Senior ML Engineer (Token Factory)

About This Opportunity

We are representing a confidential AI infrastructure organization seeking a Senior ML Engineer to join their high-performance inference and fine-tuning team in London. Our client operates one of the world's largest GPU clouds, and you'll be working to maximize throughput, minimize latency, and optimize cost-per-token across tens of thousands of GPUs.

Responsibilities

• Identify and resolve LLM inference bottlenecks to drive production performance improvements
• Optimize performance for a wide range of LLM architectures at scale, including GPT-based models, and various dense/MoE configurations
• Implement novel speculative decoding architectures and optimize components of various LLM designs (autoregressive/parallel)
• Contribute to open-source inference engines and implement performance enhancements
• Design and productionize low-precision training and inference pipelines (FP8, NVFP4/MXFP4) with measurable gains in throughput and cost-efficiency
• Profile GPU workloads and optimize compute/memory tradeoffs across the memory hierarchy

What We're Looking For

Required qualifications:

• Profound understanding of theoretical foundations of machine learning and transformer architecture
• Experience profiling GPU workloads using Nsight, PyTorch profiler, or similar tools
• Deep understanding of GPU memory hierarchy and compute/memory tradeoffs
• Familiarity with key LLM concepts: MHA, RoPE, KV-cache, Flash Attention, and quantization
• Understanding of performance aspects of large neural network training (sharding strategies, custom kernels, hardware features)
• Strong software engineering skills in Python
• Deep experience with modern deep learning frameworks
• Proficiency in CI/CD, version control, and unit testing
• Strong communication and leadership abilities

Nice to have:

• Experience with open-source inference engines (vLLM, SGLang, TensorRT-LLM), including contributions
• Experience with kernel languages or DSLs such as Triton, Cute, CUTLASS, or CUDA
• Track record of building and delivering products in dynamic, fast-paced environments
• Experience developing large distributed systems or high-load web services
• Open-source projects demonstrating engineering expertise
• Excellent written and verbal English communication skills

How We Work With You

When you apply through Get A Job.ai, our talent team will review your profile and conduct an initial screening. If there's a strong match, we'll submit your candidacy to our client for consideration. Please do not contact the client directly—all communication should flow through our recruitment team to ensure the best outcome for your application.

Pay

Compensation details will be discussed during the screening process based on experience and qualifications.

Equal Opportunity: Get A Job.ai is committed to inclusive hiring practices. We welcome applications from candidates of all backgrounds and work to ensure fair consideration throughout our recruitment process.