely roll out model upgrades. Model Serving & Performance Optimization Optimize AI/ML inference workflows for latency, throughput, reliability, and cost across both real time and batch processing paths. Work with model serving systems such as vLLM, Triton, TGI, SageMaker, Vertex AI, or custom inference services to improve batching, concurrency, warmup behavior, timeout handling, autoscaling, and GPU utilization. Evaluate and apply practical model optimization techniques such as quantization, model distillation, batching, caching, prompt optimization, and routing to smaller or cheaper models where appropriate. Design and maintain vector search and indexing systems using technologies such as Pinecone, Weaviate, Qdrant, Elastic Vectors, FAISS, pgvector, or similar tools. Build retrieval workflows that support semantic search, similarity matching, duplicate detection, media discovery, and structured metadata search. Monitor model and system performance in production, including API latency, queue depth, processing time, model error rates, GPU utilization, confidence distributions, drift signals, and cost per processed item. Infrastructure, Reliability & Observability Deploy and operate systems on AWS, GCP, Azure, or equivalent cloud platforms, including compute, storage, networking, queues, model serving infrastructure, and monitoring systems. Ensure system reliability through logging, metrics, tracing, alerting, dashboards, operational runbooks, and incident response best practices. Collaboration & Engineering Leadership Collaborate with product, design, data, and ML teams to deliver media rich, AI powered product features. Mentor junior and mid level engineers, support technical planning, review designs, and raise engineering quality across the team. Participate in code reviews, documentation, technical planning, and continuous improvement of engineering practices. Ensure code quality through testing, peer review, clear documentation, and maintainable implementation patterns. Education & Experience Bachelor's degree in Computer Science, Engineering, or equivalent practical experience. 5-7+ years of backend engineering experience, ideally building scalable distributed systems, media platforms, data pipelines, or high throughput backend services. Prior experience owning major backend modules end to end, including architecture, implementation, deployment, monitoring, and production operations. 3+ years of experience integrating AI/ML inference systems into backend workflows, including model APIs, embedding pipelines, OCR, speech to text, scene detection, or multimodal model outputs. Hands on experience creating AI powered processing pipelines for image, video, audio, or text analysis. Practical experience with production model optimization, especially for image, video, embedding, or multimodal models, including batching, caching, quantization, prompt optimization, routing strategies, latency reduction, and cost optimization. Prior experience with vector search, semantic search, media retrieval, or similarity matching systems is strongly preferred. Experience mentoring engineers, leading technical discussions, and influencing architectural decisions across backend, infrastructure, and AI/ML workflows. Technical Skills Strong expertise in Python and/or Node.js with deep understanding of building scalable RESTful APIs and backend architectures. Experience with HuggingFace transformers ecosystem and deep learning frameworks such as PyTorch and TensorFlow. Strong experience with SQL/NoSQL databases, schema design, and data modeling. Preferred exposure to distributed systems, microservices, asynchronous processing, and event driven patterns with SQS, Pub/Sub, Kafka, or other queueing/pub sub systems. Experience deploying production systems on AWS, GCP, or similar cloud platforms. Knowledge of infrastructure patterns (compute, storage, networking, observability). AI/ML Integration Experience orchestrating embedding generation, scene detection, OCR, speech to text, image classification, video analysis, and multimodal model integrations. Experience optimizing inference workflows for latency, throughput, reliability, and cost. Experience working with scalable and optimized inference settings, including tuning sampling parameters, managing output length formats, and configuring reasoning related behaviors. Familiarity with practical model optimization techniques such as batching, caching, quantization, model distillation, prompt optimization, fallback routing, and use of smaller models where appropriate. Experience working with model serving systems such as vLLM, Triton, TGI, SageMaker, Vertex AI, or custom inference services is preferred. Experience working with LLM and multi modal evaluation and benchmarking frameworks and domain specific benchmarks with the ability to interpret results and optimize model performance accordingly. System Design & Architecture Preferred understanding of distributed systems, scaling patterns, and performance engineering. Ability to design modular, maintainable, and efficient architectures. Experience with API versioning, modularization, and designing long running workflows. Understanding of performance bottlenecks and low latency backend patterns.

Tether is seeking a Senior Applied ML Engineer to design and build backend systems for a media intelligence platform. This includes architecting scalable services for media ingestion and processing, collaborating with teams, and optimizing AI/ML workflows. The ideal candidate has 5-7 years of backend engineering experience and a strong background in integrating AI systems. The position emphasizes clean design, reliability, and system performance.

Join Tether and Shape the Future of Digital Finance At Tether, we're not just building products, we're pioneering a global financial revolution. Our cutting edge solutions empower businesses-from exchanges and wallets to payment processors and ATMs-to seamlessly integrate reserve backed tokens across blockchains. By harnessing the power of blockchain technology, Tether enables you to store, send, and receive digital tokens instantly, securely, and globally, all at a fraction of the cost. Transparency is the bedrock of everything we do, ensuring trust in every transaction. Innovate with Tether Tether Finance: Our innovative product suite features the world's most trusted stablecoin, USDT, relied upon by hundreds of millions worldwide, alongside pioneering digital asset tokenization services. Tether Power: Driving sustainable growth, our energy solutions optimize excess power for Bitcoin mining using eco friendly practices in state of the art, geo diverse facilities. Tether Data: Fueling breakthroughs in AI and peer to peer technology, we reduce infrastructure costs and enhance global communications with cutting edge solutions like KEET, our flagship app that redefines secure and private data sharing. Tether Education: Democratizing access to top tier digital learning, we empower individuals to thrive in the digital and gig economies, driving global growth and opportunity. Tether Evolution: At the intersection of technology and human potential, we are pushing the boundaries of what is possible, crafting a future where innovation and human capabilities merge in powerful, unprecedented ways. Why Join Us? Our team is a global talent powerhouse, working remotely from every corner of the world. If you're passionate about making a mark in the fintech space, this is your opportunity to collaborate with some of the brightest minds, pushing boundaries and setting new standards. We've grown fast, stayed lean, and secured our place as a leader in the industry. If you have excellent English communication skills and are ready to contribute to the most innovative platform on the planet, Tether is the place for you. Are you ready to be part of the future? About the job The goal of a Product Manager is to bridge the gap between technical capabilities and business objectives, focusing on the technical aspects of product development. They work closely with engineering teams to define product roadmaps, prioritize features based on market research and customer needs, and ensure the successful delivery of technical products and services. In doing so, they manage the product vision, identify and mitigate potential risks, and maintain alignment with overall business requirements. About the challenge In this role, you will lead the vision, strategy, and execution of a GPU accelerated cloud services platform designed to empower data scientists, developers, and enterprises to build, train, and deploy AI/ML models at scale - while supporting the growth of the P2P AI ecosystem. We're seeking an experienced Technical Product Manager With a strong bias for action and measurable outcomes - someone who combines deep expertise in AI/ML technologies, cloud infrastructure, and product management with the drive to establish market leadership across strategic verticals. Potentially great fit if you have A strong product led growth mindset who wants to take their career to the next level, and move from contributing to a product area to having full ownership over the building, scaling and success of an entire new product and service lines Experience in the field of AI/ML that goes beyond consumer level and into the core of how modern AI systems behave An understanding of the intricacies of software and hardware to the extent that he/she can actively engage with highly technical stakeholders A team leader who has had close contact with operations and feels capable to work with people in all facets of a product A high agency individual who can actively set internal and external goals, team culture and liaise effectively and directly with executive management and other departments to achieve them Optionally, would be good to have crossed paths with crypto before, either as a user or professional, and can demonstrate a decent understanding of its mechanics Responsibilities Product Vision & Strategy: Define and champion the long term vision for a Cloud Services Platform (CSP) across IaaS, PaaS, and SaaS models. Align product direction with company objectives and industry trends to maintain a competitive edge and deliver sustained value. Ownership and Leadership: Own and manage a comprehensive product roadmap, prioritizing features and enhancements that drive impact. Collaborate closely with engineering and cross functional teams to ensure successful delivery of products and services. Build strong relationships with executives, partners, and industry influencers to foster strategic alliances and advocate for the platform's growth. Customer Centric Focus: Engage directly with engineers, data scientists, and researchers to identify niche market opportunities, maximize value delivery, and ensure seamless product experiences that resonate with technical users. Ecosystem Expansion: Partner with other Tether teams to design and scale a centralized, privacy preserving, and resilient infrastructure layer for P2P networks - strengthening the broader P2P ecosystem. Go To Market Strategy: Independently develop and execute launch plans in coordination with marketing and expansion teams. Drive positioning, messaging, and market entry strategies that establish the platform as a key player in select AI/ML CSP niches. Performance Tracking: Define and monitor KPIs for product engagement, customer satisfaction, and platform performance to ensure continuous optimization and alignment with success metrics. B2B Orientation: Collaborate with enterprise customers to distinguish between users and decision makers, ensuring that both receive tailored value propositions and measurable business outcomes. Qualifications 7+ years of product management experience, with at least 3 years as a technical product manager for AI infrastructure products that include: Building or managing AI platform services involving model training, fine tuning, inference optimization and quantization workflows Direct experience with GPU resource management and ML framework infrastructure (beyond API consumption of foundation models) Experience interacting with cloud platform services, such as AWS, Azure or GCP across their multiple offerings Familiarity with AI specific cloud platform services, such as TogetherAI, ScaleAI, Databricks or AWS SageMaker, at least from a consumer standpoint and strong understanding of underlying technologies Demonstrated understanding of ML model architectures, training dynamics, and optimization techniques Past experience in high growth organizations Proven track record of successful product launches Exceptional leadership, communication, prioritization and team building skills Ability to engage and evangelize a product vision to both highly technical and non technical stakeholders alike Degree in engineering, physical sciences or closely related fields Skilled in defining, tracking, and reporting on product KPIs to measure success Important information for candidates Apply only through our official channels. We do not use third party platforms or agencies for recruitment unless clearly stated. All open roles are listed on our official careers page: Verify the recruiter's identity. All our recruiters have verified LinkedIn profiles. If you're unsure, you can confirm their identity by checking their profile or contacting us through our website. Be cautious of unusual communication methods. We do not conduct interviews over WhatsApp, Telegram, or SMS. All communication is done through official company emails and platforms. Double check email addresses. All communication from us will come from emails ending We will never request payment or financial details. If someone asks for personal financial information or payment at any point during the hiring process, it is a scam. Please report it immediately. When in doubt, feel free to reach out through our official website.

About the job As a member of the AI model team, you will drive innovation in architecture development for cutting edge models of various scales, including small, large, and multi modal systems. Your work will enhance intelligence, improve efficiency, and introduce new capabilities to advance the field. You will have a deep expertise in Large Language Model (LLM) and Multi Modal architectures, a strong grasp of pre training optimization, and a hands on, research driven approach. Your mission is to explore and implement novel techniques and algorithms that lead to groundbreaking advancements: multi modal data curation and alignment, strengthening baselines, and identifying and resolving existing pre training bottlenecks to push the limits of cross modal AI performance. Responsibilities Large Scale Pre Training: Conduct foundational pre training for LLMs and Multi Modal models (integrating text, vision, audio, or other modalities) on large, distributed servers equipped with multi nodes and thousands of NVIDIA GPUs. Architecture & Alignment Innovation: Design, prototype, and scale innovative architectures, tokenizers, and cross modal alignment layers to enhance model intelligence and multi modal understanding. Data Strategy: Source, filter, and curate massive scale textual and multi modal datasets, establishing robust data pipelines for efficient pre training. Experimental Research: Independently and collaboratively execute experiments, analyze results, and refine training methodologies for optimal performance and token efficiency. Optimization & Debugging: Investigate, debug, and eliminate bottlenecks in model efficiency, computational performance, and multi modal alignment stability during long training runs. System Scalability: Contribute to the advancement of distributed training systems to ensure seamless scalability and hardware efficiency on target platforms. Qualifications A degree in Computer Science or related field. Ideally a PhD in NLP, Machine Learning, or a related field, with a solid track record in AI R&D and publications in A conferences. Hands on experience contributing to large scale LLM or Multi Modal pre training runs on large, distributed servers equipped with thousands of NVIDIA GPUs, ensuring scalability and impactful advancements in model performance. Familiarity and practical experience with large scale, distributed training frameworks, libraries, and tools. Deep knowledge of state of the art transformer and non transformer modifications aimed at enhancing intelligence, efficiency, and scalability. Strong expertise in PyTorch and Hugging Face libraries with practical experience in model development, continual pre training, and deployment.