Member of Technical Staff, ML Engineer

Member of Technical Staff, ML Engineer

📍 Cambridge, MA · Hybrid, 3 days in office · Full time

🔬 THE OPPORTUNITY

Join an early-stage team building AI systems to discover new physics at scale. Turn research code into reliable production systems and own the training and inference infrastructure behind AI-driven scientific discovery.

⚙️ WHAT YOU WILL OWN

• Translate research code into dependable services with clear interfaces, meaningful tests and operational ownership.
• Build services for launching experiments, routing across models and delivering results.
• Own distributed ML jobs, GPU scheduling and model serving on a shared engineering platform.
• Develop deep expertise in one area: distributed training, inference serving, ML platforms, or LLM gateway and agent infrastructure.

👤 WHAT YOU WILL BRING

• 3 years building and operating production ML training or inference infrastructure at meaningful scale, including ownership through failures.
• 3 years of software engineering on systems used by other engineers or researchers.

Depth in at least one of:

• Distributed training: PyTorch DDP/FSDP, Ray, multi-node jobs, checkpointing and performance.
• Inference serving: vLLM, SGLang, Triton, batching, KV cache and quantization.
• ML platforms: Kubernetes GPU scheduling, experiment tracking, registries and metering.
• Gateways and agents: model routing, agent runtimes, sandboxes and evaluation infrastructure.

🔎 USEFUL EXPERIENCE

• Debugging training loops, reward signals or inference behavior alongside researchers.
• GPU infrastructure, CUDA or ML performance engineering.
• GCP or AWS and infrastructure as code.
• Experience turning research prototypes into production systems.

🤝 HOW THE TEAM WORKS

High technical standards, simple designs and ownership from specification through deployment and on-call. Expect daily use of agentic coding tools and close collaboration with researchers.

💼 COMPENSATION

Competitive salary, benefits and meaningful early-stage equity.