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 looking for someone to join Data Operations and help researchers get the data they need to train and evaluate our models.
You'll work directly with researchers to understand what they need, figure out how to get it, and own the work through delivery. Sometimes that means finding the right vendor. Sometimes it means digging into a new domain, finding unusual sources of data, or working through a request we haven't seen before.
This is a hands-on role. You'll spend time talking directly with vendors, reviewing data quality, and building processes and systems that make the work easier to repeat and scale.
Our research priorities change quickly, so the work will too. We're looking for someone who is resourceful, flexible, and comfortable figuring things out as they go.
What You'll DoWork with researchers to understand what data they need and turn open-ended requests into a concrete plan.
Own data projects from sourcing and vendor selection through quality review and delivery.
Find the best vendors, experts, and data sources for different types of work.
Build and maintain strong partnerships with data vendors, including international partners.
Review data with researchers and vendors, identify quality issues, and improve the output.
Go deep on new data domains when needed and quickly learn what good looks like.
Build guidelines, processes, and tools that make recurring work easier and more scalable.
Automate repetitive work where it makes sense while staying close to the work that requires judgment.
Work with legal and other teams on practical ways to manage vendor relationships, and adhere to communication guidelines.
Manage multiple projects against research and model timelines and adjust quickly as priorities change.
Minimum Qualifications:
Experience owning operational, data, product, program, or vendor work from start to finish.
Experience working with data vendors ideally in AI or technology.
Strong problem-solving instincts and a willingness to dig in when there isn't an obvious answer.
Comfortable working directly with researchers or technical teams and turning loosely defined needs into action.
Good judgment about quality and a willingness to inspect the work closely rather than rely only on process.
Strong communication skills across different teams, vendors, and cultures.
Comfortable with ambiguity, changing priorities, and tight timelines.
High initiative and willingness to do whatever part of the job is needed to move the work forward.
Able to add useful structure without overcomplicating things.
Preferred qualifications — we encourage you to apply if you meet some but not all of these
Experience with pre-training, post-training, evaluation, annotation, or other AI data types.
Existing relationships with a diverse set of AI or related data vendors.
Experience at an AI company, data company, marketplace, or other fast-moving technology company.
Experience working with international vendors and cross-border operational or compliance issues.
Product, program, operations, or generalist experience in an environment where you had to build the process as you went.
Experience building simple tooling or automation to reduce manual operational work.
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 $300,000 - $350,000.
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
- Experience owning operational, data, product, program, or vendor work from start to finish
- Experience working with data vendors, ideally in AI or technology
- Strong problem-solving skills and willingness to investigate ambiguous problems
- Ability to work with researchers or technical teams and turn loosely defined needs into action
- Strong judgment about data quality and willingness to inspect work closely
- Strong communication skills across teams, vendors, and cultures
- Comfort with ambiguity, changing priorities, and tight timelines
- High initiative and willingness to perform varied work to move projects forward
- Ability to add useful structure without overcomplicating processes
- Experience with pre-training, post-training, evaluation, annotation, or other AI data types
- Existing relationships with diverse AI or related data vendors
- Experience at an AI company, data company, marketplace, or fast-moving technology company
- Experience with international vendors and cross-border operational or compliance issues
- Product, program, operations, or generalist experience building processes in ambiguous environments
- Experience building simple tooling or automation to reduce manual operational work
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.









