Jobs at Thinking Machines Lab

Own full-cycle technical recruiting for infrastructure, systems, networking, and site reliability roles. Partner with engineering leaders on role scoping, sourcing strategies, interview design, talent calibration, candidate assessment, and closing. Build proactive outreach campaigns for specialized infrastructure talent, manage candidate experience, track recruiting metrics, and develop scalable recruiting processes and playbooks.

Recruit product managers from individual contributor through leadership levels. Partner with hiring managers to define roles, develop sourcing strategies, assess candidates’ product judgment and technical fluency, design interviews, manage candidate relationships and offers, and provide market insights. The role focuses on recruiting talent across AI products, developer platforms, enterprise software, and consumer experiences.

Own full-cycle recruiting for specialized security roles, partnering with security and engineering leaders to define roles, source candidates, improve interviews, and close hires. Build relationships with security practitioners, assess candidates across security disciplines, advise hiring managers, manage confidential searches, and use pipeline data and market feedback to improve recruiting strategy.

Own end-to-end recruiting across business functions including finance, legal, people, operations, marketing, partnerships, and go-to-market. Partner with senior leaders to define roles, build candidate profiles, develop sourcing strategies, assess candidates, coordinate hiring teams, manage offers and compensation discussions, and improve recruiting practices. The role requires strong sourcing, communication, negotiation, prioritization, discretion, and the ability to learn new functions quickly.

Own and build the revenue operations function, including go-to-market systems, CRM governance, deal desk, forecasting, compensation, analytics, quote-to-cash processes, billing integrations, and workflow automation. Partner with GTM, Finance, Legal, and Product teams to operationalize pricing and products. Initially execute hands-on as the first revenue operations hire, then build and lead the team as the company scales.

Develop and evolve Thinking Machines’ visual brand across websites, launches, publications, and physical objects. Design, build, and improve digital experiences using code, while creating reusable tools, artwork generators, data-driven charts, and publishing components. Collaborate with illustrators, animators, graphic designers, engineers, and researchers to turn concepts into polished, accessible work. Contribute original ideas, manage open-ended projects, incorporate feedback, and explore agentic workflows and emerging technology.

Coordinate high-volume interviews across time zones, manage candidate communications and logistics, monitor interviewer capacity, and support recruiting operations. Maintain interview feedback, documentation, data integrity, referrals, events, and applications. Partner with recruiting teams to expand interviewer pools and improve scheduling efficiency using recruiting and communication tools while delivering a high-touch candidate experience.

Build and operate privacy and data governance systems covering data classification, retention, deletion, access controls, customer-data isolation, policy enforcement, and auditing. Conduct privacy reviews and threat models for AI/ML data pipelines and products, translate GDPR, CCPA, and contractual obligations into code, and develop shared privacy guardrails. Collaborate with researchers and product engineers to resolve complex privacy tradeoffs involving training data, fine-tuning, inference, deletion, de-identification, and differential privacy.

Leads end-to-end incident response, including investigation, containment, recovery, and postmortems. Builds security telemetry pipelines and high-fidelity detections across cloud, Kubernetes, identity, endpoint, SaaS, and application environments. Develops threat intelligence, threat hunting, automated response tooling, incident plans, and tabletop exercises. Partners with infrastructure, research, product, legal, and leadership teams to address sophisticated threats targeting AI labs, including model theft, supply chain compromise, and AI-specific abuse.

Conducts mid-training research and engineering for frontier AI models. Owns training data strategy, synthetic data pipelines, quality filtering, model behaviors, evaluation, training recipes, scaling experiments, and debugging. Collaborates with post-training researchers to improve model knowledge, reasoning, coding, mathematics, and other capabilities.

Leads operational trust and safety for AI products by reviewing flagged content, escalations, and abuse signals; applying enforcement policies; developing safety policy and moderation tooling; improving abuse detection and observability; and partnering with engineering, security, legal, research, and product teams to embed safety protections into AI experiences.

Build security into products and development workflows through threat modeling, secure design reviews, authentication and authorization controls, security tooling, CI/CD checks, vulnerability detection, and observability. Secure web applications and APIs, collaborate with AI researchers on risks such as prompt injection and data leakage, and improve detection of abuse and suspicious production behavior. The role requires backend software engineering, cloud infrastructure knowledge, and end-to-end project ownership.

Build training systems for Tinker, including reinforcement learning systems, numerics, kernels, data pipelines, and post-training engines. Develop frontier model customization techniques, co-design RL algorithms, debug large-scale training runs, optimize pipelines, and collaborate with internal researchers, companies, and open-science partners to improve platform performance and adoption.

Build and improve Tinker, a fine-tuning API, by developing production-quality Python code, training recipes, integrations, documentation, and developer tools. Work directly with researchers and open source developers to debug technical issues, optimize LLM fine-tuning and deployment workflows, and translate user feedback into product and infrastructure improvements. Share post-training research through tutorials, blogs, talks, demos, and open source contributions while helping shape the developer experience roadmap.

Own full-cycle recruiting for go-to-market roles, including account executives, sales leaders, solutions engineers, partnerships, customer success, and revenue operations. Partner with GTM leadership on role definition, sourcing, assessment, interview design, candidate closing, and hiring processes. Build talent pipelines, evaluate sales capabilities, deliver strong candidate experiences, track recruiting metrics, and improve recruiting tools, workflows, compensation, and leveling guidance.

Own compute allocation for shared supercomputing resources, balancing research demand, workload requirements, hardware suitability, capacity, timing, and dependencies. Partner with researchers and infrastructure engineers to improve scheduling, placement, utilization, and capacity planning. Teach teams to estimate compute needs, explain allocation tradeoffs, develop guidance and tools, and improve decisions through usage and performance analysis.

Operate and scale production inference systems serving live, multi-tenant traffic. Own model rollouts, observability, capacity planning, incident response, reliability, failover, and cost optimization. Partner with research and inference teams to productionize serving techniques while maintaining system performance and resilience.

Design, build, and operate distributed infrastructure for large-scale AI model training and inference. Responsibilities include orchestration, scheduling, storage, resource management, reliability, observability, distributed failure debugging, and developing internal Python and Go libraries and APIs. The role partners with researchers and platform engineers and supports infrastructure operating across thousands of accelerators.

Support 2–3 executives across legal, operations, and communications by managing calendars, meetings, travel, communications, action items, recruiting coordination, company events, and leadership projects. The role requires autonomy, discretion, adaptability, and proactive support in a fast-moving AI startup environment. The position is based in San Francisco and includes coordination across teams and time zones.

Build developer-facing tools, integrations, documentation, demos, and machine learning recipes for Inkling and Tinker. Collaborate directly with open source developers and researchers to debug issues, gather feedback, improve products, and shape the developer experience roadmap. Represent the company through technical writing, talks, tutorials, livestreams, conferences, and meetups. Requires strong Python engineering skills and hands-on experience fine-tuning, evaluating, or deploying large language models.