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

Apply Now

Senior AI Software Engineer

European Tech Recruit
Cambridge
9 months ago
Applications closed

Related Jobs

View all jobs

Senior Machine Learning Engineer, Scaling World Models

Senior AI Agent Engineer (Machine Learning)

Senior Machine Learning Engineer, Data for Embodied AI

Senior Machine Learning Engineer, Data for Embodied AI

Staff Machine Learning Engineer - Autonomy

Machine Learning Engineer - Autonomy

Required Qualifications:

  • Extensive experience in optimising AI chip architectures and AI systems, with deep familiarity with mainstream heterogeneous computing software and hardware architectures. Comprehensive expertise spanning applications, foundational software, and chip design.
  • Hands-on experience in at least one of the following areas: numerical computation, compilation, algorithm and chip co-design, runtime systems, or shared memory management.
  • Solid understanding of AI industry application scenarios, mainstream models, and algorithm development trends, with the ability to derive chip-layer requirements from these insights.
  • Expertise in analysing workload sensitivity to micro-architecture features, evaluating performance trade-offs, and providing recommendations to optimise both micro-architecture and application software.
  • Familiarity with the performance impact of various compute, memory, and communication configurations, as well as hardware and software implementation choices for AI acceleration.
  • Proficiency with GPU compute APIs like CUDA or OpenCL, and experience leveraging GPU/NPU-optimised libraries to enhance performance.
  • Practical experience in developing deep learning frameworks, compilers, or system software.
  • Strong background in compiler optimisation techniques; familiarity with LLVM-MLIR is a plus.
  • Proficiency in software development using C/C++ and Python.


Desired Qualifications:

  • Relevant experience in multiple subfields of AI, including application algorithms, frameworks, runtime systems, modelling and simulation, and compilers.
  • In-depth understanding of innovative methods, platforms, and tools used by leading AI manufacturers, with proven experience in translating academic or research achievements into commercial products.
  • Experience with GPU acceleration using AMD or NVIDIA GPUs.
  • Expertise in developing inference backends and compilers for GPU or NPU systems.
  • Proficiency with AI/ML inference frameworks such as ONNXRuntime, IREE, or TVM.
  • Practical experience deploying AI models in production environments.

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.

Jobs

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.

Jobs

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.

Jobs

Machine Learning Team Structures Explained: Who Does What in a Modern Machine Learning Department

Machine learning is now central to many advanced data-driven products and services across the UK. Whether you work in finance, healthcare, retail, autonomous vehicles, recommendation systems, robotics, or consumer applications, there’s a need for dedicated machine learning teams that can deliver models into production, maintain them, keep them secure, efficient, fair, and aligned with business objectives. If you’re hiring for or applying to ML roles via MachineLearningJobs.co.uk, this article will help you understand what roles are typically present in a mature machine learning department, how they collaborate through project lifecycles, what skills and qualifications UK employers look for, what the career paths and salaries are, current trends and challenges, and how to build an effective ML team.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.