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Trustpilot is seeking a Salesforce Developer to join our GTM Technology team. You will work on large strategic projects within Salesforce Service Cloud, collaborating with commercial, marketing and customer service teams in a London-based environment. You will build robust Salesforce solutions, automate processes, and integrate with internal/external systems. The role requires strong Apex, LWC, Aura, and API skills, plus experience with CI/CD and security models.

About the role Trustpilot seeks a Salesforce Developer (Service Cloud) to join the Go To Market Technology department. The role focuses on large strategic projects and building robust, scalable solutions that align with the company's Salesforce ecosystem. What You'll Be Doing Design, develop and implement custom Salesforce solutions using Apex, Visualforce, Lightning Web Components (LWC), and other programmatic capabilities to meet the needs of the GTM Tech team and wider business. Develop and maintain complex Salesforce Flows and automation, ensuring best practices and optimal performance. Integrate Salesforce with internal and external systems using APIs and various integration patterns. Collaborate closely with the organization wide Salesforce team, participate in code reviews, and ensure solutions align with the broader Salesforce ecosystem strategy. Design and implement Salesforce best practices, focusing on scalable architecture, data integrity, security and performance optimization. Support incident management and processes directly within Salesforce through custom development and troubleshooting. Gather complex requirements, translate them into technical specifications, and deliver high quality, scalable Salesforce solutions. Build and maintain relationships with other Trustpilot teams (e.g., Global Customer Support & Trust and Safety). Deploy changes via CI/CD, applying declarative change solutions to project delivery work. Develop and maintain comprehensive technical documentation for Salesforce customizations, integrations, and deployment processes. Maintain system auditing evidence and documentation and provide mentoring and coaching to junior Administrators. Qualifications Minimum of Advanced Administrator or Platform Developer I certifications; Platform Developer II highly valued. 5+ years of hands on experience as a Salesforce Developer & Administrator, specifically with Salesforce Service Cloud. Extensive experience with Salesforce Lightning Experience development (Aura Components and Lightning Web Components). Experience with Agentforce is a strong plus. Strong proficiency in Salesforce programmatic functionalities, including Apex, Visualforce, LWC, SOQL/SOSL and platform events. Deep understanding of Salesforce APIs (REST, SOAP, Bulk) and integration patterns. Strong grasp of Salesforce security models and their programmatic implications. Proficiency in developer tools such as VSCode, Salesforce CLI, Git, and CI/CD pipelines. Excellent interpersonal and communication skills, ability to collaborate with technical and non technical stakeholders, and mentor team members. Proactive and flexible mindset, able to adapt to changing project requirements and priorities. Analytical and problem solving skills, proactive in identifying, troubleshooting and resolving complex technical issues. Benefits Competitive compensation package plus bonus. 25 days holiday per year, increasing to 28 days after 2 years of employment. Two (paid) volunteering days a year. Rich learning and development opportunities through the Trustpilot Academy and Blinkist. Comprehensive pension and life insurance, health cash plan, online GP and 24/7 Employee Assistance Plan. Full access to the Headspace mindfulness app. Paid parental leave. Season ticket loan and cycle to work scheme. Central office location with snacks and refreshments. Regular opportunities to connect and get to know colleagues through company wide celebrations, ERG activities and team socials. Access to over 4,000 deals and discounts. Equity, Diversity and Inclusion Trustpilot is an equal opportunity workplace and will consider all applications for employment without regard to race, ethnicity, national origin, religious beliefs, gender identity or expression, sexual orientation, neurodiversity, disability, age, parental or veteran status. All applicants have the right to request adjustments for the application or recruitment process. If you have a disability and would like to discuss any adjustments, please contact the Talent Acquisition Team at and quote the role you wish to apply for. For more information on privacy and data practices, see Trustpilot's Privacy Policy.

Datadog is seeking a Customer Data Scientist to partner with customers in designing and scaling experimentation programs. You will guide experiment design, metric development, and data validation to drive measurable business outcomes. In this role, you will work cross-functionally with Sales, Product, and Engineering, helping customers build trustworthy, decision-driven experimentation practices and translate analytic insights into product improvements and strategic roadmaps.

Trustpilot is hiring a Salesforce Technical Lead in its Go-to-Market Technology team. You will design, develop, and optimize Salesforce solutions using Apex, Visualforce, and LWC while leading end-to-end projects. You'll drive integration patterns, performance, and security, mentoring juniors and owning CI/CD workflows. The role offers hybrid work from a central office with remote options, extensive learning opportunities, and a broad benefits package including health, pension, and generous

Databricks is seeking a Senior Specialist Solutions Architect (ML & AI) to act as the trusted technical ML and AI expert for customers and Field Engineering. You will partner with Solution Architects to guide enterprise clients in architecting production grade ML/AI applications on the Databricks platform. You will advance GenAI, MLOps, and LLMOps, mentor colleagues, and influence the product roadmap while delivering practical tutorials, training, and thought leadership at conferences and

Docker's AI engineering team is looking for a Staff Engineer to help build the future of containerized AI agents. This role is only available to candidates currently located in London or a reasonable commuting distance to London. Responsibilities Build Containerized Agent Systems: Design and implement systems that leverage Docker containers as the ideal runtime for AI agents, ensuring isolation, scalability, and portability. Expand cagent: Maintain and evolve the open-source cagent project, adding new capabilities for containerized agent deployment and orchestration. Agent Runtime Development: Build robust infrastructure for packaging, deploying, and managing agents in containers. RAG Integration: Enhance agent capabilities with Retrieval Augmented Generation systems to provide contextual knowledge and domain expertise. Evaluation & Testing: Design evaluation frameworks to measure agent performance, reliability, and containerized deployment effectiveness. Rapid Prototyping: Iterate quickly on new agent capabilities and deployment patterns, moving from concept to production efficiently. Open Source Community: Engage with the cagent community, review contributions, and help grow the ecosystem. Cross functional Collaboration: Work closely with product managers, designers, and engineers across Docker's AI teams to integrate containerized agent capabilities into Docker's developer experience. Qualifications Strong, demonstrated, professional proficiency in Go (required). Practical experience with large language models (LLMs) and agent development. Proven ability to design scalable, distributed systems. Deep understanding of Docker, containerization best practices, and container orchestration. Experience building Retrieval Augmented Generation systems (secondary focus). Experience with AI orchestration frameworks such as CrewAI, AGNO, ADK, LangChain/LangGraph (preferred). Python proficiency for AI prototyping and tooling (preferred). Experience with Kubernetes or other container orchestration platforms (preferred). Open source contributions and community engagement (preferred). Experience with agent evaluation, reliability, and observability techniques (preferred). 8+ years of directly applicable experience. Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience. Docker embraces diversity and equal opportunity. We are committed to building a team that represents a variety of backgrounds, perspectives, and skills. We do not offer visa sponsorship for this role.

Docker is seeking a Staff Engineer for its AI engineering team in London to build containerized AI agents and scale agent runtimes. You will expand the open-source cagent project and work on RAG-enabled capabilities with cross-functional teams, focusing on reliability and production readiness. Applicants should have 8+ years in related roles, strong Go skills, and a solid background in distributed systems, Docker, and orchestration.

Mission As a Senior Specialist Solutions Architect (ML & AI), you will serve as the trusted technical ML and AI expert for Databricks customers and the Field Engineering organization. You will partner with Solution Architects to guide enterprise and strategic customers in architecting production grade ML and AI applications on the Databricks Data Intelligence Platform. You will also continue to sharpen your technical expertise in cutting edge areas like GenAI, ML, MLOps, and LLMOps, while mentoring colleagues and establishing yourself as an AI thought leader. Impact you will have Architecting Workloads: Design and implement production level ML and AI workloads, including end to end pipelines, training/inference optimization, MLOps lifecycle management, and integration with cloud native services. GenAI Leadership: Serve as a practitioner for enterprise GenAI solutions, specializing in RAG architectures, agentic systems (including tool calling, multi agent orchestration, and guardrails), AI observability, and natural language querying of structured data. Product Influence: Collaborate cross functionally with product and engineering teams to represent the voice of the customer, define priorities, and influence the platform's AI roadmap. Thought Leadership: Drive community growth and AI platform adoption through the creation of technical tutorials and training materials, as well as by presenting at industry conferences and leading hackathons. What we look for Experience: 10+ years of hands on industry DS/ML experience, with a focus on either: ML Engineering: Building/maintaining production grade cloud infrastructure (AWS/Azure/GCP) that supports deployment of ML applications and monitoring ML model performance. Data Science/AI: Applying advanced techniques in LLMs, agentic systems, vector databases, fine tuning, and deployment tools (e.g., HuggingFace, Langchain). Hands on experience working with Distributed Spark based systems. Experience with data engineering concepts or a good understanding of data engineering concepts. Pre sales or post sales experience working with external clients across a variety of industry markets. Minimum of 5+ years of customer facing experience would be preferred. Experience working with Apache Spark to process large scale distributed datasets. Communication: Proven ability to communicate and teach complex technical concepts to both technical and non technical audiences. Core Traits: Passion for lifelong learning, collaboration, and driving business value through AI. Education: Graduate degree in a quantitative discipline (e.g., Computer Science, Engineering, Statistics, Operations Research, etc) or equivalent practical experience. Can meet expectations for technical training and role specific outcomes within 3 months of hire. Can travel up to 30% when needed. Our Commitment to Diversity and Inclusion At Databricks, we are committed to fostering a diverse and inclusive culture where everyone can excel. We take great care to ensure that our hiring practices are inclusive and meet equal employment opportunity standards. Individuals looking for employment at Databricks are considered without regard to age, color, disability, ethnicity, family or marital status, gender identity or expression, language, national origin, physical or mental ability, political affiliation, race, religion, sexual orientation, socio economic status, veteran status, and other protected characteristics.

Overview Docker has been one of the most loved brands in developer tooling, trusted by more than 20 million monthly users and over 20 billion container image pulls. From solo founders to the world's largest companies, developers rely on Docker to build, share, and run their applications across our suite of products including Docker Desktop, Docker Hub, and Docker Scout. We are a globally distributed, remote first team building the tools that define how software gets built and delivered. As AI agents redefine software development, Docker is at the center of that shift, providing the sandboxed environments, verified images, and secure infrastructure that make autonomous workflows trustworthy by default. Join Docker's AI engineering team to build the future of containerized AI agents. Docker containers are the perfect vehicle to host and run AI agents - providing isolation, portability, and reproducibility. You'll be working on Docker Agent ( ) and expanding on it to enable developers to build, deploy, and scale intelligent agents using Docker's container technology. This is a greenfield opportunity to shape how developers leverage containers for AI agents at massive scale. Location & Schedule This role is only available to candidates currently located in London (or a reasonable commuting distance to London). It is remote first with a flexible hybrid schedule. Docker does not offer visa sponsorship for this role. Responsibilities Build containerized agent systems: design and implement systems that leverage Docker containers as the ideal runtime for AI agents, ensuring isolation, scalability, and portability. Expand cagent: maintain and evolve the open source cagent project, adding new capabilities for containerized agent deployment and orchestration. Agent runtime development: build robust infrastructure for packaging, deploying, and managing agents in containers. RAG integration: enhance agent capabilities with Retrieval Augmented Generation systems to provide contextual knowledge and domain expertise (secondary focus). Evaluation & testing: design robust evaluation frameworks to measure agent performance, reliability, and containerized deployment effectiveness. Rapid prototyping: iterate quickly on new agent capabilities and deployment patterns, moving from concept to production efficiently. Open source community: engage with the cagent community, review contributions, and help grow the ecosystem. Cross functional collaboration: work closely with product managers, designers, and engineers across Docker's AI teams to integrate containerized agent capabilities into Docker's developer experience. On call rotation: participate as needed in support outside of standard business hours. Qualifications Strong proficiency in Go (or the willingness and ability to ramp up). Practical experience with large language models and agent development. Proven ability to design scalable, distributed systems. Deep understanding of Docker, containerization best practices, and orchestration. Experience building Retrieval Augmented Generation systems (secondary focus). Demonstrated ability to prototype quickly and iterate based on feedback. Experience with AI frameworks such as CrewAI, AGNO, ADK, LangChain, LangGraph or similar (preferred). Python proficiency for AI prototyping and tooling (preferred). Experience with Kubernetes or other container orchestration platforms (preferred). Open source contributions and community engagement (preferred). Experience with agent evaluation, reliability, and observability techniques (preferred). What to Expect First 30 days: integrate into the AI engineering team, deep dive into Docker Agent architecture, contribute early enhancements, understand the technical stack, and collaborate with sister AI teams. First 90 days: lead significant features or improvements, enhance deployment capabilities, collaborate with the open source community, onboard new team members, and interact with internal stakeholders. One year outlook: drive major architectural decisions, help grow the team, contribute to evaluation frameworks, lead initiatives to expand containerized agent capabilities for enterprise use cases, and shape Docker's agent ecosystem. Benefits & Perks Freedom & flexibility; fit work around your life. Designated quarterly Whaleness Days plus end of year Whaleness break. Home office setup. 16 weeks of paid parental leave (after 6 months of employment). Technology stipend equivalent to $100 net/month. Paid time off plan encouraging rest. Training stipend for conferences, courses, and classes. Equity opportunities. Docker Swag. Medical benefits, retirement and holidays vary by country. Remote first culture, with offices in Seattle and Paris. Equal Employment Opportunity Docker embraces diversity and equal opportunity. We are committed to building a team that represents a variety of backgrounds, perspectives, and skills. The more inclusive we are, the better our company will be.

Docker is seeking an experienced AI engineer to join the AI engineering team focused on containerized agents. You will design and expand the Docker Agent, improve deployment patterns, and collaborate across Docker's AI teams to scale agent capabilities. The role emphasizes Go, LLMs, and distributed systems, with remote-first work and a hybrid schedule in London. Prototyping and open-source engagement are encouraged.