Senior Applied Research Engineer

📋 Description

• Profile distributed training to identify bottlenecks in compute, memory, and inter-GPU comm.
• Contribute to architecture decisions to improve training; implement Triton/CUDA kernels.
• Design model scaling, parallelization, and memory optimizations for large-context training.
• Collaborate with ML researchers to diagnose inefficiencies and scale ideas.
• Drive productionization and serving of models; improve inference with quantization.

🎯 Requirements

• Strong understanding of modern ML architectures and large-scale training.
• Experience running distributed training on multi-GPU systems.
• Advanced profiling/debugging across CPU, GPU, memory, latency, and inter-GPU communication.
• Strong Python programming skills.
• Experience with model scaling and parallelization, including tensor and pipeline parallelism.

🎁 Benefits

• Competitive compensation with salary and equity
• Comprehensive health coverage for you and your dependents
• Paid parental leave for all new parents, inclusive of adoptive and surrogate journeys
• Relocation support for employees moving to join the team in one of our office locations
• A mission-driven, low-ego culture that values diversity of thought, ownership, and bias toward action