Engineering Manager

Inferact•San Francisco, CA

11d•$200,000 - $400,000•Onsite

About The Position

Inferact is seeking an Engineering Manager to scale a senior-heavy engineering organization working on AI inference. The manager will lead engineers across inference systems, performance, kernels, accelerators, and cloud infrastructure, building management capacity while preserving technical quality and execution speed. This is a deeply technical management role responsible for team execution, hiring, coaching, prioritization, and operating rhythm. The manager will remain technically engaged enough to earn engineers’ trust, partner with engineering and company leadership to translate priorities into technical direction, remove blockers, improve team practices, and help engineers focus on high-leverage work.

Requirements

Bachelor's degree or equivalent experience in computer science, engineering, or a similar field.
Demonstrated experience managing strong software engineers in technically demanding systems, infrastructure, ML systems, distributed systems, or performance-oriented environments.
Strong technical credibility and ability to understand architecture, ask useful questions, challenge assumptions, and unblock senior engineers without becoming a bottleneck or overriding domain experts.
Experience owning the full people-management lifecycle, including hiring, onboarding, 1:1s, feedback, performance management, career development, team health, and retention.
Ability to translate ambiguous company priorities into team goals, sequencing, ownership, and execution plans in a fast-changing startup environment.
Track record of building effective engineering operating rhythms around planning, decision-making, technical reviews, cross-team coordination, and accountability without unnecessary process.
Strong judgment about when to dive into a technical problem, delegate, and create leverage for senior engineers.
At least 2 years of management experience with 10 or more reports.

Nice To Haves

Experience leading teams in AI/ML infrastructure, model serving, distributed systems, GPU infrastructure, compilers, performance engineering, cloud infrastructure, or developer platforms.
Experience managing senior, staff, principal, research-adjacent, or PhD engineers where technical credibility and autonomy are especially important.
Experience scaling an engineering team through rapid hiring, including interviewer calibration, hiring decisions, onboarding systems, and thoughtful team design.
Familiarity with inference systems and related technologies such as vLLM, SGLang, PyTorch, CUDA/Triton, Kubernetes, distributed serving, or accelerator infrastructure.
Experience partnering closely with founders or engineering executives at an early-stage company with evolving priorities, team boundaries, and organizational needs.
Experience building or scaling a small, unusually high-talent engineering team with specialized technical scope and a very high quality bar.
Previous hands-on engineering or technical lead experience, with the ability to reason credibly about complex production systems.
Experience managing teams across technical specialties, geographies, or time zones while maintaining ownership and communication.
Experience establishing engineering management practices at an early stage.
Contributions to open-source infrastructure, AI systems, developer tools, or technically ambitious products used by external engineers.

Responsibilities

Lead a team of engineers working across areas such as inference systems, performance, kernels, accelerators, and cloud infrastructure.
Own team execution, hiring, coaching, prioritization, and operating rhythm.
Manage the full people-management lifecycle, including hiring, onboarding, 1:1s, feedback, performance management, career development, team health, and retention.
Translate company priorities into clear technical direction, team goals, sequencing, ownership, and execution plans.
Partner with engineering and company leadership.
Remove blockers and improve how the team works.
Establish effective operating rhythms for planning, decision-making, technical reviews, cross-team coordination, and accountability.
Use sound judgment about when to engage directly in technical problems and when to delegate, creating leverage for senior engineers.