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"Software / Machine Learning Engineer" Jobs in United Kingdom

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Software / Machine Learning Engineer

Prima · London

GBP 70000-105000 / yearMidRemote

Are you looking for a new challenge? Fancy helping us shape the future of motor insurance? Prima could be the place for you. Since 2015, we’ve been using our love of data and tech to rethink motor insurance and bring dri

Skills

awselixirnumpypandas

Discovered 6 days ago

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2027 Software Dev Engineer Intern (Machine Learning) - UK

Amazon · London

EntryFull Time, InternshipOnsite

Description Do you want to solve real customer problems through innovative technology? Do you enjoy working on scalable services in a collaborative team environment? Do you want to see your code directly impact millions

Skills

JavaPythonC++C#

Discovered 1 weeks ago

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Embedded Machine Learning Engineer - Electronics and Software

42 Technology · Cambourne

Mid

Overall purpose: Experienced embedded firmware/systems engineer with specific knowledge of machine learning and machine vision to join the Electronics and Software group. Main duties / key responsibilities: Work directly

Skills

linuxopencvpytorchtensorflow

Discovered 4 weeks ago

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Principal Machine Learning Engineer

Rowden Technologies · Bristol

SeniorHybrid

We’re building the UK's next generation engineering powerhouse, providing critical technology that strengthens national security and resilience. We specialise in turning advances in sensing, AI, and communications into o

Skills

Essential: - Builder mindset: bias to action, ownership over outcomes, and comfort working through ambiguity. - Proven delivery: you have made decisions that mattered and lived with the outcomes. You have led technical work into production, or matured it to a high technology readiness level (TRL) ready for deployment. - Deep ML and DL expertise: mastery across modern machine learning and deep learning, with strong grounding in the maths and the ability to reason about trade-offs in production. - Large model training: deep, hands-on experience training and fine-tuning large models, including distributed training across multi-GPU or multi-node environments and writing highly performant model training code. - LLMs & agentic systems: a good level of knowledge and practical experience with LLM and agent-based systems—retrieval/RAG, tool orchestration, prompt optimisation and evaluation. - Data and edge experience: practical experience with data engineering and deploying to edge or embedded hardware, enough to design systems that work across the whole pipeline. - Software development: excellent Python skills; experience with low-level languages such as Rust or C++ is desirable. - Interpersonal skills: strong communicator who can influence without authority and bridge technical and non-technical audiences, including senior customers. - Education: MSc or equivalent experience required. Desirable: - Edge and embedded deployment: quantisation, pruning, distillation, compilation and runtime optimisation for constrained hardware. - Working with end users: experience at trials, exercises or demonstrations, or working directly with users in operational or other high-stakes settings. - MLOps: reproducible pipelines, model versioning, CI/CD, observability, and automated evaluation. - Education: PhD in AI/ML/CS or a related field. Beneficial knowledge: - Distributed model training & infra: PyTorch DDP and FSDP, Kubeflow, Slurm, Kubernetes, and a large-scale training framework such as DeepSpeed, Megatron or TorchTitan. - LLM/Agent tooling: evaluation harnesses, prompt optimisation, agent frameworks. - Inference and edge: TensorRT, ONNX Runtime, vLLM, Nvidia Triton Inference Server, KServe, Nvidia Jetson (e.g. Jetson Thor). - Data and platforms: Databricks, Apache Spark, MLflow, SQL, AWS, GCP, Docker.

Discovered 4 days ago

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Lead Machine Learning Engineer

Rowden Technologies · Bristol

LeadHybrid

We’re building the UK's next generation engineering powerhouse, providing critical technology that strengthens national security and resilience. We specialise in turning advances in sensing, AI, and communications into o

Skills

Essential: - Builder mindset: bias to action, ownership over outcomes, and comfort working through ambiguity. - People leadership: experience line managing and developing engineers, with a track record of people who got better, took on more, or progressed because of your support. - Growing and resourcing teams: experience hiring during a period of growth, and balancing people across several concurrent projects or customers. - ML engineering: current, hands-on experience building, training, evaluating or deploying ML systems for real-world use, with strong Python and good software engineering habits. - Good engineering practice: a clear view of what good ML and software engineering looks like—performance, architecture, testing, code review, reproducibility, evaluation—and how to build it into a team. - Interpersonal skills: strong communicator who can mentor, influence, handle difficult conversations well, and bridge technical and non-technical audiences. - Education: MSc or equivalent experience required. Desirable: - Breadth across ML: experience with LLMs and agentic systems, edge deployment or large-scale training, enough to support engineers working in those areas. - MLOps: reproducible pipelines, model versioning, CI/CD, observability, and automated evaluation. Beneficial knowledge: - Ways of working: Agile delivery and engineering career frameworks. - General tooling and platforms: Databricks, AWS, GCP, GitHub, Docker/Kubernetes, MLflow, Jira. - Edge deployments: Nvidia Jetson (e.g. Jetson Thor), Raspberry Pi, or other embedded accelerators.

Discovered 4 days ago

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