Job Description

Senior GPU Systems / AI Infrastructure Engineer (NYC)

Location: New York City (Hybrid / On-site preferred)

Comp: Competitive + equity (Series A-C / high-growth AI infra)

About the Role

We’re hiring a senior-level engineer to build and optimise next-generation AI infrastructure powering large-scale model training and inference. This role sits at the intersection of GPU systems, kernel optimisation, distributed compute, and high-performance AI workloads .

You’ll work directly on the performance layer of modern AI stacks-where milliseconds matter, GPUs are saturated, and inefficiencies translate directly into cost and latency at scale.

This is a deeply technical role for engineers who are comfortable working close to the metal and care about squeezing every ounce of performance out of modern accelerators (NVIDIA, AMD, and emerging architectures).

What You’ll Work On

• Design and optimise GPU kernels (CUDA / Triton / HIP) for large-scale AI workloads
• Build and tune high-performance inference and training pipelines for LLMs and multimodal models
• Work on distributed systems for AI training (multi-node, multi-GPU clusters)
• Improve memory bandwidth utilisation, kernel fusion, and compute efficiency
• Contribute to or extend frameworks like PyTorch, JAX, or custom runtimes
• Build tooling for profiling, benchmarking, and performance regression detection
• Collaborate closely with ML researchers and infra engineers to remove system bottlenecks

What We’re Looking For (Core Profile / MPC Fit)

You’re likely a strong match if you have:

• 5-10+ years in systems engineering, HPC, GPU computing, or AI infrastructure
• Deep experience with CUDA programming and GPU kernel optimisation
• Strong understanding of parallel computing, memory hierarchies, and compute bottlenecks
• Experience with distributed systems (Ray, MPI, NCCL, custom cluster orchestration, etc.)
• Background in high-performance C++ / Rust / Python systems
• Experience working on training or inference stacks for large-scale ML models
• Strong intuition for performance profiling (Nsight, perf, flamegraphs, etc.)

Nice to Have

• Experience with Triton, TVM, or MLIR-based compiler stacks
• Exposure to kernel fusion, graph compilation, or runtime optimisation
• Experience at AI infra startups, hyperscalers, or HPC environments
• Familiarity with quantisation, KV caching, or inference acceleration techniques
• Contributions to open-source ML systems or GPU libraries
• Background in CUDA graph execution, stream scheduling, or warp-level optimisation

Why This Role

• Work on the critical performance layer of AI systems (not application-level ML)
• Direct impact on cost, latency, and scalability of frontier AI models
• High autonomy-own entire subsystems (kernel → runtime → distributed execution)
• NYC-based team building at the forefront of AI infrastructure and compute optimisation
• Opportunity to shape systems used at massive scale in production ML workloads

Darwin Recruitment is acting as an Employment Agency in relation to this vacancy.

Reece Waldon

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