ML Platform & Infrastructure Engineer

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.

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.

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.

• 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)

• 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

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.

🏢 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

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.