Senior ML Systems Engineer – GPU HPC & Distributed Training

Proxima; Boston (MA); On-site; USD 180,000 - 280,000; Full time

Job summary

Proxima is building frontier AI for proximity therapeutics, combining foundation-model ML with a scalable data generation engine. We are seeking a senior ML systems engineer to profile and optimize training and inference for large structural models, implement CUDA/Triton kernels, and scale multi-node distributed training across large GPU clusters.

Ideal candidates will have 6+ years in ML systems or HPC, deep PyTorch internals knowledge, and strong Python/C++ skills.

Qualifications

• BS/MS/PhD in CS, EE, or related field
• 6+ years in ML systems, HPC, or performance engineering
• Deep knowledge of PyTorch internals with profiling experience
• Experience with CUDA and Triton; reading Nsight output
• Experience with distributed multi-node training
• Strong Python and C++ proficiency
• Ability to name a model they made faster and quantify improvement

Responsibilities

• Profile and optimize training and inference for structural and generative models, including transformers, diffusion, and geometric deep learning
• Write and tune custom kernels (CUDA, Triton) and use compilers (torch.compile, TensorRT, XLA) when beneficial
• Scale distributed training across 32-64 nodes, employing FSDP, DeepSpeed, tensor and pipeline parallelism, and mixed precision
• Reduce inference cost by optimizing memory scaling for large complexes, improving diffusion sampling efficiency, batching ragged inputs, and maximizing throughput across up to 1000 GPUs
• Manage GPU cluster efficiency on GCP, focusing on scheduling, utilization, spot strategy, and cost reporting
• Develop benchmarks and profiling tools for the research team

Skills

Python

C++

PyTorch internals

CUDA

Triton

Distributed training

Benchmarking

Nsight profiling

Education

BS/MS/PhD in CS/EE or related field

Tools

Nsight

TensorRT

Torch.compile

Job description

Proxima is building frontier AI for proximity therapeutics, combining foundation-model ML with a scalable data generation engine. We are seeking a senior ML systems engineer to profile and optimize training and inference for large structural models, implement CUDA/Triton kernels, and scale multi-node distributed training across large GPU clusters.

Ideal candidates will have 6+ years in ML systems or HPC, deep PyTorch internals knowledge, and strong Python/C++ skills.