Systems ML Engineer — High-Performance AI (Cambridge)

Transfyr Bio; Cambridge (MA); On-site; USD 150,000 - 230,000; Full time

Benefits offered by this job

Low-cost health insurance

HSA

401K with matching

Lunch subsidy

Job summary

Transfyr Bio is building physical AI for science in Cambridge, MA. The Systems ML Engineer will own performance optimization across the ML stack, shaping training efficiency and production inference across cloud and edge infrastructure.

You will profile workloads, write custom GPU kernels when needed, and collaborate with research and perception teams to push model performance. Ideal candidates bring deep ML systems expertise, experience with large-scale training, and a hands-on approach to

Qualifications

• Expertise in ML systems engineering for training and deployment.
• Experience profiling and optimizing large-scale ML models.
• Experience with cloud and edge deployments; familiarity with security/compliance.

Responsibilities

• Profile and optimize training and inference workloads across ML stack.
• Manage cloud and edge infrastructure and optimize cloud spend.
• Build and maintain high-performance GPU kernels (CUDA/Triton) for critical workloads.
• Develop data pipelines for training and real-time inference on multimodal lab data.
• Ensure production resilience with monitoring, versioning, and rollback mechanisms.
• Collaborate with research and perception teams to improve training efficiency.

Skills

Systems ML

Distributed Training

GPU Kernel

Cloud & Edge

Profiling & Debugging

Tools

PyTorch

JAX

CUDA

Triton

Kubernetes

Nsight

Job description

Transfyr Bio is building physical AI for science in Cambridge, MA. The Systems ML Engineer will own performance optimization across the ML stack, shaping training efficiency and production inference across cloud and edge infrastructure.

You will profile workloads, write custom GPU kernels when needed, and collaborate with research and perception teams to push model performance. Ideal candidates bring deep ML systems expertise, experience with large-scale training, and a hands-on approach to