Associate
As an Associate, you will manage individual work streams to support the successful delivery and implementation of bespoke AI solutions. You will work side-by-side with Government stakeholders to understand critical mission challenges and user needs, conduct open-source analysis to support the development of AI tools, translate complex operational requirements into clear user stories for delivery and engineering teams, and design and test workflows that support user-centric decision-making in high-stakes environments. Additionally, you will collaborate closely with technical teams to ensure projects run smoothly and solutions meet customer needs, and contribute to internal thought leadership, strategy, and capability development across the team.
Software engineer, generative AI (UK)
Design and develop robust, secure, and scalable generative AI services and applications using Python and modern frameworks to drive enterprise-wide transformation; build and optimize high-performance, low-latency APIs and microservices to integrate advanced AI models and sophisticated agentic workflows into the core platform; make meaningful system design decisions and own the architecture of core platform components from initial proposal through production deployment; implement and maintain responsive user interfaces using technologies like React and TypeScript; clearly communicate changes, plans, and proposals to cross-functional teams and collaborate with product managers, data scientists, and DevOps engineers; partner with DevOps teams to build continuous deployment, logging, and monitoring systems to ensure top-tier performance, security, and reliability across distributed workloads.
Data Scientist
The Data Scientist will train, evaluate, and iterate on machine learning models for customer feedback tasks, contributing to the custom fine-tuning pipelines and running experiments with rigorous documentation. They will build and maintain LLM-powered features including retrieval pipelines, reranking systems, and insight generation with guidance from senior team members. They will contribute to evaluation frameworks by helping build test sets, defining metrics, and assessing model quality across classification, extraction, and generative tasks. The role involves working on semantic search and retrieval, developing a strong understanding of embedding-based approaches and beyond, writing clean, well-tested code, and collaborating with Engineering on model integration, data pipelines, and monitoring. Additionally, the Data Scientist will work with the wider Data Science team to translate business and product requirements into practical ML experiments and solutions and stay updated with relevant research to bring useful ideas into team discussions and experiments.
Lead Data Scientist
As a Lead Data Scientist, you are responsible for setting the technical direction for complex, business-critical projects, balancing trade-offs between speed, innovation, and reliability, designing and implementing reliable, production-grade technical solutions with comprehensive documentation, defining project problems and developing clear roadmaps, overseeing end-to-end delivery across multi-disciplinary workstreams, leading technical scoping and feasibility studies for high-value sales and strategic engagements, managing relationships and communications with demanding clients to foster trust and align technical solutions with long-term commercial goals, driving the adoption of best practices and robust technical processes across the wider Data Science craft, and mentoring and developing other data scientists and team members to contribute to the growth and technical excellence of the organisation.
Legal Advisor (US Bar Admitted) - Freelance AI Trainer
Contributors may generate prompts that challenge AI; evaluate AI-generated solutions for correctness, assumptions, and logic; improve AI reasoning to align with first principles and accepted standards; and apply structured scoring criteria to assess multi-step problem solving.
Go-to-Market - Cardiff, United Kingdom
Own the end-to-end technical onboarding experience for new enterprise customers, from kickoff through successful integration. Build and maintain integration scripts, tooling, and documentation to accelerate customer time-to-value. Serve as the primary technical point of contact during the onboarding phase, translating customer requirements into actionable engineering solutions. Diagnose integration issues across customer environments (APIs, data pipelines, cloud infrastructure) and drive them to resolution. Collaborate closely with product and engineering to surface customer feedback and influence the roadmap. Develop reusable onboarding playbooks and internal tooling to scale the deployment process. Travel to customer sites occasionally for critical onboarding milestones or technical workshops.
Software quality engineer (US)
Define and implement comprehensive quality assurance strategies and test plans for AI agents and LLM-powered applications to ensure product reliability and performance. Design and develop automation frameworks, creating robust, scalable, and maintainable automated test frameworks or enhancing existing ones using languages such as Typescript and Python. Collaborate with product managers, machine learning engineers, and data scientists to understand AI features and model behaviors, translating these into test cases and validation criteria. Drive continuous improvement of testing processes and infrastructure by integrating automated checks within CI/CD pipelines for rapid, high-quality releases. Identify, document, and track software defects and inconsistencies, performing root cause analysis to provide actionable feedback to development teams. Monitor production systems and AI model performance to identify potential issues and contribute to post-release quality validation. Champion quality best practices across engineering teams, fostering a culture of ownership and continuous improvement. Design, manage, and maintain test data strategies and mock services to ensure stable, isolated, and repeatable test execution. Design, develop, or integrate agentic AI systems, AI skills, and the Model Context Protocol (MCP). Manage the full defect lifecycle by analyzing customer feedback and debugging logs to identify, prioritize, and track software bugs, collaborating with development teams to ensure timely resolution.
Software quality engineer (UK)
Define and implement comprehensive quality assurance strategies and test plans for AI agents and LLM-powered applications to ensure product reliability and performance. Design and develop automation frameworks by creating robust, scalable, and maintainable automated test frameworks or enhancing existing ones, requiring proficiency in languages such as Typescript or Python. Collaborate closely with product managers, machine learning engineers, and data scientists to understand complex AI features and model behaviors, translating these into effective test cases and validation criteria. Drive continuous improvements in testing processes and infrastructure by integrating automated checks within CI/CD pipelines for rapid, high-quality releases. Identify, document, and track software defects, performing root cause analysis to provide actionable feedback to development teams. Monitor production systems and AI model performance to proactively identify potential issues and contribute to post-release quality validation. Champion quality best practices across engineering teams to foster ownership and continuous improvement in delivering AI solutions. Design, manage, and maintain test data strategies and mock services to ensure stable, isolated, and repeatable test execution. Manage the full defect lifecycle by analysing customer feedback and debugging logs to identify, prioritise, and track software bugs, collaborating closely with development teams for timely resolution. Additionally, have experience designing, developing, or integrating agentic AI systems, AI skills, and the Model Context Protocol (MCP).
Head of Policy & Security Research Lab
As a Production AI Ops Lead within Scale's Global Public Sector team, you will design and develop the production lifecycle of full-stack AI applications, supporting end-to-end system reliability, real-time inference observability, sovereign data orchestration, high-security software integration, and resilient cloud infrastructure for international government partners. You will take full accountability for the long-term performance and reliability of AI use cases deployed across international government agencies, oversee the end-to-end health of the platform ensuring seamless integration between AI core and full-stack components, build automated systems to monitor model performance and data drift in dispersed environments, manage the technical lifecycle within diverse regulatory frameworks, lead incident response for production issues in mission-critical environments, translate technical performance metrics into clear insights for senior government officials, and partner with Engineering and ML teams to ensure field lessons influence future technical architecture and decisions.
Senior Backend Engineer
Design, build, and maintain scalable backend services and APIs that power Chattermill’s core analytics platform. Architect reliable, maintainable distributed systems and contribute to the evolution of backend service design and infrastructure. Own end-to-end delivery of backend engineering workstreams, from technical scoping and architecture through to implementation, testing, observability, and production support. Integrate language models, agentic frameworks, and AI pipelines into core product and backend services. Drive performance, reliability, and observability across high-throughput distributed data systems, including logging, tracing, alerting, and incident response. Work with cloud infrastructure and distributed systems in GCP (preferred) or AWS environments. Collaborate closely with Product to define scope, shape technical solutions, and explore new platform capabilities and features. Contribute to engineering excellence through code reviews, architectural discussions, and continuous improvement of development standards across the team.
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