ML Platform & Infrastructure Engineer

AGI•San Francisco, CA

1d•Onsite

About The Position

Think Different. Build the Future. 🚀 Our Mission Build everyday AGI. Trustworthy, consumer-grade agents that redefine human–AI collaboration for millions. Software shouldn’t wait for commands; it should partner with you, amplifying what you can do every single day. Why AGI, Inc. We’re a stealth team of elite founders and AI researchers, with backgrounds spanning Stanford, OpenAI, and DeepMind . We’re industry leaders in mobile and computer-use agents, bringing these capabilities to consumer scale. Grounded in years of agent research, our AI is designed with trustworthiness and reliability as core pillars, not afterthoughts. We are supported by tier-1 investors who funded the first generation of AI giants; now they’re backing us to build the next: everyday AGI. (Watch the demo ) If you see possibility where others see limits, read on. What You’ll Do Training Automation: Design and implement robust CI/CD pipelines for machine learning workflows. Automate nightly and on-demand training runs, including data ingestion, job orchestration, checkpointing, and artifact management, with reliability as a first-class requirement. Evaluation Infrastructure: Build scalable evaluation harnesses that automatically benchmark models on every merge. Optimize latency and resource usage so experimentation stays fast, and performance regressions are caught immediately. Research Tooling: Develop internal SDKs, CLIs, and lightweight UIs (e.g., Streamlit, Retool) that empower researchers to: Inspect trajectories and traces Visualize model failures Curate and manage datasets Iterate without friction You’ll make experimentation ergonomic. Observability & Performance: Implement comprehensive tracking for: Model latency, throughput, and error rates GPU utilization and cluster health Inference cost and unit economics Build dashboards and alerting systems that give real-time visibility into system performance and reliability. Why This Role Matters Great research without great infrastructure slows to a crawl. Great infrastructure multiplies the impact of every researcher. You will define how experiments scale, how reliability is measured, and how quickly we can ship improvements to real users. The systems you build will directly shape the speed and quality of our progress toward everyday AGI. Our Culture 🏢 All in, in person — work moves faster face-to-face 🚀 Ship by default — novel and polished can coexist, speed is the feature 🤝 One band, one sound — radical candor, zero politics, help each other win

Requirements

Bachelor’s degree in Computer Science, Engineering, or equivalent practical experience
3+ years in Software Engineering, MLOps, or ML Infrastructure
Strong Python proficiency
Experience building internal developer tools, CLIs, or dashboards
Experience with cloud infrastructure (AWS or GCP) and containerization (Docker, Kubernetes)

Nice To Haves

Experience designing CI/CD pipelines specifically for ML workflows
Familiarity with LLM serving stacks such as vLLM or TGI
Experience managing GPU clusters and optimizing distributed workloads

Responsibilities

Design and implement robust CI/CD pipelines for machine learning workflows.
Automate nightly and on-demand training runs, including data ingestion, job orchestration, checkpointing, and artifact management, with reliability as a first-class requirement.
Build scalable evaluation harnesses that automatically benchmark models on every merge.
Optimize latency and resource usage so experimentation stays fast, and performance regressions are caught immediately.
Develop internal SDKs, CLIs, and lightweight UIs (e.g., Streamlit, Retool)
Inspect trajectories and traces
Visualize model failures
Curate and manage datasets
Iterate without friction
Implement comprehensive tracking for: Model latency, throughput, and error rates GPU utilization and cluster health Inference cost and unit economics
Build dashboards and alerting systems that give real-time visibility into system performance and reliability.