San Francisco, CA
AI research lab building customizable multimodal AI systems
Thinking Machines Lab develops AI systems and platforms focused on multimodal models for research and production. They serve researchers, engineers, and enterprises by enabling customizable, collaborative AI through tools like Tinker for fine-tuning. Advances open science and efficiency in large-scale training and inference.
Leads operational trust and safety for human-AI collaboration products by reviewing abuse cases, enforcing policy, and building automation and detection systems. Requires 7+ years of recurring case-queue experience plus expertise in AI abuse risks, policy operations, and production safety incidents.
Supports several technical leaders by managing calendars, travel, communications, recruiting coordination, and commitments. The role requires at least four years of executive or administrative support experience, strong discretion, adaptability, and the ability to operate autonomously in a fast-moving technology environment.
Lead end-to-end certifications (SOC 2, ISO 27001, FedRAMP) and day-to-day GRC processes for an AI company. Drive compliance roadmap, manage audits/risk, answer technical questions from engineering teams, and build automation/tools while growing the function.
Build and operate secure macOS endpoint infrastructure as code using MDM platforms, GitOps, and production engineering practices. Lead migration to Fleet, own Santa/Rudolph, implement Zero Trust device posture, patching, zero-touch provisioning, and telemetry-driven compliance for a large fleet.
Serve as the primary onsite IT partner for the New York office at Thinking Machines Lab, handling employee support, onboarding/offboarding, identity/access management (Okta), infrastructure (networking, physical security, A/V), device fleet management (macOS/MDM), and collaboration with the SF team using IaC practices like Terraform and GitOps. Requires 5+ years IT engineering experience and full-time onsite work in NYC.
Build and manage pipelines of exceptional research, engineering, and infrastructure talent for an AI startup. Identify and engage passive candidates through creative sourcing, personalized outreach, and relationship building while helping establish sourcing processes from the ground up.
Build and standardize AI-powered coding tools, agents, and dev environments to accelerate internal software development while maintaining security and quality. Requires experience with productivity tooling for large codebases, container/CI platforms, and AI model APIs.
Recruiting Coordinator supporting high-volume Research team hiring at Thinking Machines Lab. Own interview scheduling across time zones, candidate communications, process operations, feedback collection, and scaling interviewer pools while delivering high-touch experiences.
Full stack engineer building Tinker's fine-tuning platform. Develop backend APIs and orchestration in Python/Rust, frontend console in React/TypeScript, improve developer experience and system reliability for ML training users.
Build and operate research infrastructure like evaluation frameworks, RL training systems, experiment tracking, and visualization tools. Partner directly with ML researchers to identify bottlenecks, ensure high adoption, and accelerate research velocity. Requires strong software engineering skills and Python/Rust proficiency.
Ensure reliability of large GPU supercomputing clusters by diagnosing hardware/firmware/OS issues, automating monitoring, driving firmware rollouts, and working directly with vendors.
Own and debug multi-thousand-GPU network fabric (RDMA/RoCE, NVLink/NVSwitch) for large-scale AI training and inference. Requires backend language proficiency, large-scale cluster experience, and cross-stack ownership.
Builds and scales data infrastructure for distributed training pipelines, multimodal data catalogs, and petabyte-scale processing systems. Collaborates with researchers using distributed systems like Spark, Ray, Kafka, and cloud data architectures. Requires backend proficiency in Python/Rust and bachelor's in CS/engineering.
Site Reliability Engineer drives end-to-end reliability for AI fine-tuning platform Tinker, including SLOs, monitoring, incident response, and multi-tenant GPU scheduling. Requires distributed systems experience, software proficiency for reliability, and production incident handling.
Conducts research on visual perception, multimodal learning, and large-scale AI model training. Designs architectures, builds datasets and evaluations, and collaborates on frontier models. Requires ML expertise, Python proficiency, and experimental rigor.
Drives large-scale research products and programs in AI, coordinating cross-functional teams to translate technical ideas into scoped plans and integrate research into production systems. Requires CS/AI degree and experience in research or product management, thriving in technical, ambiguous environments.
Conducts research on pre-training methodologies for large AI models, develops new architectures and data strategies, runs large-scale experiments, and publishes findings. Requires strong ML fundamentals, Python proficiency, and experience with deep learning frameworks.
Designs and implements methods for sourcing, curating, and analyzing large-scale pre-training datasets for AI models, blending research with production-grade data engineering. Requires Python proficiency, deep learning frameworks, and strong ML fundamentals.
Conducts post-training research for AI models, designing data collection strategies, developing labeling pipelines, modeling human preferences, and iterating on evaluations to improve model alignment, reasoning, and helpfulness. Requires strong Python skills, ML framework proficiency, and experimental rigor.
Develops and tunes post-training recipes for AI models, iterates on evaluations, debugs configurations, scales methodologies, and publishes research to advance collaborative intelligence. Requires Python proficiency, deep learning frameworks, and strong ML fundamentals.
Designs and optimizes distributed training systems scaling across thousands of GPUs for large AI models. Requires strong systems engineering, PyTorch/JAX expertise, and collaborative mindset to boost research productivity.
Designs and optimizes infrastructure for scalable reinforcement learning training of large models, improving reliability, observability, and throughput. Collaborates with researchers to productionize RL algorithms; requires strong engineering skills and deep learning framework knowledge.
Designs and optimizes distributed training infrastructure for large-scale LLMs, focusing on low-precision numerics, kernel optimizations, and communication frameworks to enable stable, scalable trillion-parameter model training. Requires strong systems engineering, deep learning frameworks knowledge, and collaborative research mindset.
Designs and optimizes high-performance ML kernels (CUDA, CuTe, Triton) for large-scale LLM training, focusing on GPU efficiency, low-precision formats, and distributed compute. Collaborates with researchers to bridge algorithms and hardware.
Designs, optimizes, and scales infrastructure for high-performance AI model inference, focusing on latency, throughput, efficiency, and reliability. Collaborates with researchers to enable production deployment of large-scale models using deep learning frameworks and distributed systems.
Conducts research to advance audio capabilities in AI models, designing and training large-scale multimodal systems, building audio data pipelines, and publishing findings. Requires ML expertise, Python proficiency, and experience with deep learning frameworks.
Owns and evolves security infrastructure across compute, storage, networking, and data platforms for foundation models. Architects secure patterns, manages identities/secrets, builds threat models, and automates security checks in Kubernetes/cloud environments. Requires strong systems programming and infra experience.
Coaches managers on leadership, performance, and team dynamics while designing scalable people systems including compensation, career frameworks, and feedback processes for a high-growth AI research lab.
Leads a team of senior/staff engineers building scalable ML infrastructure and products, owning system design, reliability, and execution while contributing hands-on and hiring top talent. Requires 8+ years in production systems and 3+ years managing engineers.
Research Engineer focused on helping developers use a fine-tuning platform through cookbook recipes, libraries, integrations, demos, and technical guidance. The role requires hands-on experience with LLM fine-tuning, software library development, and communicating technical concepts to users.
Builds platform systems for AI fine-tuning API including billing, metering, authorization (RBAC/OAuth), organizations/teams, data exports, and audit logging. Requires backend proficiency in Python/Rust and experience in billing, access control, or multi-tenant systems; bachelor's or equivalent.
Builds and scales full stack products including APIs in Python/Rust and UIs in React/TypeScript. Improves dev tools, reliability, observability, and security while owning end-to-end projects in a collaborative AI research environment. Requires bachelor's in CS or equivalent and full stack proficiency.
Embeds security into product development by partnering with teams on threat modeling, implementing controls like auth and input validation, building automation tools, and mitigating AI-specific risks in a collaborative environment.
Designs, builds, and operates GPU supercomputing environments for large-scale AI training and inference. Automates cluster management, extends orchestration systems, and optimizes performance metrics in collaboration with researchers.
Builds and scales core infrastructure for AI model training, data systems, and developer tools in a high-impact team. Requires backend proficiency (Python/Rust), experience with large-scale clusters like Kubernetes, and end-to-end project ownership.