Engineering Manager Jobs in San Francisco, USA

Looking for an engineering manager job in San Francisco? Choose from 94 open roles at 39 employers. Explore real salary data and skills breakdown across remote, hybrid and on-site roles.

94 Open roles
39 Companies hiring
355K Average salary

What do Engineering managers in San Francisco work on?

The roles on this page sit firmly in AI and machine learning infrastructure. Most involve leading teams that build the systems underpinning model training, evaluation, and deployment rather than shipping consumer-facing features directly. A few examples illustrate the range: the Engineering Manager, Research Productivity at Anthropic focuses on large-scale training infrastructure and research tooling, while the Sr. Engineering Manager, AI Runtime at Databricks covers GPU training infrastructure, distributed orchestration, and fault tolerance. At OpenAI, the Engineering Manager, Model Flywheel leads teams working on model experimentation, rollout automation, and evaluation frameworks for ChatGPT. Cursor has two closely related postings: Engineering Manager, ML and Engineering Manager, Evals, both centred on model evaluation and training infrastructure. Director-level roles, such as the Director of Engineering, Physical AI at Scale AI, add organisation-building and cross-team technical strategy to those responsibilities. People management duties across all postings include hiring, coaching, and roadmap ownership.

Skills and experience employers ask for

Skills counts reflect mentions in posting descriptions across the full set of listings on this page, not stated requirements. With nearly all postings touching AI or ML infrastructure, agents and APIs appear most frequently, followed by evaluation, LLMs, and Kubernetes. Cloud platforms — AWS, Azure, and GCP — and pipeline tooling such as CI/CD and Spark appear in a meaningful minority. Python and Go feature in roughly one in ten postings. The sample is specific to this snapshot and these employers, so the counts should not be read as a general market signal.

Agents 38%
APIs 30%
Evaluation 28%
LLMs 24%
Kubernetes 19%
A/B testing 15%
AWS 14%
Azure 12%
GPUs 12%
CI/CD 12%
Python 11%
GCP 9%
Spark 9%
Go 9%

Engineering Manager roles that state a salary

The employers' own advertised ranges, for individual roles at different levels — not an average and not a market rate.

San Francisco office, hybrid or remote?

The clear majority of postings on this page specify onsite work in San Francisco, making in-person attendance the dominant expectation among these employers. A substantial portion list hybrid arrangements, where the balance between office and remote days is not standardised across postings and would need to be confirmed with each employer directly. Only a small number of postings explicitly offer fully remote work. No postings in this set left work mode unstated, so the picture is relatively transparent, though specific office day requirements for hybrid roles are not detailed in these listings.

Make your application specific to the work

  1. Read the technical scope carefully Many postings overlap in title but differ significantly in domain — GPU infrastructure, evaluation systems, identity platforms, and physical AI pipelines each require different backgrounds, so check the team focus before applying.
  2. Note management experience thresholds Several postings state minimum years of both engineering and people management experience explicitly; where those are listed, treat them as genuine filters rather than aspirational criteria.
  3. Check pay disclosure before applying Advertised ranges are available for only a minority of postings on this page; where a range is shown it belongs to the specific role listed, so do not apply it to other positions at the same employer without verifying.
  4. Confirm location arrangement directly For hybrid roles especially, the number of required onsite days is not stated in most postings, so clarify expectations with the recruiter early in the process.

Questions about Engineering Manager jobs in San Francisco

Pay is disclosed for only a minority of postings on this page. The ranges shown come from OpenAI and Lambda; OpenAI's posted ranges vary by specific team, with the Identity & Access Platform role showing a wider band than others. These figures are advertised ranges for those individual postings only and cannot be assumed to apply to other employers or unlisted roles.

Requirements vary by posting. The Anthropic Research Productivity role states at least two years of management experience, while Databricks and Scale AI postings specify three to four or more years managing technical teams alongside eight-plus years of engineering experience. Where not explicitly stated in a summary, the posting itself will carry the detail.

Only a small number of postings on this page offer fully remote work. The majority specify onsite in San Francisco, with a substantial portion offering hybrid. If remote eligibility is important to you, filter specifically for those two postings rather than assuming flexibility across the set.

Most postings on this page are concentrated in AI and machine learning infrastructure: model training, evaluation systems, inference infrastructure, and GPU platform engineering appear repeatedly across employers. Data platform and developer-tooling roles also feature, but AI-adjacent infrastructure is the dominant theme in this snapshot.

Visa sponsorship eligibility is not stated in the fact pack for these postings. You would need to check each individual job listing or ask the relevant recruiter directly, as sponsorship decisions are employer-specific and not inferable from company reputation or role seniority.

Jobs checked 5 hours ago. · Guide reviewed 9 September 2026.