Member of Technical Staff, ML Engineer at Physical Superintelligence
Boston, MA
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Jul 31, 2026
Boston, MA
Sep 24, 2026
What this job asks for AI summary
An ML infrastructure engineer role at an early-stage AI/physics research startup, responsible for building and owning the distributed training and model-serving systems that research teams depend on. The work spans GPU scheduling, inference serving (vLLM, SGLang), developer tooling for researchers, and production reliability — with a strong expectation of hands-on coding and on-call ownership. Suited to engineers with production ML infrastructure experience at meaningful scale.
Senior level · 3+ years · Boston-Cambridge-Newton, MA-NH · Full-time
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How this req sits in the market our data
Roughly 70 people in the Boston-Cambridge-Newton, MA-NH area plausibly meet what this posting asks for (software developers). range 20–120
Applicant volume Moderate — A normal amount of company. The rare requirements below are what will separate a shortlisted application from the rest.
Rare in this occupation — lead with these, and say what you built with them.
What the occupation pays Median $169,742 (middle half $134,780–$187,781).
Estimated from BLS employment for this occupation and area, per-skill prevalence across our listing corpus, and published wage benchmarks — as of Aug 2, 2026. It is a model, not a headcount.
Why we read it this way (7)
The role title 'Member of Technical Staff, ML Engineer' carries no explicit seniority level, so advertised seniority is Unspecified; however, the 3+ years of production ML infrastructure experience and full end-to-end ownership expectations support a Senior classification.
The alt SOC (15-1244) was considered because the role involves significant infrastructure operation, but the emphasis on writing production code, building services and abstractions, and owning the full stack from spec to ship points more clearly to Software Developers (15-1252).
Remote work is listed as considered 'on a case-by-case basis' — not a firm remote offering — so remote is set to false.
No compensation figures are provided beyond a general reference to competitive salary, benefits, and equity.
Distributed training is listed as a required capability; PyTorch and Ray are named as acceptable frameworks — PyTorch is emitted as the primary with Ray as an alternative. Similarly, vLLM is primary for model-serving with SGLang and Triton as alternatives.
CUDA, GCP/AWS, and Terraform appear only under the 'Nice to Have' section and are marked preferred accordingly.
'Training-as-a-service APIs', 'inference gateways', and 'job schedulers' are mentioned under Nice to Have but name no specific named technology, so they are omitted per the no-generic-concepts rule.
Read the full posting
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