Senior Software Engineer, Agent Oversight
Scale AI · San Francisco, CA
$216,000–$270,000
Jul 13, 2026
San Francisco, CA
Jul 21, 2026
What this job asks for AI summary
A backend-focused platform engineering role building the infrastructure that keeps production AI agents observable, measurable, and improvable. Day-to-day work centers on observability tooling, evaluation harnesses, APIs, and data pipelines that capture agent telemetry and performance signals at scale. The role suits engineers with solid distributed-systems experience who have shipped production software for ML or LLM-powered products and are comfortable collaborating closely with ML engineers.
Senior level · 4+ years · 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
Roughly 1,450 people in the San Francisco-Oakland-Berkeley, CA area plausibly meet what this posting asks for (software developers). range 300–2,200
Applicant volume Moderate — A normal amount of company. The rare requirements below are what will separate a shortlisted application from the rest.
What the occupation pays Median $190,744 (middle half $167,095–$224,501).
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 (5)
The job title is a plain 'Software Engineer' with no level modifier; advertised seniority is therefore Unspecified. However, the 4+ years requirement, end-to-end ownership expectations, cross-functional scope, and mentoring responsibilities support a Senior classification.
No location is explicitly stated in the posting; Scale AI is headquartered in San Francisco, CA, and the CBSA has been set accordingly. The posting does not advertise remote work.
The required skills are described in terms of capabilities and domains (backend/distributed systems, data pipelines, LLM/ML production, observability) rather than specific named technologies — no concrete stack (e.g. Python, Kafka, Kubernetes) is called out anywhere in the JD. Skills have been extracted at the highest level of specificity the posting supports.
The 'Nice to have' section explicitly covers: deep observability/monitoring experience for ML/LLM products, agent architectures (tool use, planning, multi-agent orchestration), MLOps/feature stores/model serving/experiment infrastructure, regulated/enterprise context experience, and senior/staff-level mentoring — all marked as preferred.
No compensation figures are provided in the posting.
Read the full posting
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