Principal ML Performance Engineer – GPU Optimization

Job not on LinkedIn

🔥 3 minutes ago

🗽 New York – Remote

🗽 New York – Remote

⏰ Full Time

🔴 Lead

👷🏻‍♀️ Engineer

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👻 Ghost score 12%

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Proxima

WebsiteLinkedInAll Job Openings

11 - 50 employees

Founded 2019

🧬 Biotechnology

🤖 Artificial Intelligence

💊 Pharmaceuticals

Biotechnology • Artificial Intelligence • Pharmaceuticals

Proxima is a biotechnology company that builds an integrated discovery platform to design and program protein interactions, enabling a new class of proximity-based medicines (inducers, modulators, and blockers). The company combines generative AI, advanced data-generation, and structural modeling technologies to accelerate discovery of therapeutics and collaborates with industry partners to translate induced-proximity modalities into drug candidates.

📋 Description

• Profile and optimize training and inference for structural and generative models, including transformers, diffusion, and geometric deep learning • Write and tune custom kernels using CUDA and Triton • Use compilers such as torch.compile, TensorRT, and XLA when beneficial • Scale distributed training across 32–64 nodes using FSDP, DeepSpeed, tensor parallelism, 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 • Help develop Proxima’s AI and data-generation platform for proximity therapeutics and protein-interaction discovery

🎯 Requirements

• Minimum of 6+ years experience in ML systems, HPC, or performance engineering • BS/MS/PhD in CS, EE, or related field • Ability to set technical direction beyond coding, select infrastructure, influence research teams, and mentor engineers • Deep knowledge of PyTorch internals with hands-on experience profiling and fixing real bottlenecks • Experience with CUDA and Triton • Skilled at reading Nsight output • Strong understanding of memory bandwidth and occupancy • Experience with distributed training at multi-node scale • Strong proficiency in Python and C++ • Able to name a model they made materially faster and quantify the improvement