Platform Engineer
Axiom · San Francisco, CA
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Jul 11, 2026
San Francisco, CA
Jul 21, 2026
What this job asks for AI summary
A senior engineering role focused on designing and building the core infrastructure behind an enterprise ML platform — covering model evaluation, deployment, inference, serving, and customer data management. The work involves taking large-scale reasoning agents from research into production, integrating them into on-premises customer-facing systems, and architecting storage and retrieval pipelines for chemical, biological, and clinical data. The role also carries a strong mentorship dimension, helping scientists across ML, chemistry, and biology develop strong engineering practices.
Senior level · National · Full-time
We read this from the posting text with AI. Skim the description below before ruling yourself out.
How this req sits in the market our data
Most people in this occupation already list these. Still required — just not what gets you shortlisted.
What the occupation pays Median $138,970 (middle half $107,524–$175,762).
Estimated from BLS employment for this occupation and area, per-skill prevalence across our listing corpus, and published wage benchmarks — as of Jul 28, 2026. It is a model, not a headcount.
Why we read it this way (6)
No work location is specified in the posting. Given Axiom's early-stage profile and enterprise customer focus, the role is likely on-site or hybrid at a US office, but this cannot be confirmed from the text — remote=false is a conservative default.
The posting blends IC engineering scope (designing and building core infrastructure) with culture-setting and team-growth responsibilities ('instilling a great engineering culture', 'empower scientists to become great engineers'). It stops short of describing direct reports or people-management authority, so Software Developers (15-1252) is the primary classification, with Computer and Information Systems Managers (11-3021) as the runner-up.
Skills are drawn from two sections: 'Various expertise which gets us interested' (treated as preferred/nice-to-have) and 'Key criteria' (treated as hard gates). The 'Key criteria' section names cloud infrastructure, ML systems, backend systems, and distributed systems as required generalist competencies; SaaS product experience and ML deployment/evaluation/serving are also called out there.
LLM-powered data systems, evaluation pipelines, versioning, and monitoring appear only under the 'Various expertise which gets us interested' heading and are therefore preferred.
No compensation, degree requirement, years of experience, or security clearance is stated in the posting.
The posting appears to be cut off mid-sentence ('Demonstrates relentless…'), so some requirements may be missing.
Read the full posting
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