Senior Systems ML Engineer (Edge/Cloud) — Physics AI

S27a; Cambridge (MA); On-site; USD 180,000 - 240,000; Full time

Job summary

Transfyr seeks a Systems ML Engineer to ensure ML models train and run efficiently from large-scale training to production inference. You will own performance optimization across the ML stack, working with research and production teams to accelerate training and inference across cloud and edge environments.

The role spans ML systems engineering, cloud/edge deployments, and DevOps, with opportunities across levels from hands-on builders to senior engineers who shape infrastructure and technical

Qualifications

• Experience in ML systems engineering including production deployment and debugging.
• Ability to optimize inference throughput and resource utilization across cloud and edge.
• Experience with profiling tools and kernel optimization for performance.

Responsibilities

• Profile and optimize performance across ML stack to speed training and inference.
• Improve distributed training efficiency and pipelines with PyTorch Distributed.
• Develop and maintain high-performance GPU kernels in Triton or CUDA.
• Build and optimize data loading and inference pipelines for multimodal lab data.
• Manage deployment across cloud and edge environments with security considerations.
• Debug and resolve production bottlenecks and failures across training and deployment stack.
• Collaborate with research and perception teams to improve end-to-end ML pipelines.
• Implement monitoring, versioning, and rollback for safe model updates.

Skills

ML systems

Distributed training

Cloud & edge infra

ML stack

Cross-stack debugging

Algorithm optimization

Tools

Nsight

PyTorch Profiler

PyTorch Dist

CUDA

Triton

Kubernetes

Terraform

AWS

Job description

Transfyr seeks a Systems ML Engineer to ensure ML models train and run efficiently from large-scale training to production inference. You will own performance optimization across the ML stack, working with research and production teams to accelerate training and inference across cloud and edge environments.

The role spans ML systems engineering, cloud/edge deployments, and DevOps, with opportunities across levels from hands-on builders to senior engineers who shape infrastructure and technical