Thinking Machines Lab is hiring an Assistant Controller to own core accounting functions and financial operations during a period of exceptional growth. The Assistant Controller will report to the Controller and lead month-end close, financial reporting, internal controls, audit readiness, and accounting policy.
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\nWe're looking for an attorney to lead the corporate function at Thinking Machines Lab. This role will report to the General Counsel.
\nYou'll own two interconnected bodies of work. On the corporate side, you'll be the company's primary lawyer for equity and financing transactions, corporate governance, cap table management, board and investor matters, and subsidiary maintenance. On the commercial side, you'll be the person who turns early-stage deal flow into a scalable contracting function, supporting customer agreements, strategic partnerships, vendor relationships, licensing, data agreements, and research collaborations.
\nThis is a hands-on role for someone who likes being close to the business and building the systems that help it move. You'll work directly with Finance, People, the executive team, and the board on corporate matters, and with product, engineering, and research on commercial ones. The work requires strong judgment across both disciplines, comfort with ambiguity, and the ability to translate complex business needs into practical legal terms.
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\nWe're looking for an attorney to own privacy strategy and advise on advanced AI development and products for Thinking Machines Lab.
\nYou'll work directly with teams across research, engineering, product, security, safety, and privacy to build the programs and address the issues that arise when building and operating advanced AI systems. You'll develop deep knowledge of our technical stack, and shape proactive legal strategy for products and initiatives that don't fit neatly into existing frameworks.
\nThis is a high-autonomy role with significant ownership. You'll build legal frameworks that help Thinking Machines move quickly, protect users and partners, and develop AI systems in a way that reflects our mission.
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\nWe're seeking an infrastructure research engineer to design and build scalable, efficient training systems for large models. As a key member of our team, you'll take ownership of the training stack end-to-end, ensuring every GPU cycle drives scientific progress. Your goal is to make experimentation and training at Thinking Machines fast and reliable, allowing our research teams to focus on science, not system bottlenecks.
\nKey responsibilities include designing, implementing, and optimizing distributed training systems, developing high-performance optimizations, and establishing standards for reliability, maintainability, and security. You'll collaborate with researchers and engineers to build scalable infrastructure and publish learnings through internal documentation, open-source libraries, or technical reports.
\nWe're looking for someone who blends deep systems and performance expertise with a curiosity for machine learning at scale. A strong understanding of deep learning frameworks, such as PyTorch, and experience working on distributed training for large models are preferred. If you have a track record of improving research productivity through infrastructure design or process improvements, that's a plus.
\nThis role is based in San Francisco, California, and offers a competitive salary range of $350,000 - $475,000 USD per year, depending on background, skills, and experience. We sponsor visas and offer generous health, dental, and vision benefits, unlimited PTO, paid parental leave, and relocation support as needed.
","enriched_at":1776527879640},{"id":"job_7e28478b-c37","title":"Research, Audio Expertise","source_url":"https://job-boards.greenhouse.io/thinkingmachines/jobs/5002212008","location":"San Francisco","job_type":"full-time","experience_level":"senior","work_arrangement":"onsite","category":"Engineering","description":"We're seeking a researcher 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.
\nThis is a highly collaborative role. You'll work closely across pre-training, post-training, and product with world-class researchers, infrastructure engineers, and designers.
\nAs a researcher in this role, you'll be expected to:
\nShare code, datasets, and insights that accelerate progress across industry and academia.
\nThis 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.
\nIt'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.
","enriched_at":1776527849075},{"id":"job_0a2ea62c-943","title":"Research Engineer, Infrastructure, RL Systems","source_url":"https://job-boards.greenhouse.io/thinkingmachines/jobs/5013930008","location":"San Francisco","job_type":"full-time","experience_level":"senior","work_arrangement":"onsite","category":"Engineering","description":"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.
\nThis 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 optimising 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.
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\nWe're looking for someone with strong engineering skills, ability to contribute performant, maintainable code and debug in complex codebases. You should have a good understanding of deep learning frameworks (e.g., PyTorch, JAX) and their underlying system architectures.
\nExperience training or supporting large-scale language models with tens of billions of parameters or more is a plus. Familiarity with monitoring and observability tools (Prometheus, Grafana, OpenTelemetry) is also a plus.
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\nWe'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.
\nThis 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.
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\nPreferred qualifications , we encourage you to apply if you meet some but not all of these:
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\nWe'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.
\nThis 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.
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\nWe're looking for an engineer to join our 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.
\nAs a software engineer on our data infrastructure team, you'll design, build, and operate scalable, fault-tolerant infrastructure for LLM Research: distributed compute, data orchestration, and storage across modalities. You'll develop high-throughput systems for data ingestion, processing, and transformation , including training data catalogs, deduplication, quality checks, and search. You'll also build systems for traceability, reproducibility, and robust quality control at every stage of the data lifecycle.
\nYou'll collaborate with research teams to unlock new features, improve data quality, and accelerate training cycles. You'll implement and maintain monitoring and alerting to support platform reliability and performance.
\nIf 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.
","enriched_at":1776527640309},{"id":"job_4ced2159-802","title":"Research, Vision Expertise","source_url":"https://job-boards.greenhouse.io/thinkingmachines/jobs/5002288008","location":"San Francisco","job_type":"full-time","experience_level":"senior","work_arrangement":"onsite","category":"Engineering","description":"Thinking Machines Lab is seeking a researcher to join their team in San Francisco. The successful candidate will work on advancing the science of visual perception and multimodal learning. They will 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.
\nThe ideal candidate will have expertise in multimodality and experience running large-scale experiments. They will be comfortable contributing to complex engineering systems and have a strong grasp of probability, statistics, and machine learning fundamentals.
\nThis is an evergreen role, meaning that the position is open on an ongoing basis. The company receives many applications, and there may not always be an immediate role that aligns perfectly with the candidate's experience and skills. However, they encourage candidates to apply and continuously review applications.
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\nPreferred qualifications include 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, and a strong grasp of probability, statistics, and ML fundamentals.
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