Thinking Machines Lab

We are seeking a Software Engineer, Inference to be responsible for the reliability, scale, and efficiency of the systems that serve our AI models to users. This production-facing systems role is central to the company, acting as a bridge between cutting-edge inference techniques and the practicalities of serving live traffic, including rollouts, capacity management, and incident response. You will ensure that performance gains from our research and inference teams reach production safely and remain operational, powering our live serving platform and related products.

$300k - $400k

Thinking Machines is building AI that extends human will and judgment, with a focus on training frontier models and developing tools like Tinker, a fine-tuning API that allows researchers and developers to customize AI models. This role is for an engineer focused on developer experience, who will work hands-on with users of Tinker, turning their challenges into product improvements. You will write and update documentation, add library features, prototype integrations, and ensure users can customize models smoothly. This position acts as a bridge between Tinker users and internal teams, surfacing user patterns to guide product and infrastructure priorities, and sharing learnings through various channels. It's a technical-first role with a significant user-facing component, ideal for someone who enjoys coding and engaging with creative individuals.

$300k - $350k

Thinking Machines is building AI that extends human will and judgment, and our Core Services team is responsible for the foundational infrastructure that enables models to run code, use tools, and take actions safely and reliably. This role involves designing, building, and operating a sandboxing platform where models can execute untrusted code and interact with tools. This is critical infrastructure that supports both internal research and external product development, requiring a focus on speed, security, and dependability.

$300k - $400k

Thinking Machines is building AI that extends human will and judgment, focusing on training frontier models, developing tools for model customization, and crafting interfaces for human-AI communication. This role is crucial for shaping the fundamental reasoning capabilities of our models by owning the late-stage training process. It involves a blend of fundamental research and practical engineering, making it ideal for individuals comfortable working at the intersection of pre-training and post-training.

$350k - $475k

At Thinking Machines, we build tools that empower individuals to customize AI models for their specific needs, including the ability to train model weights. In this role, you will focus on frontier customization techniques and contribute to developing a leading post-training engine called Tinker. Your work will involve a comprehensive understanding of Reinforcement Learning (RL) science, and your findings will directly influence Tinker's training defaults, API design, and the open-source Tinker Cookbook. You will collaborate with internal research teams and contribute to open science initiatives for external partners.

$350k - $475k

Thinking Machines is building AI that extends human will and judgment, with a focus on training frontier models and developing tools that allow users to customize AI for their unique needs. This role leads the Fine-tuning Science team, focusing on frontier customization techniques and the Tinker post-training engine. It's a player-coach position where you will remain hands-on with scientific research while also guiding the team, shaping its direction, and ensuring research findings are integrated into Tinker. You will collaborate with internal research teams, contribute to open science, and engage with external users.

$350k - $475k

Thinking Machines is seeking a Site Reliability Engineer (SRE) to ensure the speed, reliability, and ease of iteration for our post-training and reinforcement learning (RL) systems. This role is akin to production engineering or site reliability, focused on model training infrastructure. You will be responsible for the health of training runs, clusters, and pipelines, working closely with research teams to debug issues in real-time, strengthen infrastructure, and develop tools and automation that allow researchers to focus on scientific discovery rather than job management. This position offers significant ownership, requiring you to be the point person for critical issues during active model runs and to implement solutions preventing future occurrences.

$300k - $350k

Thinking Machines is building AI that extends human will and judgment, and we're looking for individuals passionate about creating a human-centric future. In this role, you will design and build an end-to-end evaluation platform crucial for developing frontier AI systems. This platform will empower researchers to easily author, run, and analyze model evaluations at scale, supporting both standardized benchmarks and internal research needs. You will collaborate closely with various teams to enhance how we evaluate models and translate results into research decisions.

$300k - $350k

Thinking Machines is building AI to extend human will and judgment, developing frontier models and crafting interfaces for human-AI communication. We are seeking a Software Engineer to join our full-stack team, which works closely with researchers to build systems that accelerate research and engineering velocity. You will own key parts of our internal platform, including evaluation and training libraries, experiment-tracking systems, and visualization tools. This role involves identifying researcher bottlenecks and transforming them into reliable, generalizable systems, requiring strong technical judgment, cross-functional collaboration, and product intuition. Success means building systems that researchers trust, rely on daily, and find delightful to use.

$300k - $350k

This role is responsible for advancing the agentic capabilities of our models, with ownership spanning the full development cycle. The research team is small, and the role carries a corresponding degree of autonomy and responsibility. We are looking for individuals to build AI that extends human will and judgment, developing frontier models and crafting interfaces that broaden human-AI communication.

$350k - $475k

The Coding Agents team at Thinking Machines is dedicated to enhancing our AI models' capabilities in agentic coding. This involves developing expertise in writing, debugging, and reasoning about code across complex, multi-turn tasks. As a member of this small, high-leverage team, you will be instrumental in defining the recipes, data, and infrastructure that drive coding proficiency improvements in every model release. You will own the entire post-training stack for coding capabilities, encompassing synthetic and human data generation, RL environments, reward design, and large-scale training runs. This research-focused role offers significant ownership, allowing you to shape the technical direction of our coding agent development.

$350k - $475k

Thinking Machines is seeking a researcher to develop reliable model evaluations for research signals. This role involves creating, assessing usability, auditing, and improving the efficiency of evaluations. You will collaborate closely with researchers and engineers across post-training and the wider research organization, with opportunities to focus on specific areas or work across multiple problems based on your interests and experience.

$350k - $475k

Thinking Machines is building AI that extends human will and judgment, focusing on training frontier models, enabling user customization, and crafting interfaces for human-AI communication. As a safety researcher, you will ensure our models are safe and trustworthy by investigating how models handle harmful requests, what they learn from data, and how training influences their behavior. This role involves both research and hands-on technical work, exploring the science behind model behaviors and designing experiments to guide training and evaluation processes.

$350k - $475k

Thinking Machines is seeking a researcher to scale reinforcement learning for frontier models. This role focuses on the intersection of training recipes and infrastructure, particularly for high-training-compute, long-horizon RL. A key area of interest is asynchronous RL, requiring a deep understanding of its algorithms, design choices, and trade-offs on both ML and systems sides. The position also emphasizes the importance of inference systems, low-precision numerics, and quantization, as these significantly impact RL scaling and cost. This is a research role with full-stack ownership, from algorithms to training run health.

$350k - $475k

The role of post-training researchers sits at the core of our roadmap, serving as the critical bridge between raw model intelligence and a system that is actually useful, safe, and collaborative for humans. This position blends fundamental research and practical engineering, requiring proficiency in writing high-performance code and reading technical reports. It's an excellent fit for individuals who enjoy both deep theoretical exploration and hands-on experimentation, and who wish to shape the foundations of how AI learns. This is an evergreen role, kept open to express interest in this research area, with applications continuously reviewed for emerging opportunities.

$350k - $475k

Thinking Machines is seeking an infrastructure research engineer to design, optimize, and scale the systems that power large AI models. The goal is to make inference faster, more cost-effective, more reliable, and more reproducible, enabling teams to focus on advancing model capabilities. This role is crucial for ensuring that every experiment, evaluation, and deployment runs smoothly at scale, with a focus on performant and efficient model inference for both real-world applications and research acceleration.

$350k - $475k

The role of pre-training researchers is central to our roadmap, blending research with large-scale data engineering to assemble pre-training datasets and data systems for the next generation of AI models. You will design and implement methods for sourcing, curating, and analyzing pre-training data for quality and performance, working with automated pipelines and human-in-the-loop processes. This position requires both scientific insight and production-grade code, ideal for someone who thrives at the intersection of data, machine learning, and systems, and is excited by the challenge of shaping frontier AI. The role encompasses fundamental research and practical engineering, expecting candidates to write high-performance code and read technical reports, appealing to those who enjoy deep theoretical exploration and hands-on experimentation.

$350k - $475k

Thinking Machines is seeking researchers to advance the frontier of audio capabilities within AI. This role focuses on exploring how audio models can enable more natural and efficient communication and collaboration, preserving more information and capturing user intent. It's a highly collaborative position, working with researchers, engineers, and designers to shape the fundamental capabilities of AI systems. The role blends fundamental research with practical engineering, requiring proficiency in writing high-performance code and analyzing technical reports. It's ideal for individuals who enjoy both deep theoretical exploration and hands-on experimentation.

$350k - $475k

Thinking Machines is seeking an infrastructure research engineer to design and build the core systems that enable efficient large-scale model training, with a particular focus on numerics. This role involves improving the numerical foundations of their distributed training stack, from precision formats and kernel optimizations to communication frameworks, ensuring the stability, scalability, and speed of training trillion-parameter models. The ideal candidate will excel at the intersection of research and systems engineering, possessing a strong understanding of both optimization mathematics and the practicalities of distributed compute.

$350k - $475k

Thinking Machines is seeking individuals to advance the science of visual perception and multimodal learning, focusing on the interaction between vision and language at scale. The role involves designing architectures that fuse pixels and text, building datasets and evaluation methods for real-world comprehension, and developing representations that ground abstract concepts in the physical world. This position blends fundamental research with practical engineering, requiring high-performance code and the ability to conduct deep theoretical exploration alongside hands-on experimentation to shape the foundations of AI learning.

$350k - $475k