The mission of Thinking Machines is to build AI that extends human will and judgment. We are training frontier models with Inkling, developing Tinker to let people make models their own, and crafting interfaces that broaden human-AI communication. We believe the future worth building is human, and we're hiring people who want to build it.
About the Role
We're hiring a Technical Recruiter, to lead full-cycle recruiting for the infrastructure eams building and operating the systems behind our frontier model training and Tinker's platform - including cluster and network engineering, distributed systems, and site reliability. You'll partner closely with infrastructure leaders and engineers to understand niche hiring needs and translate them into sourcing strategies, calibrated interview loops, and craft a closing process that lands top candidates in a competitive market.
This is a high-ownership role at a company where infrastructure is the foundation of everything we ship. You'll need to go deep enough on distributed training, networking, and cluster orchestration to evaluate technical signal yourself, not just pattern-match keywords, and to speak credibly with the engineers you're recruiting.
What You’ll Do
Own full-cycle recruiting — sourcing, screening, interview coordination, and closing — for infrastructure, systems, and networking roles.
Partner directly with hiring managers and technical leaders on role scoping and interview loop design
Build and run proactive sourcing strategies and outbound campaigns to reach passive, highly specialized infrastructure talent
Serve as a subject matter expert across distributed training, cluster networking, Kubernetes/container orchestration, and systems programming languages, with the ability to speak to the team's work in depth while assessing candidates
Calibrate talent bar alongside engineering leaders and help interviewers give clear, well-supported feedback
Manage a high-touch candidate experience and act as a trusted advisor to candidates through offer and close
Track pipeline health and hiring metrics, and use that data to continuously improve the hiring process
Help build the recruiting playbooks, tools, and processes that will scale as the infrastructure org grows
Skills & Qualifications
Minimum Qualifications
4+ years of full-cycle technical recruiting experience, with a track record of hiring for infrastructure, platform, or systems engineering roles
Ability to hold a substantive technical conversation about infrastructure domains such as distributed systems, cluster or cloud networking, container orchestration, and systems languages
Experience partnering directly with hiring managers on intake, bar calibration, and interview loop design
Sound independent judgment on candidate quality, and the ability to manage multiple concurrent searches with minimal oversight
Preferred Qualifications
Experience recruiting at a high-growth AI, machine learning, or infrastructure-focused technology company
Proficiency with an applicant tracking system (e.g., Greenhouse or Ashby) and modern sourcing tools
Experience building sourcing strategy or recruiting processes from scratch at an early-stage or fast-scaling team
A genuine interest in Thinking Machines' mission and in the role a world-class infrastructure team plays in achieving it
Logistics
Location: This role is based in San Francisco, CA.
Compensation: Depending on background, skills and experience, the expected annual salary range for this position is $200,000 - $275,000 USD.
Visa sponsorship: We sponsor visas. While we can't guarantee success for every candidate or role, if you're the right fit, we're committed to working through the visa process together.
Benefits: Thinking Machines offers generous health, dental, and vision benefits, unlimited PTO, paid parental leave, and relocation support as needed.
As set forth in Thinking Machines' Equal Employment Opportunity policy, we do not discriminate on the basis of any protected group status under any applicable law.
Thinking Machines Lab will consider for employment qualified applicants with criminal histories in a manner consistent with the requirements of the California Fair Chance Act, the San Francisco Fair Chance Ordinance, and any other applicable state or local fair chance ordinance or law.
Skills Required
- 4+ years of full-cycle technical recruiting experience
- Track record hiring for infrastructure, platform, or systems engineering roles
- Ability to hold substantive technical conversations about distributed systems, cluster or cloud networking, container orchestration, and systems programming languages
- Experience partnering with hiring managers on intake, talent-bar calibration, and interview-loop design
- Independent judgment on candidate quality
- Ability to manage multiple concurrent searches with minimal oversight
- Experience recruiting at a high-growth AI, machine learning, or infrastructure-focused technology company
- Proficiency with an applicant tracking system such as Greenhouse or Ashby and modern sourcing tools
- Experience building sourcing strategy or recruiting processes from scratch at an early-stage or fast-scaling team
- Genuine interest in the company mission and the role of infrastructure teams
What We Do
Thinking Machines Lab is an artificial intelligence research and product company. We're building a future where everyone has access to the knowledge and tools to make AI work for their unique needs and goals. While AI capabilities have advanced dramatically, key gaps remain. The scientific community's understanding of frontier AI systems lags behind rapidly advancing capabilities. Knowledge of how these systems are trained is concentrated within the top research labs, limiting both the public discourse on AI and people's abilities to use AI effectively. And, despite their potential, these systems remain difficult for people to customize to their specific needs and values. To bridge the gaps, we're building Thinking Machines Lab to make AI systems more widely understood, customizable and generally capable. We are scientists, engineers, and builders who've created some of the most widely used AI products, including ChatGPT and Character.ai, open-weights models like Mistral, as well as popular open source projects like PyTorch, OpenAI Gym, Fairseq, and Segment Anything.
