Engineering Manager

Inferact•San Francisco, CA

9d•Onsite

About The Position

Inferact is seeking an Engineering Manager to help scale a senior-heavy engineering organization working at the frontier of AI inference. The manager will lead engineers across areas including inference systems, performance, kernels, accelerators, and cloud infrastructure, and build the management capacity needed to support rapid growth while maintaining technical quality and execution speed. This is a deeply technical management role. The manager will own team execution, hiring, coaching, prioritization, and operating rhythm while staying technically engaged enough to earn the trust of engineers working on complex systems problems. They will partner with engineering and company leadership to translate company 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, including the ability to understand architecture, ask useful questions, challenge assumptions, and unblock senior engineers.
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 clear goals, sequencing, ownership, and execution plans in a fast-changing startup environment.
Track record of building effective engineering operating rhythms without creating unnecessary process.
Strong judgment about when to engage directly in technical problems, 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 team design.
Familiarity with inference systems and technologies such as vLLM, SGLang, PyTorch, CUDA/Triton, Kubernetes, distributed serving, or accelerator infrastructure.
Experience partnering closely with founders or engineering executives in an early-stage company.
Experience building or scaling a small, unusually high-talent engineering team with specialized technical scope and a very high quality bar.
Previous hands-on experience as an engineer or technical lead, 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 from an early stage.
Contributions to open-source infrastructure, AI systems, developer tools, or technically ambitious products used by external engineers.

Responsibilities

Lead engineers working across 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 team goals, sequencing, ownership, and execution plans.
Establish effective practices for planning, decision-making, technical reviews, cross-team coordination, and accountability.
Partner with engineering and company leadership to set technical direction and address organizational needs.
Remove blockers and help senior engineers make progress without becoming a bottleneck or overriding domain experts.
Support rapid team growth while maintaining technical quality and execution speed.