Thinking Machines Lab
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Research Engineer, Infrastructure, Training Systems
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Thinking Machines Lab's mission is to empower humanity through advancing collaborative general intelligence. We're building a future where everyone has access to the knowledge and tools to make AI work for their unique needs and goals.
We are scientists, engineers, and builders who’ve created some of the most widely used AI products, including ChatGPT and Character.ai, open-weights models like Mistral, as well as popular open source projects like PyTorch, OpenAI Gym, Fairseq, and Segment Anything.
About the Role
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.
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.
Ready to apply?
Apply to Thinking Machines Lab
Research Engineer, Infrastructure, RL Systems
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Thinking Machines Lab's mission is to empower humanity through advancing collaborative general intelligence. We're building a future where everyone has access to the knowledge and tools to make AI work for their unique needs and goals.
We are scientists, engineers, and builders who’ve created some of the most widely used AI products, including ChatGPT and Character.ai, open-weights models like Mistral, as well as popular open source projects like PyTorch, OpenAI Gym, Fairseq, and Segment Anything.
About the Role
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.
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.
Ready to apply?
Apply to Thinking Machines Lab
Research Engineer, Infrastructure, Numerics
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Thinking Machines Lab's mission is to empower humanity through advancing collaborative general intelligence. We're building a future where everyone has access to the knowledge and tools to make AI work for their unique needs and goals.
We are scientists, engineers, and builders who’ve created some of the most widely used AI products, including ChatGPT and Character.ai, open-weights models like Mistral, as well as popular open source projects like PyTorch, OpenAI Gym, Fairseq, and Segment Anything.
About the Role
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.
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.
Ready to apply?
Apply to Thinking Machines Lab
Research Engineer, Infrastructure, Kernels
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Thinking Machines Lab's mission is to empower humanity through advancing collaborative general intelligence. We're building a future where everyone has access to the knowledge and tools to make AI work for their unique needs and goals.
We are scientists, engineers, and builders who’ve created some of the most widely used AI products, including ChatGPT and Character.ai, open-weights models like Mistral, as well as popular open source projects like PyTorch, OpenAI Gym, Fairseq, and Segment Anything.
About the Role
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.
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.
Ready to apply?
Apply to Thinking Machines Lab
Research Engineer, Infrastructure, Inference
Share this job
Thinking Machines Lab's mission is to empower humanity through advancing collaborative general intelligence. We're building a future where everyone has access to the knowledge and tools to make AI work for their unique needs and goals.
We are scientists, engineers, and builders who’ve created some of the most widely used AI products, including ChatGPT and Character.ai, open-weights models like Mistral, as well as popular open source projects like PyTorch, OpenAI Gym, Fairseq, and Segment Anything.
About the Role
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.
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.
Ready to apply?
Apply to Thinking Machines Lab