Senior AI Research Engineer

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior AI Research Engineer based in United Kingdom.

This role offers the opportunity to build and scale the infrastructure powering next-generation AI solutions for industrial automation. You will work at the intersection of machine learning engineering, MLOps, and research, enabling teams to move advanced AI concepts into production faster. The position focuses on designing reliable systems, optimizing research workflows, and supporting the deployment of intelligent control technologies. You will collaborate with researchers, engineers, and cross-functional teams to transform complex AI experiments into impactful real-world applications.

With ownership across the research-to-production lifecycle, you will influence technical direction while solving challenging engineering problems s is an ideal opportunity for an experienced engineer passionate about AI, distributed systems, and creating technology with measurable real-world impact.

• Own and evolve research infrastructure end-to-end, including experiment orchestration, distributed training, model tracking, evaluation workflows, and automated deployment systems.
• Build and scale distributed computing solutions for machine learning workloads, including multi-node GPU environments, data pipelines, and cost-efficient infrastructure management across cloud platforms.
• Improve research and development velocity through performance engineering, including optimizing simulators, training pipelines, profiling bottlenecks, and implementing scalable solutions.
• Act as a bridge between research and production engineering teams, helping transform AI breakthroughs into reliable production-ready systems.
• Develop a deep understanding of internal platforms, tools, and technical capabilities to support effective customer-facing solutions.
• Maintain clear documentation of research projects, engineering decisions, products, and operational processes.
• Contribute to medium- and long-term technical decisions that shape research infrastructure and engineering strategy.
• Lead projects from concept to delivery, taking ownership of execution, prioritization, and successful outcomes.
• Mentor team members, share technical knowledge, and support collaborative problem-solving across engineering teams.
• Continuously improve development practices, tooling, and infrastructure to accelerate AI research and deployment.

• 4+ years of relevant professional experience in software engineering, machine learning engineering, MLOps, or related technical fields.
• Proven experience leading technical projects and owning delivery from initial concept through implementation.
• Previous experience working in machine learning research and development environments, ideally connecting research initiatives with production systems.
• Strong understanding of machine learning and MLOps concepts, including experiment tracking, model lifecycle management, deployment processes, and systems involving non-deterministic components.
• Strong programming skills in Python and familiarity with lower-level programming languages such as C++ or Rust.
• Solid engineering foundation combined with scientific understanding in areas such as machine learning, optimization, control systems, or physical sciences.
• Experience designing scalable infrastructure for AI workloads, distributed computing, or cloud-based environments.
• Strong problem-solving abilities, curiosity, and willingness to explore unfamiliar technical domains.
• Excellent organizational, communication, and collaboration skills in a remote and international environment.
• Alignment with values centered around transparency, collaboration, ownership, operational excellence, and empathy.

• Experience with machine learning research, AI systems, or MLOps-focused engineering.
• Familiarity with reinforcement learning, simulation environments, or control systems.
• Experience using distributed computing frameworks such as Ray and managing GPU workloads across multiple nodes.
• Knowledge of platforms and tools such as PyTorch, Sci Py, scikit-learn, Num Py, pandas, MLflow, Docker, Kubernetes, and cloud infrastructure.
• Understanding of industrial systems, including heating, cooling, manufacturing, or data center environments.
• Scientific or technical background that enables effective collaboration with research-focused teams.

• Competitive base salary ranging from £92,065 to £173,648
  , depending on location tier, experience, qualifications, and other relevant factors.
• Eligibility for meaningful equity participation.
• Fully remote work environment with flexibility across different locations and time zones.
• Medical, dental, and vision insurance, with benefits varying by region.
• Unlimited paid time off with a required minimum of 20 days per year.
• Paid parental leave, depending on regional policies.
• Flexible stipends supporting workspace setup, personal…