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 RoleWe're hiring a Recruiting Sourcer to build the pipeline of exceptional research and engineering talent that will define the next generation of AI. You'll partner closely with recruiters, hiring managers, and our technical leaders to identify, engage, and build relationships with candidates across research, engineering, and infrastructure.
This role is foundational to our growth. You'll need to think creatively about where to find rare technical talent, craft outreach that resonates with researchers and engineers who aren't actively looking, and help us build a sourcing function from the ground up at a fast-moving startup.
What You’ll DoBuild and manage a pipeline of candidates across research, engineering, and infrastructure roles, using creative and diverse sourcing channels
Partner with recruiters and hiring managers to deeply understand role requirements and translate them into effective sourcing strategies
Write and send personalized outreach that engages passive candidates, including senior researchers and engineers
Track pipeline metrics and sourcing effectiveness, and iterate on strategy based on what's working
Represent Thinking Machines' mission and culture authentically to prospective candidates throughout the sourcing process
Help build and refine our sourcing tools, processes, and infrastructure as the team scales
Minimum qualifications:
3+ years of sourcing or recruiting experience, ideally supporting technical roles in AI labs or tech startups
Track record of successfully identifying and engaging passive technical candidates, including researchers and engineers
Excellent written communication skills, with the ability to craft compelling, personalized outreach
Preferred qualifications:
Familiarity with the AI/ML research landscape, including key labs, conferences, and communities
Experience using sourcing tools and platforms (e.g., LinkedIn Recruiter, GitHub, academic search tools)
Experience building sourcing processes and infrastructure at an early-stage or fast-scaling company
Comfort operating with significant autonomy and adapting quickly as priorities shift
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 $250,000 - $300,000 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.
Skills Required
- 3+ years of sourcing or recruiting experience, ideally supporting technical roles in AI labs or technology startups
- Proven success identifying and engaging passive technical candidates, including researchers and engineers
- Excellent written communication skills and ability to craft compelling, personalized outreach
- Familiarity with the AI/ML research landscape, including key labs, conferences, and communities
- Experience using sourcing tools and platforms such as LinkedIn Recruiter, GitHub, and academic search tools
- Experience building sourcing processes and infrastructure at an early-stage or fast-scaling company
- Comfort operating autonomously and adapting quickly as priorities shift
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.


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