Job Application for Research, Post-Training at Thinking Machines Lab Back to jobs Research, Post-Training San Francisco Apply 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.
About
the Role
The role of post-training researchers sits at the core of our roadmap. This is the critical bridge between raw model intelligence and a system that is actually useful, safe, and collaborative for humans. This role blends fundamental research and practical engineering, as we do not distinguish between the two roles internally. You will be expected to write high-performance code and read technical reports. It’s an excellent fit for someone who enjoys both deep theoretical exploration and hands-on experimentation, and who wants to shape the foundations of how AI learns. Note: This is an "evergreen role" that we keep open on an on-going basis to express interest in this research area. We receive many applications, and there may not always be an immediate role that aligns perfectly with your experience and skills. Still, we encourage you to apply. We continuously review applications and reach out to applicants as new opportunities open. You are welcome to reapply if you get more experience, but please avoid applying more than once every 6 months. You may also find that we put up postings for singular roles for separate, project or team specific needs. In those cases, you're welcome to apply directly in addition to an evergreen role. What You’ll Do Develop and tune the recipe: iterate on post-training recipes, consisting of a collection of datasets, training stages, and hyperparameters. Measure how recipe choices affect various metrics. Iterate on evals: post-training involves a never-ending loop of defining a set of evaluations, optimizing them, and then realizing your existing evals don’t capture what matters. You’ll be responsible for both making numbers go up, and making sure the numbers are meaningful. Debug and understand: while tuning the details of a training configuration, we often observe results that don’t quite make sense. You’ll be responsible for both getting things to work, and developing a deeper understanding, which we can bring to the next problem. Scale and explore: post-training will involve a combination of scaling the existing methodologies and developing new ones. We’ll want to both measure how performance metrics scale with dataset size, and explore using a completely different kind of training dataset. Publish and present research that moves the entire community forward. Share code, datasets, and insights that accelerate progress across industry and academia. Skills and
Qualifications Minimum
qualifications: Proficiency in Python and familiarity with at least one deep learning framework (e.g., PyTorch, TensorFlow, or JAX). Comfortable with debugging distributed training and writing code that scales. Bachelor’s degree or equivalent experience in Computer Science, Machine Learning, Physics, Mathematics, or a related discipline with strong theoretical and empirical grounding. Clarity in communication, an ability to explain complex technical concepts in writing. Preferred
qualifications — we encourage you to apply even if you don’t meet all preferred
qualifications, but at least some: A strong grasp of probability, statistics, and ML fundamentals. You can look at experimental data and distinguish between real effects, noise, and bugs. Prior experience with RLHF, RLAIF, preference modeling, or reward learning for large models. Experience managing or analyzing human data collection campaigns or large-scale annotation workflows. Research or engineering contributions in alignment, data-centric AI, or human-AI collaboration. PhD in Computer Science, Machine Learning, Physics, Mathematics, or a related discipline with strong theoretical and empirical grounding; or, equivalent industry research experience. Logistics 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 $350,000 - $475,000 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. Create a Job Alert Interested in building your career at Thinking Machines Lab? Get future opportunities sent straight to your email. Create alert Apply for this job * indicates a required field Autofill with MyGreenhouse First Name * Last Name * Preferred First Name Email * Phone Country * Phone * Location (City) * Locate me Resume/CV * Attach Attach Dropbox Google Drive Enter manually Enter manually Accepted file types: pdf, doc, docx, txt, rtf Education School * Select... Degree * Select... Discipline * Select... Start date year End date year Add another LinkedIn Profile Link * Please provide the URL to your LinkedIn; if you don't have one, please write "none". Github Link * Please provide the URL to your Github; if you don't have one, please write "none". Personal Website About You * Please provide the URL to your personal website, Google Scholar, etc if you have one. Put "none" if you do not. Current Company * Please tell us the name of your current employer (today if you are employed). Put "none" if this does not apply to you; for example, if you are in school or not currently employed -- this does not disqualify you. Feel free to enter previous roles in the field below in "Past Company 1". Current Title or Role * Please enter your current title at your current employer. If you are not currently employed (or in school etc) please enter "none" and feel free to enter previous roles in the field below in "Past Company". Past Company 1 * Please enter the Company name of your most recent previous employer. If you have not worked at another company before your current one, please enter “none”. Past Company Title or Role * Please enter your title at your most recent previous employer. If you have not worked at another company before your current one, please enter “none”. Past Company 2 If you would like, please enter the Company name of your second previous employer. If you have not worked at another company before your current or previous one, please enter “none” or skip this question. Past Company Title or Role 2 If you would like, please enter the Title or Job of your second previous employer. If you have not worked at another company before your current or previous one, please enter “none” or skip this question. What domains of research do you have expertise in? * Pre-training Data Pre-training Science Pre-training Scaling Laws Architecture Optimization Multimodal Audio Perception Vision Robotics Post-Training Post-Training Data Human Data Synthetic Data Reinforcement Learning Reasoning Safety Alignment Other Select all that apply where you have actively completed work in and would be able to interview for in a technical interview. This will help us when picking between projects If you selected other, what areas did we not include that you have expertise in? * If you completed research under an advisor, such as through a PhD program or masters program where you published, who was your advisor? First name and last name of your Advisor / what program this was Do you have any links to publications we should read? Links to any publications, please list here (Optional) List 3 projects you're proud of. Please list 3 projects you're proud of, using 1 sentence each. Feel free to add a link if helpful. We will send this to the hiring team when reviewing your application. Will you now or in the future require sponsorship for employment visa status in the United States? * Select... (Optional) Other notes Voluntary Self-Identification For government reporting purposes, we ask candidates to respond to the below self-identification survey. Completion of the form is entirely voluntary. Whatever your decision, it will not be considered in the hiring process or thereafter. Any information that you do provide will be recorded and maintained in a confidential file. As set forth in Thinking Machines Lab’s Equal Employment Opportunity policy, we do not discriminate on the basis of any protected group status under any applicable law. Gender Select... Are you Hispanic/Latino? Select... Race & Ethnicity Definitions If you believe you belong to any of the categories of protected veterans listed below, please indicate by making the appropriate selection. As a government contractor subject to the Vietnam Era Veterans Readjustment Assistance Act (VEVRAA), we request this information in order to measure the effectiveness of the outreach and positive recruitment efforts we undertake pursuant to VEVRAA. Classification of protected categories is as follows: A "disabled veteran" is one of the following: a veteran of the U.S. military, ground, naval or air service who is entitled to
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