Thinking Machines Lab's mission is to empower humanity through advancing collaborative general intelligence. We're building a future where everyone has access to the knowledge and tools to make AI work for their unique needs and goals.
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.
We are hiring our first technical sourcer to ensure that Thinking Machines has a pipeline of exceptional candidates for technical roles. You will develop a sourcing strategy that meets both challenges: identifying and tracking the best talent, and getting them interested in our company. This role requires nurturing relationships, understanding technical fields, and staying focused on the long game.
What You’ll Do- Map the talent landscape for researchers and engineers across companies, open-source communities, research labs, and technical ecosystems.
- Run high-volume outreach through social media and direct channels.
- Plan events that build our recruiting pipeline: coding competitions, happy hours, dinners, conference presence.
- Build community engagement via outreach, in-person networking, and referrals.
- Maintain a candidate database with important context for recruiters, track the performance of our candidate pipeline over the long term.
- Partner with hiring managers to translate staffing requirements into a sourcing strategy.
- Help ensure our reputation attracts the world’s best talent to Thinking Machines.
Minimum qualifications:
- 4+ years of technical sourcing experience, ideally at high-growth startups or competitive technical environments.
- Track record of sourcing for specialized roles where candidates need to be found, not filtered.
- Proficiency with sourcing tools (LinkedIn Recruiter, GEM, Juicebox, or similar).
- Strong research, mapping, and market intelligence skills.
- Ability to assess technical candidate quality from a GitHub profile or research paper
Preferred qualifications — we encourage you to apply if you meet some but not all of these:
- Experience sourcing for roles in ML research, infrastructure, and similar areas in AI.
- Existing network in AI/ML communities.
- Experience partnering with senior technical leadership to build out their organization
- A genuine interest in technology, artificial intelligence, and their potential for positive impact on humanity.
- Location: This role is based in San Francisco, California.
- Compensation: Depending on background, skills and experience, the expected annual salary range for this position is $150,000 - $250,000 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 technical sourcing experience
- Proficiency with sourcing tools
- Strong research, mapping, and market intelligence skills
- Ability to assess technical candidate quality from a GitHub profile or research paper
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.
