Senior Machine Learning Engineer
Bjakcareer · United States
About this role
## About ACTAI ACTAI builds proactive applications for people who aren’t AI-native. With over 5 billion users relying on basic apps today (email, notes, tasks, calendar), our mission is to bring intelligence to conversations, errands, organizing, and workflows—**with minimal to no prompting**. We focus on **high reliability for long-running workflows**, **persistent context**, and **real-world task completion**, with the belief that great products can significantly reduce hallucinations. ## Role: Senior Member of Technical Staff, Machine Learning You’ll be an independent owner of **critical ML subsystems in production**. You’ll take ambiguous problems, design practical solutions, and ship systems that operate reliably at scale. **Hands-on, high-impact, depth-focused** role. ### What you’ll do - Build core ML systems powering 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 → repeat** - Collaborate closely with research, product, and engineering to deliver real user impact - Mentor and review other ML engineers through example and technical judgment - Work under real production constraints: **latency, cost, reliability, and safety** ### Tech stack - Python - PyTorch / JAX - GPU-based training and inference systems ### Ideal experience - You’ve built and shipped ML systems used by real users - You understand how modern ML models behave—and misbehave—in production - You write strong, production-quality code and think in systems (not scripts) - You take ownership, work independently, and push work across the finish line - You learn fast, communicate clearly, and improve through iteration ### Outcomes we care about - ML models and systems consistently meet **accuracy, latency, reliability, and efficiency** targets - Complex production issues are monitored, debugged, and resolved with minimal disruption - Training, inference, and data pipelines are robust, scalable, and maintainable - Measurable improvements driven by real-world signals and user feedback - Mentorship and technical guidance that raises the overall ML engineering standard - Cross-functional collaboration so ML features integrate seamlessly into products and meet business goals ## How we work Small, world-class teams with high talent density. Decisions are collective, and we move quickly—balancing **shipping high-quality work** with **learning**. ## Interview process - If there’s a fit, we’ll schedule **3 interviews** (no more than **4**) - Interviews are via virtual meetings and/or onsite - Expect a **prompt decision** for transparency and efficiency If you’ve demonstrated the exceptional skills and mindset we’re looking for, we’ll extend an offer to join us.
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