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.