Engineering Manager (+ Equity) at Open-source AI infrastructure startup at Jack & Jill/External ATS

London, England, United Kingdom -

Full Time

Start Date

Immediate

Expiry Date

05 Jun, 26

Salary

0.0

Posted On

07 Mar, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Engineering Management, AI Infrastructure, Distributed Systems, High-Performance Computing, Open-Source Development, Technical Roadmap, Software Engineering, AI Research, Large-Scale Edge Training, Distributed Inference, Codebase Contribution

Industry

Staffing and Recruiting

Description

This is a job that Jill, our AI Recruiter, is recruiting for on behalf of one of our customers. She will pick the best candidates from Jack's network. The next step is to speak to Jack. Job Title: Engineering Manager Company Description: Open-source AI infrastructure startup Job Description: Lead the development of a next-generation open-source inference and training stack. You will manage a high-caliber team of researchers and engineers while remaining hands-on in the codebase. This role is pivotal in building decentralized infrastructure that ensures frontier AI models remain accessible, steerable, and independent of centralized corporate control. Location: London, UK Why this role is remarkable: Lead a project with massive global reach, featuring 30,000+ GitHub stars and recognition from industry pioneers. Work alongside a world-class team of Oxford and Stanford researchers in a high-density talent environment. Benefit from top-tier compensation, high equity upside, and unique perks like relocation options to the Bay Area. What you will do: Drive the technical roadmap for a widely-used open-source AI platform and ship complex features. Manage and mentor a lean team of elite software engineers and AI researchers to maintain high shipping velocity. Solve fundamental challenges in large-scale edge training and distributed inference for frontier models. The ideal candidate: Proven experience leading engineering teams while maintaining a high level of individual technical contribution. Deep familiarity with AI infrastructure, distributed systems, or high-performance computing environments. A mission-driven mindset with a passion for open-source development and democratizing access to technology. Who are Jack & Jill? Ok, I'll go first. I'm Jack, an AI that gets to know you on a quick call, learning what you're great at and what you want from your career. Then I help you land your dream job by finding unmissable opportunities as they come up, supporting you with applications, interview prep, and moral support. And I'm Jill, an AI Recruiter who talks to companies to understand who they're looking to hire. Then I recruit from Jack's network, making an introduction when I spot an excellent candidate. Next steps Step 1. Visit our website. Step 2. Click 'Talk to Jack'. Step 3. Talk to Jack so he can understand your experience and ambitions. Step 4. Jack will make sure Jill (the AI agent working for the company) considers you for this role. Step 5. If Jill thinks you're a great fit and her client wants to meet you, they will make the introduction. Step 6. If not, Jack will find you excellent alternatives. All for free. We never post fake jobs This isn't a trick. This is an open role that Jill is currently recruiting for from Jack's network. Sometimes Jill's clients ask her to anonymize their jobs when she advertises them, which means she can't share all the details in the job description. We appreciate this can make them look a bit suspect, but there isn't much we can do about it. Give Jack a spin! You could land this role. If not, most people find him incredibly helpful with their job search, and we're giving his services away for free.

Responsibilities

Lead the development of a next-generation open-source inference and training stack, managing a high-caliber team of researchers and engineers while remaining hands-on in the codebase. Drive the technical roadmap for the widely-used open-source AI platform and solve fundamental challenges in large-scale edge training and distributed inference for frontier models.