Thinking Machines Lab
Research, Mid Training
San Francisco · Mid
H1B sponsorship available$350k-$475kDetected 25 days ago
PythonMachine LearningDeep LearningTensorFlowPyTorchData EngineeringLLMsStatisticsLogisticsResearchCommunication
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odds of building a lasting career here
59Sponsors, lottery-bound
Cap-exempt (no lottery)0
Sponsors this role80
Entry-level history0
PERM / green-card track0
Lottery odds (Level IV)94
Fits your clock70
Sponsors, but it's cap-subject — you still face the weighted lottery (~61% per draw at Level IV). Good if you win; have a cap-exempt backup on your list.
Lottery odds assume a STEM candidate.
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About the role
- Mid-training is a step between pre-training and post-training, where we take a base model and train it into the foundation for reasoning.
- This role blends fundamental research and practical engineering, as we do not distinguish between the two internally.
- It's an excellent fit for someone comfortable working across the boundary of pre-training and post-training, and who wants to shape what our models can do at their core.
Responsibilities
- Build the pipelines that filter, deduplicate, verify, and rewrite raw material into training-grade datasets.
- Design and measure interventions that increase knowledge: targeted corpora, synthetic rephrasings and QA over source documents, knowledge-dense mixes.
- Build the quality pipeline.
- Own the automatic filtering and scoring stack: train and calibrate quality classifiers and LLM-based judges, build verifiers for synthetic data, and use them to raise the quality bar of the mix by a lot, not a little.
- Develop and tune the recipe. Iterate on mid-training recipes: the collection of datasets, training stages, annealing schedules, and hyperparameters. Measure how recipe choices affect metrics, including downstream of post-training.
- 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 we can bring to the next problem.
- Scale and explore. Mid-training will involve a combination of scaling existing methodologies and developing new ones. We'll want to both measure how performance scales with dataset size and explore using completely different kinds of training data.
- We believe the future worth building is human, and we're hiring people who want to build it.
- Develop and tune the recipe.
Requirements
- Comfort debugging distributed training and writing code that scales.
- Clarity in communication, an ability to explain complex technical concepts in writing.
Nice to have
- A strong grasp of probability, statistics, and ML fundamentals.
- You can look at experimental data and distinguish between real effects, noise, and bugs.
- Experience with knowledge injection, continued pre-training, or domain adaptation of large models, and with measuring what a model knows.
- Experience generating synthetic data and understanding its failure modes (diversity collapse, contamination, teacher artifacts).
- Research or engineering contributions in alignment, data-centric AI, or human-AI collaboration.
- This is an IC role within Thinking Machines' Research Team.
- You will report directly to our Head of Research, and work most closely with our small group of post-training researchers.
- (we encourage you to apply if you meet some but not all of these):
Compensation
- Depending on background, skills and experience, the expected annual salary range for this position is $350,000- $475,000 USD.
Benefits
- Thinking Machines offers generous health, dental, and vision benefits, unlimited PTO, paid parental leave, and relocation support as needed.
- Proficiency in Python and familiarity with deep learning frameworks (e.g., PyTorch, TensorFlow, or JAX).
- Bachelor's degree or equivalent experience in Computer Science, Machine Learning, Physics, Mathematics, or a related discipline with strong theoretical and empirical grounding.
Company info
- 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.
Visa & Work Authorization
- 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.
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This listing is sourced directly from Thinking Machines Lab's careers page and normalized into a canonical job model.