Senior Technical Recruiter

Posted 22 Days Ago
Be an Early Applicant
San Francisco, CA, USA
In-Office
180K-250K Annually
Senior level
Artificial Intelligence • Cloud • Software
The Role
Own end-to-end technical recruiting for highly specialized engineering roles across AI infrastructure, systems, networking, compilers, kernels, and distributed systems. Build talent maps, source passive candidates, partner with engineering leaders and founders, calibrate hiring standards, assess candidates, close hires, and improve recruiting processes using data and AI.
Summary Generated by Built In

About us

Gimlet is building the first multi-silicon neocloud designed for fast, efficient AI inference.

We combine large-scale compute infrastructure with an execution platform that partitions AI workloads and maps each stage to the hardware best suited to run it.

We work with foundation labs, hyperscalers, and AI-native companies, giving our team access to technical problems spanning frontier models, production infrastructure, and emerging hardware.

About the role

Gimlet Labs is hiring a Senior Technical Recruiter to help build one of the most technically ambitious engineering teams in AI infrastructure.

We are building systems that make heterogeneous compute work together at scale, from compiler and kernel optimization to distributed systems and production inference infrastructure. Recruiting for these teams means finding exceptional engineers who operate at the intersection of hardware, software, and performance.

You will partner directly with engineering leadership to understand complex technical challenges, develop creative sourcing strategies, and engage talent who are rarely actively looking for their next opportunity.

We are looking for someone who loves the craft of technical recruiting: understanding what makes an engineer exceptional, finding talent others overlook, building genuine relationships, and helping great people make consequential career decisions.

What success looks like

In your first 12–18 months, you will:

  • Build exceptional engineering teams. Identify, engage, and close high-caliber engineers across compiler, kernel, ML infrastructure, distributed systems, and other specialized technical disciplines.

  • Become a trusted partner to engineering leadership. Develop a deep understanding of the technical challenges we're solving and help hiring managers make informed decisions about talent, hiring priorities, and market opportunities.

  • Build differentiated talent pipelines. Develop creative sourcing strategies, map relevant companies and engineering teams, and build relationships with exceptional engineers who are difficult to reach through traditional recruiting channels.

  • Own searches from start to finish. Drive recruiting processes with urgency, strong candidate engagement, and clear accountability, from initial sourcing through offer acceptance.

  • Raise the quality of our hiring process. Partner with interviewers to improve evaluation consistency, candidate experience, and decision-making while maintaining a high technical bar.

  • Help scale the Talent Acquisition function. Identify opportunities to improve our tools, workflows, and sourcing practices, building processes that make the team more effective as Gimlet grows.

You may be a good fit if you have
  • 5+ years of technical recruiting experience, with a track record of hiring exceptional engineers in competitive talent markets.

  • Experience recruiting across specialized engineering disciplines such as compilers, kernels, ML infrastructure, distributed systems, networking, or HPC.

  • Strong technical curiosity and the ability to understand complex engineering domains beyond matching keywords.

  • Exceptional sourcing skills, with demonstrated success identifying, engaging, and closing passive talent.

  • Strong judgment in evaluating engineering talent and partnering with technical leaders on hiring decisions.

  • High ownership and the ability to independently drive searches from sourcing strategy through offer acceptance in a fast-moving environment.

Strong candidates may also have
  • Direct experience recruiting compiler, GPU kernel, or low-level performance engineers.

  • Experience hiring for AI infrastructure, high-performance computing, or distributed systems teams.

  • A strong network within specialized engineering communities and a track record of engaging hard-to-reach talent.

  • Experience building recruiting strategies and processes at early-stage or high-growth technology companies

Why join now?

Gimlet is expanding from its core technology into a production neocloud spanning new hardware, customers, and data centers.

  • Solve hard problems.

  • Own meaningful work.

  • Build for production.

  • Help define what’s next.

Agency Policy: Gimlet Labs does not accept unsolicited resumes from recruitment agencies or search firms. Any unsolicited resumes submitted without a signed agreement will be considered the property of Gimlet Labs, and no fees will be paid.

Skills Required

  • 5+ years of technical recruiting experience
  • Experience recruiting difficult-to-find engineering talent
  • Experience hiring in systems, infrastructure, ML/AI, distributed systems, compilers, kernels, networking, databases, or similarly technical domains
  • Excellent sourcing ability, including finding passive candidates others do not identify
  • Ability to learn technical markets quickly
  • Ability to challenge and advise senior engineering leaders
  • Strong candidate experience and ability to close highly sought-after passive candidates
  • Urgency, independence, and strong judgment
  • Experience recruiting compiler, kernel, GPU, HPC, or AI infrastructure engineers
  • Experience at an early-stage or rapidly scaling technical company
  • Strong network across systems or AI infrastructure
  • Experience building recruiting programs, processes, or market intelligence from scratch
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The Company
34 Employees
Year Founded: 2023

What We Do

Gimlet Labs builds an AI inference cloud and software stack that routes and orchestrates agent workloads across heterogeneous processors, including GPUs, CPUs, and other architectures. Its technology can divide a model's execution among different chips, improving inference speed, throughput, and efficiency. The company combines applied AI research with managed multi-silicon data centers and software customers can deploy in their own facilities, aiming to expand compute available for AI.

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