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Rowden

Edge computing for defence and security

Principal Machine Learning Engineer

Applied Machine Learning +1Updated on Nov 13
Full-timeBristol, UKNo salary listed

About the Job

Requirements

  • Demonstrated delivery of technical work into production or maturation to a high technology readiness level ready for deployment.
  • Deep expertise in modern machine learning and deep learning, with strong mathematical grounding and ability to reason about production trade-offs.
  • Hands-on experience training and fine-tuning large models, including distributed training across multi-GPU or multi-node environments and writing performant model-training code.
  • Practical knowledge and experience with large language model and agent-based systems, including retrieval-augmented generation, tool orchestration, prompt optimisation, and evaluation.
  • Practical experience with data engineering and deploying systems to edge or embedded hardware across the whole pipeline.
  • Excellent Python skills.
  • Strong communication skills and ability to influence without authority and bridge technical and non-technical audiences, including senior customers.
  • MSc or equivalent experience required.

Responsibilities

  • Take technical ownership of demanding machine-learning projects, set the technical approach, and make key decisions.
  • Design, build, train, and debug systems hands-on.
  • Lead large-model work, including pre-training, fine-tuning, distributed training, and evaluation at scale.
  • Contribute practical expertise to projects involving large language models and agentic systems, considering latency, cost, and reliability in constrained settings.
  • Ensure solutions work end-to-end from data pipelines and training infrastructure through deployment on edge hardware, with evaluation of model performance.
  • Represent Rowden’s machine-learning capability at exercises, trials, and customer engagements, and contribute technical expertise to bid writing.
  • Set quality standards with craft and technical leads, and mentor engineers through reviews, pairing, and example.

Desired Qualifications

  • Experience with low-level programming languages such as Rust or C++.
  • Experience with quantisation, pruning, distillation, compilation, and runtime optimisation for constrained hardware.
  • Experience at trials, exercises, or demonstrations, or working directly with users in operational or other high-stakes settings.
  • Experience with reproducible machine-learning pipelines, model versioning, continuous integration and continuous delivery, observability, and automated evaluation.
  • PhD in Artificial Intelligence, Machine Learning, Computer Science, or a related field.
  • Knowledge of PyTorch Distributed Data Parallel and Fully Sharded Data Parallel, Kubeflow, Slurm, Kubernetes, and large-scale training frameworks such as DeepSpeed, Megatron, or TorchTitan.
  • Knowledge of evaluation harnesses, prompt optimisation, and agent frameworks.
  • Knowledge of TensorRT, ONNX Runtime, vLLM, NVIDIA Triton Inference Server, KServe, and NVIDIA Jetson, including Jetson Thor.
  • Knowledge of Databricks, Apache Spark, MLflow, SQL, AWS, Google Cloud Platform, and Docker.

About the Company

Rowden

Edge computing for defence and security

Rowden Technologies designs and delivers mission-critical edge computing and distributed sensor systems for environments with limited or unreliable connectivity. It handles end-to-end design, integration, sovereign manufacturing, and long-term managed services to support defence, national security, and emergency services. Its products include Trove for frontline generative AI, Magos for cost-effective signal processing and communications production, and Dovetail for modular edge-system integration, enabling rapid, secure decision-making even in denied environments. The company differentiates itself by prioritizing edge solutions with low connectivity, acting as a prime systems integrator for large programmes, offering sovereign manufacturing and lifecycle support, and pursuing projects like automated ML deployment to UAS for AUKUS Pillar II to reduce dependence on traditional defence contractors.

Company size
51-200
Company stage
Grant
Total funding
$33.7M
Headquarters
Filton, United Kingdom
Founded
2017

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