Member of Technical Staff, ML Engineer

Location: Cambridge, MA, United States; hybrid with 3 days in office

Salary: Up to $230,000 base salary plus equity

Company

hirify.global supports an early-stage team building AI systems to discover new physics at scale.

What you will do

• Translate research code into dependable production 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 distributed training, inference serving, ML platforms, or LLM gateway and agent infrastructure.
• Collaborate closely with researchers and use agentic coding tools while owning work from specification through deployment and on-call.

Requirements

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

Nice to have

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

Culture & Benefits

• High technical standards, simple designs, and ownership from specification through deployment and on-call.
• Close collaboration with researchers and daily use of agentic coding tools.
• Competitive salary, benefits, and meaningful early-stage equity.