2 days ago
Bristol, United KingdomStaff+
Responsibilities
- Take technical ownership of demanding machine learning projects and make key architectural and implementation decisions.
- Design, build, train, debug, and deploy machine learning and deep learning systems hands-on.
- Lead pre-training, fine-tuning, distributed training, and large-scale evaluation of large models.
- Develop and advise on LLM and agentic systems, including retrieval, tool orchestration, prompt optimization, latency, cost, and reliability.
- Deliver solutions end-to-end from data pipelines and training infrastructure through deployment on edge or embedded hardware.
- Lead technical representation at exercises, trials, customer engagements, and partner interactions, and contribute to bid writing.
- Set technical standards and mentor engineers through reviews, pairing, and example.
Requirements
- Proven experience delivering technical work into production or maturing it to a deployment-ready technology readiness level.
- Mastery of modern machine learning and deep learning, with strong mathematical foundations and production trade-off reasoning.
- Deep hands-on experience training and fine-tuning large models using distributed multi-GPU or multi-node environments and performant training code.
- Practical experience with LLM and agent-based systems, including retrieval/RAG, tool orchestration, prompt optimization, and evaluation.
- Practical data engineering and edge or embedded hardware deployment experience across the full ML pipeline.
- Excellent Python skills; Rust or C++ experience is desirable.
- Strong communication and influencing skills across technical, non-technical, and senior customer audiences.
- MSc or equivalent experience required.
- Experience with edge optimization techniques such as quantization, pruning, distillation, compilation, and runtime optimization is desirable.
- Experience working with end users in trials, exercises, demonstrations, operational, or other high-stakes settings is desirable.
- Experience with reproducible MLOps pipelines, model versioning, CI/CD, observability, and automated evaluation is desirable.
- PhD in AI, ML, computer science, or a related field is beneficial.
- Candidates must be eligible for Developed Vetting clearance.
Benefits
- Hybrid working is supported, with a minimum or typical average of three days per week onsite at the Bristol headquarters.
- Occasional travel to customer sites and international travel a few times per year is required.
- Flexible working, part-time working discussions, and workplace adjustments are supported.
- Inclusive workplace and disability support are provided.
Tech Stack
Categories
About Rowden
Rowden builds mission-critical, real-time edge computing systems that turn advances in sensing, AI, and communications into operational capability in degraded or denied environments. It provides systems integration, software, and technology services to national security, defense, and critical industry customers, improving decision-making for frontline teams. Founded in 2017 and headquartered in Bristol, England, the privately held company operates over 20,000 square feet of engineering and manufacturing facilities.