Software Engineer, Platform
As a Production AI Ops Lead, you will design and develop the production lifecycle of full-stack AI applications, support 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 own the production outcome, taking full accountability for the long-term performance and reliability of AI use cases deployed across international government agencies. You will ensure full-stack integrity by overseeing the health of the platform, ensuring seamless integration between the AI core and all full-stack components from APIs to UI. Additionally, you will build automated systems to monitor model performance and data drift across geographically dispersed environments, manage the technical lifecycle within diverse regulatory frameworks, lead the response for production issues in mission-critical environments, translate deep technical performance metrics into clear insights for senior international government officials, and partner with Engineering and ML teams to ensure field lessons influence future technical architecture and decisions.
Staff Engineer, Forward Deployed (R5021)
The Software Applications Engineer will provide technical expertise and support to customers during the implementation and use of Shield AI enterprise software products. This role involves working closely with customers to understand their requirements and ensuring successful product integration for AI & Autonomy development. Responsibilities include becoming an expert user of the Hivemind Enterprise Autonomy Software Development Kit; developing AI & Autonomy applications using Hivemind for unmanned platforms across air, sea, land, and space; deploying Hivemind AI infrastructure at customer sites; providing technical training to customers for their autonomy development efforts; assisting customers in integrating Hivemind autonomy to their vehicles and platforms; helping customers debug software and API integration issues; collaborating with the product and engineering teams to address customer feedback and improve products; developing and maintaining technical documentation and training materials; assisting the sales team in answering complex technical queries from customers; and traveling to and staying at customer sites as required, approximately 30% travel.
Researcher, Training - London
Design, prototype and scale up new architectures to improve model intelligence; execute and analyze experiments autonomously and collaboratively; study, debug, and optimize both model performance and computational performance; contribute to training and inference infrastructure.
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.
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