AI Systems Engineer | VC-backed Startup | TWE46402

Join a venture-backed AI infrastructure startup building autonomous systems that improve the performance, efficiency and scalability of modern machine learning workloads.

The team is developing next-generation optimisation technology that enables AI systems to become faster, more efficient and increasingly autonomous.

Working at the intersection of machine learning, systems engineering and high-performance computing, you'll help solve some of the most challenging performance problems in modern AI while contributing to technology already being adopted by enterprise customers.

Role

As an AI Infrastructure Engineer, you will build autonomous systems that optimise modern AI workloads.

Working across GPU programming, machine learning infrastructure and automated optimisation techniques, you'll tackle complex performance challenges with real production impact.

Responsibilities

• Develop, optimise and deploy performance-critical software for modern AI workloads, including low-level GPU acceleration where appropriate.
• Own production machine learning infrastructure and AI systems from design through to deployment.
• Measure how low-level performance improvements affect end-to-end application efficiency.
• Build infrastructure that enables autonomous agents to run large-scale optimisation experiments, including compilation, validation, benchmarking, scoring and experiment tracking.
• Design automated optimisation and search strategies that continuously improve system performance.
• Investigate performance bottlenecks using profiling tools and translate findings into practical engineering improvements.
• Share technical knowledge through documentation, technical talks and open-source contributions where appropriate.
• For lead-level candidates, hire, mentor and develop a small team of high-calibre engineers and researchers.
• Key Skills
• Strong experience developing performance-critical software for GPU-accelerated computing environments.
• Deep understanding of GPU performance optimisation, including memory access, parallel execution, register utilisation, occupancy and instruction scheduling.
• Experience optimising mixed-precision and quantised AI workloads, including formats such as INT4, INT8 and FP8.
• Understanding of distributed AI systems and performance optimisation techniques.
• Ability to diagnose and resolve performance bottlenecks using modern profiling tools.
• Experience with GPU programming technologies such as CUDA, Triton, Cu Te, Helion or similar, with confidence working close to the hardware where required.
• Knowledge of modern GPU architectures and how performance characteristics vary across hardware generations.
• Practical understanding of transformer architectures, attention mechanisms, KV caching and how model design influences system performance.
• Experience with production AI training or inference frameworks such as v LLM, Megatron-LM or similar.
• Experience building performance-critical tooling such as compilers, profilers, auto-tuners, optimisation frameworks or developer tooling.
• Strong understanding of optimisation algorithms, automated search techniques or evolutionary methods.
• Contributions to open-source AI infrastructure or performance optimisation projects.
• Published research in machine learning systems, GPU optimisation, high-performance computing or related fields.
• Experience with AMD GPUs, Apple MLX, edge AI hardware or HPC environments.
• Familiarity with emerging GPU programming tools and compiler technologies.
• Experience building AI agent systems or autonomous software.
• Public technical work demonstrating expertise in machine learning systems, GPU programming or performance engineering through Git Hub, blogs, benchmarks, conference talks or similar.
• Interest in advanced optimisation techniques, reinforcement learning, neuroevolution or related research areas.
• Join a well-funded early-stage company with significant technical ambition and growing enterprise adoption.
• Take ownership of core technology and play a significant role in shaping the company's technical direction.
• For experienced candidates, the opportunity to build and mentor a high-performing engineering team.
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