Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Senior ML Research Engineer

IC Resources
Oxford
1 year ago
Applications closed

Related Jobs

View all jobs

Machine Learning Research Engineer

Senior Research Scientist: Data Science and Machine Learning AIP

Machine Learning Engineer (Databricks)

Senior Data Scientist - AI/ML (CADD)

VP Machine Learning

Senior MLOps Platform Engineer: AI Cloud & Kubernetes

Senior ML Research Engineer

IC Resources is seeking a Machine Learning Research Engineer to join our client in Oxford, UK developing neural network technology to accelerate expensive scientific simulations with minimal data and high accuracy.

Primary responsibilities

  • Develop machine learning frameworks
  • Replicate and integrate state-of-the-art deep learning techniques from research papers to the frameworks  

Required experience

  • PhD in Computer Science, Mathematics or similar from a top
  • 4+ years’ experience working on ML product development for industrial products  
  • Python, PyTorch

Beneficial experience

Contribution to open source software

What’s on offer?

  • Competitive salary
  • Bonus scheme
  • Hybrid working

Interested?This is a great opportunity for a PhD educated Senior Machine Learning Research Engineer. Please apply now for immediate consideration and speak with Chris Wyatt at IC Resources who is recruiting for this position in Oxford, UK.

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Machine Learning Hiring Trends 2026: What to Watch Out For (For Job Seekers & Recruiters)

As we move into 2026, the machine learning jobs market in the UK is going through another big shift. Foundation models and generative AI are everywhere, companies are under pressure to show real ROI from AI, and cloud costs are being scrutinised like never before. Some organisations are slowing hiring or merging teams. Others are doubling down on machine learning, MLOps and AI platform engineering to stay competitive. The end result? Fewer fluffy “AI” roles, more focused machine learning roles with clear ownership and expectations. Whether you are a machine learning job seeker planning your next move, or a recruiter trying to build ML teams, understanding the key machine learning hiring trends for 2026 will help you stay ahead.

Machine Learning Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK machine learning hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise shipped ML/LLM features, robust evaluation, observability, safety/governance, cost control and measurable business impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for ML engineers, applied scientists, LLM application engineers, ML platform/MLOps engineers and AI product managers. Who this is for: ML engineers, applied ML/LLM engineers, LLM/retrieval engineers, ML platform/MLOps/SRE, data scientists transitioning to production ML, AI product managers & tech‑lead candidates targeting roles in the UK.

Why Machine Learning Careers in the UK Are Becoming More Multidisciplinary

Machine learning (ML) has moved from research labs into mainstream UK businesses. From healthcare diagnostics to fraud detection, autonomous vehicles to recommendation engines, ML underpins critical services and consumer experiences. But the skillset required of today’s machine learning professionals is no longer purely technical. Employers increasingly seek multidisciplinary expertise: not only coding, algorithms & statistics, but also knowledge of law, ethics, psychology, linguistics & design. This article explores why UK machine learning careers are becoming more multidisciplinary, how these fields intersect with ML roles, and what both job-seekers & employers need to understand to succeed in a rapidly changing landscape.