About the Role

We're hiring a compensation partner to join our Operations team. You'll own the full spectrum of compensation at Thinking Machines – from building our philosophy and job architecture to partnering on day-to-day offer and pay decisions. 

This role directly impacts our ability to hire and retain exceptional talent. We're looking for a compensation partner who brings a point of view, not just a process.

What You’ll Do

• Design and implement Thinking Machines’ overall compensation framework and programs to attract and retain highly sought-after talent.
• Advise managers and leadership on pay decisions across offers, promotions, and retention.
• Establish our compensation strategy based on market research, analysis, and your own judgment, with awareness that standards benchmarks lag the reality of the talent market.
• Build the equity compensation framework in partnership with finance and leadership.
• Communicate compensation to candidates and employees in a clear way that builds trust. 

Skills and Qualifications

Minimum qualifications:

• 7+ years of compensation experience in a high-growth environment.
• Track record building compensation programs from the ground up, ideally in an early-stage environment.
• Experience operating in a competitive and dynamic talent market without relying on benchmarks and mature processes.
• Ability to articulate a clear point of view on good compensation philosophy.

Preferred Qualifications

• Experience at an AI lab, foundation model company, or high-growth tech company where compensation moves faster than survey cycles.
• Familiarity with the compensation landscape across research (PhDs, postdocs, industry researchers), engineering, and non-technical functions, and how expectations differ meaningfully across all three.

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 $250,000 - $425,000 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.

About the Role

We are hiring a developer productivity engineer to advance how we build software internally: safely, quickly, and with delight. The main focus are AI tools and coding agents. You’ll partner with platform, security, and product engineers to build state-of-the-art tooling for AI-assisted software development, and make our inner loop dramatically faster. 

The scope of this role includes both setting up company-wide platforms and working with developers to accelerate their individual workflows. 

Note: This is an "evergreen role" that we keep open on an on-going basis to express interest. 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

• Enable our researchers and engineers to leverage AI to improve coding productivity without compromising code quality
• Standardize AI coding tools, such as Claude Code, Cursor, and Codex. You will help configure, harden, and maintain the best tools, integrating org-wide configurations with individual preferences.
• Build secure, reproducible agent sandboxes for remote dev & CI testing.
• Set up golden-path dev environments and guardrails for secrets/PII.
• Help individual contributors develop their personalized AI-enabled workflow.
• Track tool usage, reliability, and cost.

Skills and Qualifications

Minimum qualifications:

• Bachelor’s degree or equivalent industry experience in computer science, engineering, or similar.
• Experience developing productivity tools and best practices for large codebases.
• Ability to communicate clearly and work with researchers to build and manage a variety of internal tools.

Preferred qualifications — we encourage you to apply even if you don’t meet all preferred qualifications, but at least some:

• Hands-on experience with container platforms (e.g. Docker/Kubernetes), modern CI (GitHub Actions/Buildkite), and package management tools (uv).
• Practical experience with AI coding tools and model APIs (e.g. OSS via vLLM / SGLang / TGI).
• Solid Linux/networking fundamentals; comfort with secrets management and safe egress.
• Proficiency in systems programming languages (e.g. Rust) and scripting languages (e.g. Python).

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.

About the Role

We’re looking for an engineer to join us and contribute to data infrastructure. You'll join a small, high-impact team responsible for architecting and scaling the core infrastructure behind distributed training pipelines, multimodal data catalogs, and intelligent processing systems that operate over petabytes of data.

Infrastructure is critical to us: it's the bedrock that enables every breakthrough. You'll work directly with researchers to accelerate experiments, develop new datasets, improve infrastructure efficiency, and enable key insights across our data assets.

If you're excited by distributed systems, large-scale data mining, open-source tools like Spark, Kafka, Beam, Ray, and Delta Lake, and enjoy building from the ground up, we'd love to hear from you.

Note: This is an "evergreen role" that we keep open on an on-going basis to express interest. 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

• Design, build, and operate scalable, fault-tolerant infrastructure for LLM Research: distributed compute, data orchestration, and storage across modalities.
• Develop high-throughput systems for data ingestion, processing, and transformation — including training data catalogs, deduplication, quality checks, and search.
• Build systems for traceability, reproducibility, and robust quality control at every stage of the data lifecycle.
• Implement and maintain monitoring and alerting to support platform reliability and performance.
• Collaborate with research teams to unlock new features, improve data quality, and accelerate training cycles.

Skills and Qualifications

Minimum qualifications:

• Bachelor’s degree or equivalent experience in computer science, engineering, or similar.
• Proficiency in at least one backend language (we use Python or Rust).
• Are fluent in distributed compute frameworks such as Apache Spark or Ray.
• Are deeply familiar with cloud infrastructure, data lake architectures, and batch and streaming pipelines.
• Comfort operating across the stack and owning projects end-to-end.
• Thrive in a highly collaborative environment involving many, different cross-functional partners and subject matter experts.
• A bias for action with a mindset to take initiative to work across different stacks and different teams where you spot the opportunity to make sure something ships.

Preferred qualifications — we encourage you to apply if you meet some but not all of these:

• Have hands-on experience with Kafka, dbt, Terraform, and Airflow.
• Have experience building a web crawler.
• Have extensive experience understanding and scaling deduplication, data mining, and search.
• Have strong knowledge of file formats and storage systems (e.g., Parquet, Delta Lake, etc.) and how they impact performance and scalability.
• Are proactive about documentation, testing, and empowering your teammates with good tooling.

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.

About Tinker

Tinker is our fine-tuning API that empowers researchers and developers to customize frontier AI to their needs — opening access to capabilities that have previously been concentrated in a handful of labs. We manage the infrastructure while allowing Tinkerers full flexibility in training open weights models with their own data, algorithms, and for their own needs. Tinker is rapidly adding new customers, features, and novel use-cases. We’re hiring to grow the platform alongside the Tinker community.

About the Role

We're looking for a Site Reliability Engineer to drive the reliability of Tinker end-to-end. You'll work alongside the engineers building the platform and research teams to make every layer of the system more robust and resilient. 

What You’ll Do

• Define and own end-to-end reliability, from CI/CD flows to production observability and incident response.
• Develop appropriate Service Level Objectives for distributed training systems, balancing job completion reliability and scheduling latency with development velocity.
• Design and implement monitoring and observability across the full training path.
• Drive incident response for Tinker platform issues, ensuring rapid recovery, thorough incident reviews, and systematic improvements that prevent recurrence.
• Harden multi-tenant isolation and resource scheduling so that LoRA-based workload co-scheduling maximizes utilization without compromising reliability or data separation
• Collaborate with security teams to address production vulnerabilities

Skills and Qualifications

Minimum qualifications:

• Bachelor's degree or equivalent experience in computer science, engineering, or similar.
• Experience in distributed systems, cloud infrastructure, or site reliability engineering.
• Proficiency writing software to solve reliability problems, including building tooling and automation.
• Experience with production incident response, postmortems, and systematic reliability improvement.
• Strong communication skills and track record of coordination across engineering and research teams.

Preferred qualifications — we encourage you to apply if you meet some but not all of these:

• Deep experience operating production cloud services at scale (e.g., public cloud platforms, internal cloud services)
• Background in distributed training frameworks and how infrastructure failures surface in training behavior.
• Track record building checkpoint and recovery systems for long-running distributed jobs.
• Expertise in Kubernetes at scale: deploying, operating, debugging, and tuning clusters handling heterogeneous GPU workloads.

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.

About the Role

Thinking Machines builds multimodal-first. We’re looking for new team members to advance the science of visual perception and multimodal learning. We think about how vision and language interact at scale. We design architectures that fuse pixels and text, build datasets and evaluation methods that test real-world comprehension, and develop representations that let models ground abstract concepts in the physical world. Our goal is to create multimodal systems that support seamless integration into real-world environments.

You’ll work at the intersection of visual understanding, multimodal reasoning, and large-scale model training. You’ll help develop the architectures, data, and evaluation tools that teach AI to see, understand, and collaborate. The best candidate is curious about multimodal interfaces, has experience running large scale experiments and is comfortable contributing to complex engineering systems. While we are looking for a person with expertise in multimodality, Thinking Machines Lab operates in a unified fashion and expects new hires to work across modalities as one team.

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

• Own research projects on training and performance analysis of multimodal AI models.
• Curate and build large-scale datasets and evaluation benchmarks to advance vision capabilities.
• Work with our data infrastructure engineers, pretraining researchers and engineers, and product team to create frontier multimodal models and the products that leverage them.
• 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:

• Ability to design, run, and analyze experiments thoughtfully, with demonstrated research judgment and empirical rigor.
• Understanding of machine learning fundamentals, large-scale training, and distributed compute environments.
• 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:

• Research or engineering contributions in visual  reasoning, spatial understanding, or multimodal architecture design.
• Experience developing evaluation frameworks for multimodal tasks.
• Publications or open-source contributions in vision-language modeling, video understanding, or multimodal AI.
• A strong grasp of probability, statistics, and ML fundamentals. You can look at experimental data and distinguish between real effects, noise, and bugs.
• 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.

About the Role

As a Research Product Manager (RPM) at Thinking Machines Lab, you’ll play a central role in driving complex, high-impact technical products and programs that span research, infrastructure, and applied. You’ll help turn ambitious ideas into reality by driving cross-functional efforts, maintaining momentum across projects, and creating clarity in fast-moving, ambiguous environments.

Your work will connect people, ideas, and systems to ensure that our most important research initiatives stay aligned, well-scoped, and moving forward efficiently. It’s for someone who thrives in deeply technical discussions, understands the rhythm of research, can abstract at high-level and get into the weeds, with the ultimate goal of helping the company execute at scale.

Note: This is an "evergreen role" that we keep open on an on-going basis to express interest. 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

• Drive and coordinate large-scale research products and programs, ensuring that complex projects are executed efficiently, transparently, and with scientific excellence.
• Translate technical ideas into actionable, well-scoped plans, defining milestones and keeping teams aligned across model development, data campaigns, infrastructure, and product integration.
• Collaborate across disciplines — from research and ML infrastructure to legal and business development — ramping up quickly on new domains as needed.
• Create and maintain compute and resource roadmaps, identifying bottlenecks, trade-offs, and opportunities to accelerate progress.
• Synthesize and communicate progress across diverse technical teams, ensuring the right people are looped in at the right time and that information flows smoothly across the organization.
• Support the integration of new technologies and research insights into production systems and product roadmaps, bridging the gap between frontier research and real-world applications.

Skills and Qualifications

Minimum qualifications:

• Degree in computer science, artificial intelligence, mathematics, physics, engineering, or similar
• Experience in research program management, product management, or similar

Preferred qualifications — we encourage you to apply if you meet some but not all of these:

• Masters or PhD in computer science, artificial intelligence, mathematics, physics, engineering, or similar
• Past experience at a frontier or academic research lab, contributing to research in artificial intelligence
• Past publications relevant to artificial intelligence or frontier models, with contributions to areas like evals, multimodality, human-ai interaction, post-training, pre-training, data, or other similar domains needed to conduct frontier AI research
• Ability to learn new technical domains
• Strong technical communication, written and verbal

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 $175,000 - $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.

About the Role

The role of pre-training researchers sits at the core of our roadmap. This work advances the science of how large models learn from data. You’ll explore new pre-training methods, architectures, and learning objectives that make model training efficient, robust, and aligned with human goals.

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

• Research and develop new methodologies for pre-training.
• Work in areas such as scaling, architecture, algorithms, or optimization of large scale training runs depending on your research interest and experience.
• Design data curricula and sampling strategies that improve learning dynamics and model generalization.
• Collaborate with infrastructure and data teams to conduct large-scale experiments efficiently and reproducibly.
• 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:

• Ability to design, run, and analyze experiments thoughtfully, with demonstrated research judgment and empirical rigor.
• Experience with distributed or high-performance computing environments.
• 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 training or analyzing large-scale models, or contributing to pre-training or foundation model research.
• Strong publication record or open-source contributions in representation learning, optimization, scaling laws, or other areas of pre-training.
• Familiarity with curriculum learning, data selection, or active learning techniques.
• Experience designing or maintaining evaluation frameworks for large models.
• Contributions to open datasets, research publications, or data tooling.
• 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.

About the Role

The role of pre-training researchers sits at the core of our roadmap. This work blends research with large-scale data engineering to help assemble the pre-training datasets and data systems that underpin the next generation of AI models. You’ll design and implement methods for sourcing, curating, and analyzing pre-training data for quality and performance.

You’ll work with automated pipelines and human-in-the-loop processes, contributing both scientific insight and production-grade code. It’s ideal for someone who enjoys working at the intersection of data, machine learning, and systems, and who’s excited by the challenge of shaping frontier AI.

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

• Design and implement techniques for curating, sourcing, and filtering large-scale text, code, and multimodal data.
• Develop data quality metrics and analysis to measure coverage, diversity, and representativeness across sources.
• Collaborate with research and infrastructure teams to scale data processing systems efficiently and reproducibly.
• Investigate and mitigate data risks, including privacy, safety, and licensing concerns, to ensure responsible and ethical data use.
• Continuously evaluate dataset improvements by analyzing their downstream effects on model learning and behavior.
• 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.
• Experience with curation, preprocessing, and analysis of large-scale text, code, or multimodal datasets.
• Prior experience in data engineering, dataset construction, or large-scale web data processing for machine learning models.
• Experience evaluating or improving training data quality and knowledge of data ethics, safety, and licensing frameworks relevant to AI dataset creation.
• Contributions to open datasets, research publications, or data tooling.
• 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.

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.

Post-training data research work sits at the intersection of human insight and machine learning. Our work combines human and synthetic data techniques, along with other innovative approaches, to capture the nuances of human behavior and use them to steer models. We research and model the mechanisms that create value for people to explain, predict, and optimize for human preferences, behaviors, and satisfaction. Our goal is to turn research ideas into data by scoping well-run data labeling or collection campaigns, and understanding the science behind what makes the data high quality and useful to train our models. We also develop and evaluate quantitative metrics that measure the success and impact of our data and training interventions.

Beyond execution, we explores new paradigms for human-ai interaction and scalable oversight, experimenting with how humans can best supervise, guide, and collaborate with models. It’s interdisciplinary work that blends research, data operations, and technical implementation to advance the frontier of aligned, human-centered AI systems.

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

• Design and execute data collection and synthesis strategies for post-training by combining human feedback, preference data, and synthetic examples to guide model behavior.
• Develop pipelines and frameworks for scalable, high-quality human labeling, model-assisted labeling, and synthetic data generation.
• Research and model human preferences and behavior, creating data-driven methods to improve reasoning, truthfulness, and helpfulness.
• 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.
• Design and evaluate metrics and benchmarks that measure data quality, alignment, and the real-world impact of post-training interventions.
• Scale and explore: post-training will involve a combination of scaling the existing methodologies and developing new ones.
• 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:

• Strong engineering skills, ability to contribute code and debug in complex codebases.
• Experience with data curation, human feedback, or synthetic data generation for large language models or similar systems.
• Ability to design, run, and interpret experiments with scientific rigor and clarity.
• 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.
• Familiarity with synthetic data pipelines, active learning, or model-assisted labeling
• 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.

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.

About the Role

We’re looking for an infrastructure research engineer to design and build the core systems that enable scalable, efficient training of large models for deployment and research. Your goal is to make experimentation and training at Thinking Machines fast and reliable to ensure our research teams can focus on science, not system bottlenecks.

This role is ideal for someone who blends deep systems and performance expertise with a curiosity for machine learning at scale. You’ll take ownership of the training stack end to end, ensuring every GPU cycle drives scientific progress.

Note: This is an "evergreen role" that we keep open on an on-going basis to express interest. 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

• Design, implement, and optimize distributed training systems that scale across thousands of GPUs and nodes for large-scale training workloads.
• Develop high-performance optimizations to maximize throughput and efficiency.
• Develop reusable frameworks and libraries to improve training reproducibility, reliability, and scalability for new model architectures.
• Establish standards for reliability, maintainability, and security, ensuring systems are robust under rapid iteration.
• Collaborate with researchers and engineers to build scalable infrastructure.
• Publish and share learnings through internal documentation, open-source libraries, or technical reports that advance the field of scalable AI infrastructure.

Skills and Qualifications

Minimum qualifications:

• Bachelor’s degree or equivalent experience in computer science, electrical engineering, statistics, machine learning, physics, robotics, or similar.
• Strong engineering skills, ability to contribute performant, maintainable code and debug in complex codebases
• Understanding of deep learning frameworks (e.g., PyTorch, JAX) and their underlying system architectures.
• Thrive in a highly collaborative environment involving many, different cross-functional partners and subject matter experts.
• A bias for action with a mindset to take initiative to work across different stacks and different teams where you spot the opportunity to make sure something ships.

Preferred qualifications — we encourage you to apply if you meet some but not all of these:

• Past experience working on distributed training for the world’s largest models to make them stable, reliable, and performant.
• Track record of improving research productivity through infrastructure design or process improvements.
• Contributions to open-source ML infrastructure such as PyTorch, XLA, Megatron-LM, or DeepSpeed.

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.

About Tinker

Tinker is our fine-tuning API that empowers researchers and developers to customize frontier AI to their needs — opening access to capabilities that have previously been concentrated in a handful of labs. We manage the infrastructure while allowing Tinkerers full flexibility in training open weights models with their own data, algorithms, and for their own needs. Tinker is rapidly adding new customers, features, and novel use-cases. We’re hiring to grow the platform alongside the Tinker community.

About the Role

We're hiring a Forward Deployed Engineer to be the main point of contact with our customers, which range from individual hackers to major enterprises. You will diagnose their problems and needs and turn those into product improvements, taking ownership of both ends of this process.

What You’ll Do

• Triage and resolve customer issues across the full stack, reading logs, reproducing failures, and tracing job execution.
• Build tools, integrations, and automation that eliminate recurring failure modes and reduce time-to-resolution for users.
• Write and maintain high-quality documentation and cookbook recipes, grounded in real issues and implementations from our users.
• Partner closely with research and infrastructure teams to translate customer pain into prioritized engineering work.
• Inform Tinker's product roadmap based on what you learn from working directly with customers every day.
  
  

Skills and Qualifications

Minimum qualifications:

• Bachelor's degree or equivalent experience in computer science, engineering, or similar.
• Experience fine-tuning large language models with supervised and reinforcement learning methods.
• Experience debugging distributed systems and training infrastructure: reading logs, tracing job failures, diagnosing GPU, networking, or scheduling issues.
• Proficiency in at least one backend language (we use Python and Rust).

Preferred qualifications — we encourage you to apply if you meet some but not all of these:

• 5+ years of software engineering experience, including work in customer-facing or deployment roles.
• Building or shipping a product end-to-end — a startup, an open-source project, or an internal tool you owned from idea to users.
• Published technical tutorials, recipes, or documentation that helped others get unstuck.
• Experience at a developer tools, API-first, or ML platform company.
• Familiarity with hyperparameter tuning for LLM training algorithms.
• Contributions to open-source ML or infrastructure projects.

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.

About the Role

We’re looking for an infrastructure research engineer to design and build the core systems that enable scalable, efficient training of large models through reinforcement learning.

This role sits at the intersection of research and large-scale systems engineering: a builder who understands both the algorithms behind RL and the realities of distributed training and inference at scale. You’ll wear many hats, from optimizing rollout and reward pipelines to enhancing reliability, observability, and orchestration, collaborating closely with researchers and infra teams to make reinforcement learning stable, fast, and production-ready.

Note: This is an "evergreen role" that we keep open on an on-going basis to express interest. 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

• Design, build, and optimize the infrastructure that powers large-scale reinforcement learning and post-training workloads.
• Improve the reliability and scalability of RL training pipeline, distributed RL workloads, and training throughput.
• Develop shared monitoring and observability tools to ensure high uptime, debuggability, and reproducibility for RL systems.
• Collaborate with researchers to translate algorithmic ideas into production-grade training pipelines.
• Build evaluation and benchmarking infrastructure that measures model progress on helpfulness, safety, and factuality.
• Publish and share learnings through internal documentation, open-source libraries, or technical reports that advance the field of scalable AI infrastructure.

Skills and Qualifications

Minimum qualifications:

• Bachelor’s degree or equivalent experience in computer science, electrical engineering, statistics, machine learning, physics, robotics, or similar.
• Strong engineering skills, ability to contribute performant, maintainable code and debug in complex codebases
• Understanding of deep learning frameworks (e.g., PyTorch, JAX) and their underlying system architectures.
• Thrive in a highly collaborative environment involving many, different cross-functional partners and subject matter experts.
• A bias for action with a mindset to take initiative to work across different stacks and different teams where you spot the opportunity to make sure something ships.

Preferred qualifications — we encourage you to apply if you meet some but not all of these:

• Experience training or supporting large-scale language models with tens of billions of parameters or more.
• Experience working with reinforcement learning workloads (e.g., PPO, DPO, RLHF, or reward modeling).
• Background in high-performance or reliability engineering — distributed training frameworks and cluster orchestration (Kubernetes, Slurm).
• Familiarity with monitoring and observability tools (Prometheus, Grafana, OpenTelemetry).
• Contributions to large-scale ML research or infrastructure, open-source frameworks, or internal performance optimization efforts.

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.

About the Role

We’re looking for an infrastructure research engineer to design and build the core systems that enable efficient large-scale model training with a focus on numerics. You will focus on improving the numerical foundations of our distributed training stack, from precision formats and kernel optimizations to communication frameworks that make training trillion-parameter models stable, scalable, and fast.

This role is ideal for someone who thrives at the intersection of research and systems engineering: a builder who understands both the math of optimization and the realities of distributed compute.

Note: This is an "evergreen role" that we keep open on an on-going basis to express interest. 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

• Design and optimize distributed training infrastructure for large-scale LLMs, focusing on performance, stability, and reproducibility across multi-GPU and multi-node setups.
• Implement and evaluate low-precision numerics (for example, BF16, MXFP8, NVFP4) to improve efficiency without sacrificing model quality.
• Develop kernels and communication primitives that use hardware-level support for mixed and low-precision arithmetic.
• Collaborate with research teams to co-design model architectures and training recipes that align with emerging numeric formats and stability constraints.
• Prototype and benchmark scaling strategies such as data, tensor, and pipeline parallelism that integrate precision-adaptive computation and quantized communication.
• Contribute to the design of our internal orchestration and monitoring systems to ensure that thousands of distributed experiments can run efficiently and reproducibly.
• Publish and share learnings through internal documentation, open-source libraries, or technical reports that advance the field of scalable AI infrastructure.

Skills and Qualifications

Minimum qualifications:

• Bachelor’s degree or equivalent experience in computer science, electrical engineering, statistics, machine learning, physics, robotics, or similar.
• Understanding of deep learning frameworks (e.g., PyTorch, JAX) and their underlying system architectures.
• Thrive in a highly collaborative environment involving many, different cross-functional partners and subject matter experts.
• A bias for action with a mindset to take initiative to work across different stacks and different teams where you spot the opportunity to make sure something ships.
• Strong engineering skills, ability to contribute performant, maintainable code and debug in complex codebases in areas such as floating-point numerics, low-precision arithmetic, and distributed systems.

Preferred qualifications — we encourage you to apply if you meet some but not all of these:

• Familiarity with distributed frameworks such as PyTorch/XLA, DeepSpeed, Megatron-LM.
• Experience implementing FP8, INT8, or block-floating point (MX) formats and understanding their numerical trade-offs.
• Prior contributions to open-source deep learning infrastructure such as PyTorch, DeepSpeed, or XLA.
• Publications, patents, or projects related to numerical optimization, communication-efficient training, or systems for large models.
• Experience training and supporting large-scale AI models.
• Track record of improving research productivity through infrastructure design or process improvements.

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.

About the Role

We’re looking for an infrastructure research engineer to design, optimize, and maintain the compute foundations that power large-scale language model training. You will develop high-performance ML kernels (e.g., CUDA, CuTe, Triton), enable efficient low-precision arithmetic, and improve the distributed compute stack that makes training large models possible.

This role is perfect for an engineer who enjoys working close to the metal and across the research boundary. You’ll collaborate with researchers and systems architects to bridge algorithmic design with hardware efficiency. You’ll prototype new kernel implementations, profile performance across hardware generations, and help define the numerical and parallelism strategies that determine how we scale next-generation AI systems.

Note: This is an "evergreen role" that we keep open on an on-going basis to express interest. 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

• Design and implement custom ML kernels (e.g., CUDA, CuTe, Triton) for core LLM operations such as attention, matrix multiplication, gating, and normalization, optimized for modern GPU and accelerator architectures.
• Design and think through compute primitives to reduce memory bandwidth bottlenecks and improve kernel compute efficiency.
• Collaborate with research teams to align kernel-level optimizations with model architecture and algorithmic goals.
• Develop and maintain a library of reusable kernels and performance benchmarks that serve as the foundation for internal model training.
• Contribute to infrastructure stability and scalability, ensuring reproducibility, consistency across precision formats, and high utilization of compute resources.
• Document and share insights through internal talks, technical papers, or open-source contributions to strengthen the broader ML systems community.

Skills and Qualifications

Minimum qualifications:

• Bachelor’s degree or equivalent experience in computer science, electrical engineering, statistics, machine learning, physics, robotics, or similar.
• Strong engineering skills, ability to contribute performant, maintainable code and debug in complex codebases
• Understanding of deep learning frameworks (e.g., PyTorch, JAX) and their underlying system architectures.
• Thrive in a highly collaborative environment involving many, different cross-functional partners and subject matter experts.
• A bias for action with a mindset to take initiative to work across different stacks and different teams where you spot the opportunity to make sure something ships.
• Proficiency in CUDA, CuTe, Triton, or other GPU programming frameworks.
• Demonstrated ability to analyze, profile, and optimize compute-intensive workloads.

Preferred qualifications — we encourage you to apply if you meet some but not all of these:

• Experience training or supporting large-scale language models with tens of billions of parameters or more.
• Track record of improving research productivity through infrastructure design or process improvements.
• Experience developing or tuning kernels for deep learning frameworks such as PyTorch, JAX, or custom accelerators.
• Familiarity with tensor parallelism, pipeline parallelism, or distributed data processing frameworks.
• Experience implementing low-precision formats (FP8, INT8, block floating point) or contributing to related compiler stacks (e.g., XLA, TVM).
• Contributions to open-source GPU, ML systems, or compiler optimization projects.
• Prior research or engineering experience in numerical optimization, communication-efficient training, or scalable AI infrastructure.

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.

About the Role

We’re looking for an infrastructure research engineer to design, optimize, and scale the systems that power large AI models. Your work will make inference faster, more cost-effective, more reliable, and more reproducible to enable our teams to focus on advancing model capabilities rather than managing bottlenecks.

Our focus is on performant and efficient model inference both to power real-world applications and to accelerate research. This role is responsible for the infrastructure that ensures every experiment, evaluation, and deployment runs smoothly at scale.

Note: This is an "evergreen role" that we keep open on an on-going basis to express interest. 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

• Work alongside researchers and engineers to bring cutting-edge AI models into production.
• Collaborate with research teams to enable high-performance inference for novel architectures.
• Design and implement new techniques, tools, and architectures that improve performance, latency, throughput, and efficiency.
• Optimize our codebase and compute fleet (e.g., GPUs) to fully utilize hardware FLOPs, bandwidth, and memory.
• Extend orchestration frameworks (e.g., Kubernetes, Ray, SLURM) for distributed inference, evaluation, and large-batch serving.
• Establish standards for reliability, observability, and reproducibility across the inference stack.
• Publish and share learnings through internal documentation, open-source libraries, or technical reports that advance the field of scalable AI infrastructure.
  
  

Skills and Qualifications

Minimum qualifications:

• Bachelor’s degree or equivalent experience in computer science, engineering, or similar.
• Understanding of deep learning frameworks (e.g., PyTorch, JAX) and their underlying system architectures.
• Experience with inference serving systems optimized for throughput and latency (e.g., SGLang, vLLM).
• Thrive in a highly collaborative environment involving many, different cross-functional partners and subject matter experts.
• A bias for action with a mindset to take initiative to work across different stacks and different teams where you spot the opportunity to make sure something ships.
• Strong engineering skills, ability to contribute performant, maintainable code and debug in complex codebases

Preferred qualifications — we encourage you to apply if you meet some but not all of these:

• Experience training or supporting large-scale language models with hundreds of billions of parameters or more.
• Understanding of distributed compute systems, GPU parallelism, and hardware-aware optimizations.
• Contributions to open-source ML or systems infrastructure projects (e.g., SGLang, vLLM, PyTorch, Triton, DeepSpeed, XLA).
• Track record of improving research productivity through infrastructure design or process improvements.

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.

About the Role

Thinking Machines builds multimodal-first. For us, there is no separate multimodal work. It’s at the core of everything we do, from the scientific goals we’re setting to the infrastructure we’re building. We’re looking for researchers to advance the frontier of audio capabilities. You’ll explore how audio models enable more natural and efficient communication/collaboration, preserving more information and capturing user intent.

This is a highly collaborative role. You’ll work closely across pre-training, post-training, and product with world-class researchers, infrastructure engineers, and designers. This is an opportunity to shape the fundamental capabilities of AI systems that millions of people will use.

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

• Own research projects on audio training, low-latency inference and conversational responsiveness.
• Design and train large-scale models that natively support audio input and output.
• Investigate scaling behavior such as how data, model size, and compute affect capability and efficiency.
• Build and maintain audio data pipelines, including preprocessing, filtering, segmentation, and alignment for training and evaluation.
• Collaborate with data and infrastructure teams to scale audio training efficiently across distributed systems.
• 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:

• Ability to design, run, and analyze experiments thoughtfully, with demonstrated research judgment and empirical rigor.
• Understanding of machine learning fundamentals, large-scale training, and distributed compute environments.
• 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.
• Experience with real-time inference, streaming architectures, or optimization for low latency.
• Prior experience training or evaluating large-scale audio or multimodal models.
• Publications, releases, or open-source projects related to speech, audio, voice, or similar areas.
• Demonstrated experience in audio or speech modeling, including ASR, TTS, or self-supervised audio learning.
• 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.

About the Role

We're hiring a Recruiting Coordinator to help us build a best-in-class recruiting engine as we scale. This is a coordination-heavy role, but the scope goes beyond scheduling - you'll own the systems and processes that keep the team organized and moving, and you'll be expected to improve them over time.

This means helping to build the processes, documentation, and systems that grow with us, not just executing the ones that already exist. Beyond scheduling, you'll plug into broader recruiting operations such as talent mapping, reporting, referral processing, and events - wherever the team needs support.

What You’ll Do

Recruiting Operations

• Maintain data integrity across our ATS and recruiting tools - audit regularly and keep things accurate.
• Support talent mapping, referral processing, inbound application triage, and recruiting events.
• Track interview feedback completion and follow up to make sure nothing stalls.
• Identify process gaps and propose improvements - this role is expected to make the function better, not just execute.
• Collect, organize, and audit interview feedback to ensure nothing gets lost or delayed.
• Maintain up-to-date recruiting information in documentation and in Slack, support data integrity across recruiting systems.

Candidate Scheduling & Experience

• Manage high-volume interview coordination across multiple time zones, calendars, and stakeholders while maintaining a world-class candidate experience.
• Coordinate all interview logistics, from travel and expenses to room setup and technical support.
• Serve as the main point of contact for candidates, communicating in a warm, professional, and informative manner from initial screening through offer.
• Anticipate scheduling bottlenecks before they happen: monitor interviewer availability, flag conflicts early, and keep hiring velocity high.
• Scale interviewer pools by partnering with teams to identify, train, and prepare new interviewers as hiring volume grows
• Use modern scheduling tools (e.g., Greenhouse, ModernLoop) to automate scheduling, reduce manual work, and improve response speed
• Manage high-volume scheduling across time zones, calendars, and multiple stakeholders.

Skills and Qualifications

Minimum qualifications:

• Experience coordinating schedules, managing calendars, or supporting operations in a fast-paced environment
• Excellent written and verbal communication skills, especially in direct candidate communications
• Ability to adapt quickly to different teams, roles, and ways of working without losing your footing
• Comfortable with Google Workspace, scheduling tools, and Slack
• A do-what-it-takes mindset: you’re proactive, detail-oriented, and don’t let things fall through the cracks

Preferred qualifications — we encourage you to apply if you meet some but not all of these:

• 1+ years in a high-volume scheduling or recruiting coordination role
• Experience with ATS, HRIS, and scheduling tools like Greenhouse, GoodTime, Workday, and ModernLoop.
• Experience partnering closely with recruiters and hiring managers
• Proven ability to be proactive in ambiguous situations and propose multiple solutions
• Experience supporting technical or leadership-level hiring

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 $140,000 - $200,000 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.

About the Role

We’re looking for an infrastructure engineer to own and evolve the security infrastructure that underpins our foundation models. In this role, you’ll work across compute, storage, networking, and data platforms, making sure our systems are secure, reliable, and built to scale. You’ll shape controls, architecture, and tooling so that security is part of how the platform works by default. You’ll partner closely with research and product teams, enabling them to move quickly while keeping our models, data, and environments protected.

Note: This is an "evergreen role" that we keep open on an on-going basis to express interest. 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

• Architect security patterns for platforms and services, including network segmentation, service-to-service authentication, RBAC, and policy enforcement in Kubernetes and cloud environments.
• Manage identity, access, and secrets for humans and services: workload and cross-cloud identity, least-privilege IAM, and secrets management.
• Build secure platforms for data ingestion, processing, and curation: classification, encryption, access controls, and safe sharing patterns across teams.
• Write threat models and review designs with researchers and engineers to help them ship features and experiments in a safe, scalable way.
• Automate security checks and build guardrails: policy-as-code, secure infrastructure baselines, validation in CI/CD, and tools that make the secure path the easiest one.

Skills and Qualifications

Minimum qualifications:

• Bachelor’s degree or equivalent experience in engineering, or similar.
• Strong background with containers and orchestration (e.g., Kubernetes) and how to secure them (namespaces, network policies, pod security, admission controls, etc.)
• Practical experience with Infrastructure as Code (Terraform or similar), including secure patterns for provisioning networks, IAM, and shared services.
• Solid understanding of cloud networking and security: VPCs, load balancers, service discovery, mTLS, firewalls, and zero-trust-style architectures.
• Proficiency with a systems language such as Rust and scripting in Python for building platform components and internal tools.
• Evidence of owning complex, production-critical systems, including debugging issues that span infra, security, and application layers.

Preferred qualifications — we encourage you to apply if you meet some even if you don't meet all of these:

• Experience with ML infrastructure, GPU clusters, or large-scale training environments (schedulers, job queues, shared storage, multi-tenant clusters).
• Background in AI labs, HPC environments, or ML-heavy organizations where both security and performance are first-class concerns.
• Experience profiling and tuning high-throughput systems, and an ability to reason about the cost of additional security layers.
• Talks, blogs, or publications on infrastructure security, distributed systems, or performance engineering.
• Open-source contributions to security, orchestration, observability, or infrastructure tooling.
• Familiarity with securing specialized hardware (GPUs, TPUs) and their integrations into training and inference pipelines.

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 $200,000 - $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.

HR Business Partner

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 a small team of scientists, engineers, and builders who've created some of the most widely used AI products including ChatGPT, Character.ai, and PyTorch. As we scale our team, some of the hardest challenges we face are about empowering and aligning our people and helping our managers make crucial decisions under uncertainty.

About the Role

The new role of HR Business Partner will combine two tasks: leadership coaching, and people systems design. The two require different skillsets, but a shared vision for managing talent.

You will coach managers at Thinking Machines Lab to be more effective leaders. You will provide strategic support on researcher and engineer performance, team dynamics, and personal growth. You will also build the people infrastructure that will scale this support as the company grows: performance and feedback systems, compensation structures, and career frameworks.

What You’ll Do

• Coach managers by observing how they lead, identifying strengths and blind spots, and working on continuous improvement.
• Advise leadership on organizational decisions: team structure, succession planning, and strategic people choices that shape how we work.
• Design compensation structures that let us compete for the best machine learning talent in the world while staying aligned on values and principles.
• Create career/leveling frameworks that work for a research lab where career advancement often doesn’t mean managing people, contributions such as mentorship and taste are harder to measure, and where senior researchers expect to grow and learn even after a decade in the role.
• Build feedback and evaluation processes that help people improve, not just get measured.

Skills and Qualifications

Minimum qualifications:

• 5+ years experience working as a people leader or HR business partner in a high-growth technical environment.
• Fluency with employment law and best practices in North America.
• Proven track record of developing HR practices and supporting a diverse set of talent.

Preferred qualifications:

We encourage you to apply even if you don’t meet all preferred qualifications.

• 10+ years experience as a people leader or HR business partner.
• Experience in setting up novel systems that scale in a fast-growing company.
• Coaching experience that showcases a skill set in providing confidential support, motivating improvement, and conflict resolution.
• Track record of supporting a high trust and low ego environment for high caliber talent with strong retention and growth.

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 $190,000 - $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.
• 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.

About Tinker

Tinker is our fine-tuning API that empowers researchers and developers to customize frontier AI to their needs — opening access to capabilities that have previously been concentrated in a handful of labs. We manage the infrastructure while allowing Tinkerers full flexibility in training open weights models with their own data, algorithms, and for their own needs. Tinker is rapidly adding new customers, features, and novel use-cases. We’re hiring to grow the platform alongside the Tinker community.

About the Role

We're hiring a GTM Strategy & Operations lead to build the commercial engine for Tinker. You will craft the strategies, processes, and playbooks that turn organic adoption into a scalable, repeatable revenue engine. You'll drive how we package, price, launch, and sell fine-tuning capabilities across segments, working in close partnership with product, engineering, and research. 

Tinker is a technical product sold to sophisticated buyers. The GTM lead needs to be credible in conversations about training infrastructure and fluent in how developers evaluate and adopt tools.

What You’ll Do

• Design and execute commercialization strategies — pricing, packaging, launch sequencing — based on market and competitive research.
• Develop Tinker's go-to-market motions across customer profiles.
• Manage partnerships to expand Tinker's distribution and unlock new demand channels.
• Design customer pilots, onboarding flows, and expansion playbooks that move accounts from experimentation to production spend.
• Build commercial playbooks that enable Tinker's customer-facing engineers and FDEs to position and sell the platform effectively.
• Define and track success metrics for launches and GTM initiatives, running experiments to validate pricing and packaging hypotheses.

Skills and Qualifications

Minimum qualifications:

• Track record of executing GTM strategy, product commercialization, or business operations at a technology company.
• Experience selling a highly technical product to technical buyers while building trust and credibility.
• Proven ability to take strong product-market fit and build the commercial scaffolding around it — segmentation, pricing, packaging, expansion motions.
• Proficiency with analytics: financial models, metrics frameworks, and using data to drive commercialization decisions.

Preferred qualifications — we encourage you to apply if you meet some but not all of these:

• Experience at a developer infrastructure, API, or ML platform company.
• Experience with ecosystem partnerships.
• Technical fluency sufficient to engage credibly with ML engineers and researchers on fine-tuning workflows, model customization, and training infrastructure tradeoffs.
• Direct experience with new product introductions or other 0-to-1 commercial programs.
• Familiarity with the AI ecosystem — who's building what, where adoption energy is moving, and how the competitive landscape for fine-tuning is evolving.
• Experience building GTM processes and playbooks that scaled beyond yourself into a team.

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 $175,000 - $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.

About the Role

We're hiring an Engineering Manager to lead a team of senior and staff-level engineers across ML infrastructure and product. You will help the team build and scale systems that are reliable, performant, and easy to operate.

This role combines collaboration with hand-on work. You’ll partner with tech leads to set the technical direction for your team and own its execution. You should also be ready to go deep on system design and contribute directly when needed.

What You’ll Do

• Lead and grow a team of senior and staff-level engineers, setting clear expectations and maintaining a high bar for execution.
• Own architecture, system design, and long-term technical direction for your team's systems, with emphasis on reliability and performance.
• Contribute directly to design reviews, prototyping, and debugging critical issues.
• Partner with researchers and product teams to define roadmaps and prioritize work.
• Hire and close senior engineering talent. Mentor engineers into technical leaders.

Skills and Qualifications

Minimum qualifications:

• Bachelor’s degree or equivalent industry experience in computer science, engineering, or similar.
• 8+ years of experience building and scaling production systems, including system design and distributed systems.
• 3+ years of engineering management experience in high-growth environments.

Preferred qualifications — we encourage you to apply even if you don’t meet all preferred qualifications, but at least some:

• Experience managing teams of senior or staff-level engineers.
• Background in infrastructure, systems engineering, or developer productivity.
• Familiarity with AI/ML systems, data infrastructure, or high-performance computing.
• Track record of building or contributing to widely used systems, platforms, or tools.

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 $400,000 - $500,000 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.

About the Role

We're hiring an Executive Business Partner to support several technical leaders out of our San Francisco office. You will help our team stay focused and organized, managing personal logistics and any tasks that might fall through the cracks. 

This is a non-traditional EA role, requiring creativity in adapting to different people’s work styles and the new challenges that emerge at a fast-moving startup. The role entails real autonomy in making decisions without tight supervision.

What You’ll Do

• Manage calendars, schedule meetings, and coordinate travel for 3-4 technical leaders
• Serve as the primary point of contact between your supported leaders and the rest of the company
• Support recruiting coordination efforts
• Track projects and commitments so nothing falls through the cracks

Skills and Qualifications

Minimum qualifications:

• 4+ years of executive or administrative support experience, ideally in AI labs or tech startups
• Track record of supporting technical leaders such as engineers or researchers

Preferred qualifications:

• Track record supporting a diverse team, adapting assistance and communication styles to individual needs
• Experience managing a satellite office and coordinating across time zones
• Experience adapting to a fast-changing role and meeting challenges proactively
• Proven professionalism and discretion with sensitive or personal information
• Having followed an executive to a new company

Logistics

• 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 $200,000 - $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.

About the Role

We’re hiring a designer to define how people experience our artificial intelligence. You will own aspects of the whole stack, from the interface used by our customers and developers to the model behavior itself. Along with our researchers and engineers, you will co-design AI model experiences that feel intuitive, empowering, and crafted with care.

You will need to combine your design instincts with an understanding of how cutting-edge AI is trained and evaluated. This role requires exceptional taste, conceptual thinking, and the technical skills to ship your work in code. 

What You’ll Do

• Design end-to-end product experiences from early concepts and high-fidelity prototypes through high-fidelity UI and implementation in code.
• Contribute to our design system that helps a small team move fast without sacrificing craft.
• Participate in model training and evaluation to guide model behavior with AI researchers.
• Contribute to brand, storytelling, demos, and external-facing materials as needed.

Skills and Qualifications

Minimum qualifications:

• Portfolio demonstrating exceptional visual craft and strong interaction design.
• Codes and ships their own work — not mockups, but live prototypes and real features.
• Track record of taking ownership and initiative in ambiguous and dynamic situations.
• Interest in AI development: evals, post-training, model behavior.

Preferred qualifications — we encourage you to apply if you meet some but not all of these:

• Experience with production frameworks (React, Swift, or similar).
• Track record of using AI tools and agents to accelerate your design and development workflows.
• Prior design work on AI products or other novel interaction paradigms where established design patterns don't exist yet.
• Familiarity with machine learning concepts.
• Experience contributing to or building design systems and component libraries.

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.

About the Role

As data partnerships lead at Thinking Machines Lab, you'll own the end-to-end pipeline of data procurement for frontier model training, from understanding what our research teams need, to sourcing and closing providers, to managing the quality and delivery of data. You will be the connective tissue between research, legal, and external vendors, making sure the right data reaches the right teams at the right time.

This role is ideal for a technical-leaning person who wants to get deep into the data world in service of an ambitious research agenda. You'll need to be comfortable context-switching between planning out the data needed for training runs and negotiating pricing with data vendors. Over time, you'll build the repeatable, scalable processes that allow our data operations to keep pace with the speed of our research.

What You'll Do

• Drive and coordinate data procurement initiatives end-to-end, ensuring that complex sourcing efforts are executed efficiently, transparently, and with scientific rigor.
• Partner closely with research teams to proactively understand data needs across pre-training, post-training, and evaluation workstreams, staying ahead of the roadmap rather than reacting to requests.
• Source, evaluate, and onboard data providers, building and maintaining a pipeline of potential vendors across domains.
• Negotiate pricing, licensing terms, and contract structures with data providers, working alongside legal to close agreements that serve our research goals.
• Evaluate incoming data alongside researchers, assessing quality and coverage for intended training objectives.
• Monitor and manage ongoing data deliveries, including tracking schedules, flagging issues, and ensuring what we receive matches what was agreed upon.
• Build repeatable, scalable processes around the full data procurement lifecycle so that sourcing new data becomes faster and more systematic over time.
• Translate technical data requirements into actionable plans with clear milestones, keeping teams aligned across research, infrastructure, legal, and external partners.
• Synthesize and communicate progress across diverse stakeholders, ensuring the right people are informed at the right time and that nothing falls through the cracks.

Skills and Qualifications

Minimum qualifications:

• Degree in computer science, artificial intelligence, mathematics, physics, engineering, or similar.
• Experience in data pipelines, procurement, and operations, or a similar role.

Preferred qualifications — we encourage you to apply if you meet some but not all of these:

• Masters or PhD in computer science, artificial intelligence, mathematics, physics, engineering, or similar.
• Experience working with data at or for frontier AI labs, including sourcing, evaluating, and managing large-scale data pipelines.
• Experience at a frontier or academic research lab, contributing to research in artificial intelligence.
• Familiarity with the data provider landscape and the commercial, legal, and technical considerations involved in data licensing.
• Ability to learn new technical domains quickly and operate comfortably in ambiguity.
• Strong technical communication, written and verbal.

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 $175,000–$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.

About Tinker

Tinker is our fine-tuning API that empowers researchers and developers to customize frontier AI to their needs — opening access to capabilities that have previously been concentrated in a handful of labs. We manage the infrastructure while allowing Tinkerers full flexibility in training open weights models with their own data, algorithms, and for their own needs. Tinker is rapidly adding new customers, features, and novel use-cases. We’re hiring to grow the platform alongside the Tinker community.

About the Role

We're looking for a full stack engineer to build and ship the products and services that Tinker users interact with every day. You'll work across frontend, backend, and infrastructure building the Tinker console, developer tools, and whatever Tinker needs most.

What You’ll Do

• Build and extend Tinker's APIs and backend services in Python and Rust, from job submission and orchestration to billing and usage tracking.
• Ship user-facing product surfaces — console and future tools — with React and TypeScript.
• Improve the developer experience for Tinker users: SDK ergonomics, error messages, API design, onboarding flow.
• Improve system reliability, observability, and security across Tinker's production environment; participate in on-call.
• Build internal tooling that accelerates the Tinker research and infrastructure teams.

Skills and Qualifications

Minimum qualifications:

• Bachelor's degree or equivalent experience in computer science, engineering, or similar.
• Proficiency in at least one backend language (we use Python and Rust).
• Some familiarity with React, TypeScript, or mobile platforms.
• Thrive in a highly collaborative environment involving many, different cross-functional partners and subject matter experts.
• A bias for action with a mindset to take initiative to work across different stacks and different teams where you spot the opportunity to make sure something ships.

Preferred qualifications — we encourage you to apply if you meet some but not all of these:

• 4+ years building backend systems in production.
• Experience designing and maintaining backend APIs at scale, especially for developer-facing platforms.
• Experience building tooling or products for ML training, fine-tuning, or inference workloads.
• Familiarity with distributed training infrastructure, job orchestration, and GPU scheduling.
• Ability to build high-quality, production-level UIs from prototype to polish.
• Experience with developer experience work — SDK design, CLI tooling, API ergonomics, documentation.

Logistics

• Location: This role is based in San Francisco, California or New York, New York. 
• 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.

About Tinker

Tinker is our fine-tuning API that empowers researchers and developers to customize frontier AI to their needs — opening access to capabilities that have previously been concentrated in a handful of labs. We manage the infrastructure while allowing Tinkerers full flexibility in training open weights models with their own data, algorithms, and for their own needs. Tinker is rapidly adding new customers, features, and novel use-cases. We’re hiring to grow the platform alongside the Tinker community.

About the Role

We're looking for a software engineer to own the platform systems that enable Tinker — billing and usage metering, permissions and access control, organizations and teams, data exports, audit logging, and the admin surfaces that tie them together. This role partners with everyone from product to legal, as every new feature, pricing change, and enterprise deal flows through your work.

What You’ll Do

• Design the authorization layer across all products: RBAC, API key scoping, organization hierarchies, and permission boundaries.
• Own billing infrastructure end to end, including usage metering and aggregation, plan management, payment processing, invoicing, and revenue recognition support.
• Build and evolve the organizations and teams model: seat management, SSO/SAML, workspace isolation, and invite flows.
• Implement data export and deletion pipelines that meet enterprise compliance and data residency requirements.
• Build audit logging so customers and internal teams have clear visibility into who did what, when.

Skills and Qualifications

Minimum qualifications:

• Bachelor’s degree or equivalent experience in computer science, engineering, or similar.
• Proficiency in at least one backend language (we use Python or Rust).
• Experience with at least one of: billing/payments infrastructure, identity and access control (RBAC/ABAC, OAuth, SAML), or multi-tenant platform systems.
• Thrive in a highly collaborative environment involving many, different cross-functional partners and subject matter experts.
• A bias for action with a mindset to take initiative to work across different stacks and different teams where you spot the opportunity to make sure something ships.

Preferred qualifications — we encourage you to apply if you meet some but not all of these:

• 4+ years building backend systems in production.
• Experience building billing or metering systems at scale (Metronome pipelines, usage-based pricing, invoicing pipelines).
• Familiarity with enterprise-readiness patterns: SSO/SCIM provisioning, audit trails, data residency, custom contracts, role hierarchies.
• Strong opinions on building correct financial systems — idempotency, exactly-once semantics, reconciliation.
• Experience with event-driven architectures or stream processing for usage metering.
• Background in security engineering or a strong security mindset (least-privilege design, secure defaults, threat modeling).
• Prior work at an AI/ML company, developer tools company, or API-first platform.

Logistics

• Location: This role is based in San Francisco, California or New York, New York. 
• 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.

About the Role

We’re looking for a full stack engineer to build and ship products from prototype to scale and to maintain tools that accelerate research and product teams. You’ll work across frontend and backend components, and contribute to reliability, observability, and security in production.

Note: This is an "evergreen role" that we keep open on an on-going basis to express interest. 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

• Prototype and build new APIs and product backends in Python and Rust.
• Launch new products and UX with React and TypeScript where needed.
• Improve developer experience for local dev, deployment, testing, and iteration speed.
• Improve system reliability, observability, and security across production environments; participate in on‑call.

Skills and Qualifications

Minimum qualifications:

• Bachelor’s degree or equivalent experience in computer science, engineering, or similar.
• Proficiency in at least one backend language (we use Python or Rust).
• Some familiarity with ReactJS, TypeScript or mobile platforms.
• Comfort operating across the stack and owning projects end-to-end.
• Thrive in a highly collaborative environment involving many, different cross-functional partners and subject matter experts.
• A bias for action with a mindset to take initiative to work across different stacks and different teams where you spot the opportunity to make sure something ships.

Preferred qualifications — we encourage you to apply if you meet some but not all of these:

• Experience designing and maintaining backend APIs at scale.
• Experience building tooling or products for LLMs or other systems that scale to a large number of users.
• Ability to build high‑quality, production-level UIs from prototype to polish.
• Familiarity with NodeJS, Python, and/or Rust.
• Experience building AI products or other products that scale to a large number of users.

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.

About the Role

We’re looking for a software engineer focused on making our products secure by default while supporting fast and ambitious product iteration. You’ll embed with product and research teams to bake security into design and development and to build tooling and automation that keep systems safe at scale.

Note: This is an "evergreen role" that we keep open on an on-going basis to express interest. 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

• Partner with product and research teams to embed security into the development lifecycle: threat modeling, design reviews, and secure defaults for new features.
• Design and implement security controls across our product stack (authentication, authorization, session management, input validation, etc.).
• Build and maintain security tooling and automation for engineers: secure frameworks and templates, CI/CD checks, dependency management, and vulnerability detection.
• Collaborate with researchers to identify and mitigate AI-specific product risks, such as model abuse, prompt injection, data leakage, or misuse of capabilities.
• Improve observability and detection for security-relevant events: access anomalies, abuse patterns, and suspicious behavior in production.

Skills and Qualifications

Minimum qualifications:

• Bachelor’s degree or equivalent experience in computer science, engineering, or similar.
• Proficiency in at least one backend language (we use Python or Rust).
• Strong generalist software engineering background and ability to review production code for security risks.
• Hands-on experience securing web apps and APIs especially auth flows, access control, secrets management, input validation, and data protection.
• Familiarity with common vulnerability classes and prevention frameworks; experience hardening prototypes into production.
• Comfort with modern cloud infrastructure and understanding how application concerns intersect with infrastructure.
• Comfort operating across the stack and owning projects end-to-end.
• Thrive in a highly collaborative environment involving many, different cross-functional partners and subject matter experts.
• A bias for action with a mindset to take initiative to work across different stacks and different teams where you spot the opportunity to make sure something ships.

Preferred qualifications — we encourage you to apply if you meet some but not all of these:

• Experience securing AI‑powered products or working with ML/LLM APIs and their unique threat models.
• Background in human-computer interaction, especially where security or trust plays a central role in the user experience.
• Strong skills in rapid prototyping and iteration, with a habit of turning ad-hoc fixes into reusable patterns and tools.
• Open‑source security work, bug bounty write‑ups, or published tooling.

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.

About the Role

We’re looking for an engineer to design, build, and operate the GPU supercomputing environment that powers large‑scale training and inference. You will deliver high‑performant, reliable, and cost‑efficient compute so our users and researchers can move fast at scale.

Note: This is an "evergreen role" that we keep open on an on-going basis to express interest. 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

• Operate and automate large GPU clusters including provisioning, imaging, and capacity planning.
• Write software that abstracts cluster management and presents a unified interface for training and inference.
• Extend scheduling/orchestration (Kubernetes, Slurm, or similar) for topology‑aware placement, preemption, quotas, and fair‑share multi‑tenancy.
• Monitor and improve operational metrics of speed, reliability, and error recovery.
• Build reliable storage and artifact paths for datasets, checkpoints, and logs with clear retention and lineage.
• Partner with researchers to unblock scale runs and advise on parallelism and performance trade‑offs.

Skills and Qualifications

Minimum qualifications:

• Bachelor’s degree or equivalent experience in computer science, engineering, or similar.
• Proficiency in at least one backend language (we use Python or Rust).
• Experience operating large‑scale clusters and container orchestration systems (e.g. Kubernetes or Slurm).
• Comfort operating across the stack and owning projects end-to-end.
• Thrive in a highly collaborative environment involving many, different cross-functional partners and subject matter experts.
• A bias for action with a mindset to take initiative to work across different stacks and different teams where you spot the opportunity to make sure something ships.

Preferred qualifications — we encourage you to apply if you meet some but not all of these:

• Strong systems background: Linux, networking, and infrastructure‑as‑code.
• Familiarity with CUDA/NCCL and performance profiling for distributed training/inference.
• Prior work supporting large‑scale model training or inference environments.
• Understanding of deep learning frameworks (e.g., PyTorch, TensorFlow, JAX) and their underlying system architectures.
• Track record of working in fast-paced environments balancing care with urgency.

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.

About the Role

We’re looking for generalist infrastructure and systems engineers to help build the systems that power our foundation models and the internal teams on research and product development to be able to create the models and ship the products powered by our models.

You'll join a small, high-impact team responsible for architecting and scaling the core infrastructure behind everything we do. You’ll work across the full technical stack, solving complex distributed systems problems and building robust, scalable platforms.

Infrastructure is critical to us: it's the bedrock that enables every breakthrough. You'll work directly with researchers to accelerate experiments, improve infrastructure efficiency, and enable key insights across our models, products, and data assets.

Note: This is an "evergreen role" that we keep open on an on-going basis to express interest. 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

We interview generally, but during project selection we’ll take into account your interests and experience alongside organizational needs. This flexible approach allows us to match talented engineers with the infrastructure teams where they'll have the greatest impact and growth potential.

Here are example areas you may contribute to depending on your area of expertise and interest:

• Core Infrastructure: We support teams that train, research, and ultimately serve AI models and build the underlying infrastructure for the clusters to reliably and safely train frontier models. Examples might include building systems and running large Kubernetes clusters with GPU workloads, or building infrastructure to support Tinker.
• Data Infrastructure: We build and maintain the data systems for our research and products. You'll design and optimize data pipelines using tools like Spark and other modern data infrastructure technologies.  You’ll build scalable, reliable, data infrastructure while embedding governance best practices.
• Developer Productivity: We care deeply about research and engineering productivity and our ability to continue shipping quickly. We build tooling, systems, frameworks, and systems to make sure everyone gets well configured, optimized developer environments.

Skills and Qualifications

Minimum qualifications:

• Bachelor’s degree or equivalent experience in computer science, engineering, or similar.
• Proficiency in at least one backend language (we use Python or Rust).
• Experience operating large‑scale clusters and container orchestration systems (e.g. Kubernetes or Slurm).
• Comfort operating across the stack and owning projects end-to-end.
• Thrive in a highly collaborative environment involving many, different cross-functional partners and subject matter experts.
• A bias for action with a mindset to take initiative to work across different stacks and different teams where you spot the opportunity to make sure something ships.

Preferred qualifications — we encourage you to apply if you meet some but not all of these:

• Strong debugging across application, OS, and network layers.
• Proficiency in Python or Rust (or similar), containers, and modern CI.
• Experience with Kubernetes, controllers/operators, or performance profiling.
• Familiarity with GPU/ML workflows or large‑scale data/eval pipelines.

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.

About the Role

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.

Skills and Qualifications

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.

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 $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.