ML Platform & Infrastructure Engineer

Location San Francisco Office Employment Type Full time Location Type On-site Department Engineering 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. Minimum Qualifications 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) Preferred Qualifications 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 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 Perks Competitive company-sponsored medical, dental, and vision insurance ✈️ Top-tier relocation and immigration support How to Apply Send us: A link — or 60‑second video — of something you built and why it matters Your resume or LinkedIn Two sentences on the hardest problem you've cracked Every exceptional candidate hears back within 48 hours. If you see possibility where others see limits, we'd love to meet you. #J-18808-Ljbffr Pantera Capital