Independent owner of critical ML subsystems within a proactive AI assistant product, building and shipping machine learning systems in production from design through monitoring.
Responsibilities
- ▹Build core ML systems that power a proactive, long-horizon AI product
- ▹Own work end-to-end: data preparation, training, evaluation, inference, and iteration
- ▹Turn research ideas into working systems that run reliably in production
- ▹Debug model failures and system issues using real production signals
- ▹Iterate quickly: ship, measure outcomes, refine, and repeat
- ▹Collaborate closely with research, product, and engineering to deliver real user impact
- ▹Mentor and review work from other ML engineers through example and technical judgment
- ▹Work under real production constraints: latency, cost, reliability, and safety
Requirements
- ▹Built and shipped ML systems used by real users
- ▹Understanding of how modern ML models behave — and misbehave — in production
- ▹Writes strong, production-quality code and thinks in systems, not scripts
- ▹Takes ownership, works independently, and pushes work across the finish line
- ▹Learns fast, communicates clearly, and improves through iteration
Soft skills
Structure and sound technical judgmentOwnership and independent executionClear communicationCollaboration in a small, high-talent-density teamMentoring ability
About the company
A1's mission is to build a proactive smart assistant for everyday users, bringing intelligence to conversations, errands, organizing, and workflows with minimal prompting. The product focuses on reliability for long-running workflows, persistent context, and real-world task completion, aiming to reduce users' time on daily tasks by about 90%. The team is small, high-talent-density, makes decisions collectively, and moves fast.
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