Staff / Senior Engineer - Top AI lab or founding eng at AI startup pedigree
Talent Search PRO · San Francisco, CA
$225,000–$300,000from the description
Jul 6, 2026
San Francisco, CA
Jul 21, 2026
What this job asks for AI summary
A senior technical leadership role focused on designing and building compound AI systems — including agent orchestration, retrieval pipelines, process mining infrastructure, and evaluation frameworks — deployed inside large enterprise environments. The position carries responsibility for both hands-on architecture and growing an engineering team. It suits someone with a strong production AI/ML background, ideally from a top AI lab or a high-signal AI-native startup.
Staff level · San Francisco-Oakland-Berkeley, CA · 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
What the occupation pays Median $190,744 (middle half $167,095–$224,501). This posting is about at that midpoint.
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 (7)
The role is explicitly titled without a seniority level, but the scope — owning technical architecture, setting engineering pace, recruiting and scaling a team from 5 to 50, and bridging research and production — clearly places this at the Staff level (broad cross-team technical authority) rather than Senior. It stops short of Principal because the framing is more execution-and-team-building than org-wide research direction.
This role sits between Staff IC and an Engineering Manager/CTO archetype (11-3021): it has explicit people-management duties (recruit, mentor, lead, set code review standards) alongside deep hands-on technical ownership. It is classified as Software Developers (15-1252) because the primary day-to-day work described is building and shipping production AI systems, with management as a secondary responsibility.
No specific total years of experience is required; the JD explicitly states 'years of experience is not a hard filter' and gates instead on pedigree (top AI lab or high-signal AI-native startup) and demonstrated output.
The role is 5 days a week in-person in San Francisco; fully remote is not an option.
Kubernetes, distributed systems, process mining, and workflow automation all appear under the 'Nice-to-Have' section and are marked as preferred accordingly.
Skills such as 'agent systems / compound AI workflows / LLM-powered applications in production' and 'published research or significant open-source contributions in AI/ML' are listed under Nice-to-Have but are too generic or non-tool-specific to emit as discrete named skills; the relevant concrete technologies (agent orchestration, LLMs, retrieval pipelines) are already captured under the required skills.
Ignored 3 non-technology phrase(s) as skills (responsibilities/concepts, not named tools): inference optimization, agent orchestration, workflow automation.
Read the full posting
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