The mission of Thinking Machines is to build AI that extends human will and judgment. We are training frontier models with Inkling, developing Tinker to let people make models their own, and crafting interfaces that broaden human-AI communication. We believe the future worth building is human, and we're hiring people who want to build it.
About the RoleWe're hiring a Software Engineer, Inference to own the reliability, scale, and efficiency of the systems that serve our models to real users. Our research and inference teams push the limits of model performance and serving efficiency; this role makes sure those gains reach production safely and stay up — powering Tinker's live, multi-tenant serving and the products built on top of our models.
This is a production-facing systems role at the center of the company. You'll be the bridge between cutting-edge inference techniques and the day-to-day reality of serving real traffic: rollouts, capacity, incidents, and everything that keeps a fast-growing platform online.
What You'll DoOperate and scale the production inference systems that serve live traffic, including Tinker's multi-tenant serving platform
Own the rollout process for new models, model versions, and inference optimizations, ensuring safe, incremental deployment to production
Build and improve observability, alerting, and capacity planning so the team can detect, diagnose, and resolve production issues quickly
Partner with inference and research teams to productionize new serving techniques without compromising reliability
Lead incident response for production inference issues, driving root cause analysis and durable fixes
Design for graceful degradation, failover, and redundancy so that serving stays resilient as usage grows
Manage capacity and cost tradeoffs for serving infrastructure as traffic and model sizes scale
Experience operating large-scale, latency-sensitive production systems
Proficiency in Python and Go or another systems language
Experience with observability, monitoring, and incident response for production services
Strong understanding of distributed systems and how they fail at scale
Experience running production inference for large language models or other large-scale ML systems
Experience with deployment and rollout systems, such as canarying, blue/green deploys, or feature flags
Experience with capacity planning and cost optimization for GPU or TPU infrastructure
Familiarity with inference-specific techniques, such as batching, caching, or quantization, and their operational implications
Comfortable being on-call and leading incident response for critical production systems
Comfortable working with high autonomy in a fast-changing, early-stage environment
Location: This role is based in San Francisco, CA.
Compensation: Depending on background, skills and experience, the expected annual salary range for this position is $300,000 - $400,000 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.
Skills Required
- Experience operating large-scale, latency-sensitive production systems
- Proficiency in Python and Go or another systems language
- Experience with observability, monitoring, and incident response for production services
- Strong understanding of distributed systems and how they fail at scale
- Experience running production inference for large language models or other large-scale machine learning systems
- Experience with deployment and rollout systems, such as canarying, blue-green deployments, or feature flags
- Experience with capacity planning and cost optimization for GPU or TPU infrastructure
- Familiarity with inference techniques such as batching, caching, or quantization and their operational implications
- Comfort working on-call and leading incident response for critical production systems
- Comfort working with high autonomy in a fast-changing, early-stage environment
What We Do
Thinking Machines Lab is an artificial intelligence research and product company. We're building a future where everyone has access to the knowledge and tools to make AI work for their unique needs and goals. While AI capabilities have advanced dramatically, key gaps remain. The scientific community's understanding of frontier AI systems lags behind rapidly advancing capabilities. Knowledge of how these systems are trained is concentrated within the top research labs, limiting both the public discourse on AI and people's abilities to use AI effectively. And, despite their potential, these systems remain difficult for people to customize to their specific needs and values. To bridge the gaps, we're building Thinking Machines Lab to make AI systems more widely understood, customizable and generally capable. 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.
