Machine Learning Engineer

We’re hiring on behalf of A1, a high‑talent team building the next generation of AI‑native productivity applications. Their mission is to replace repetitive digital work with AI that can reliably complete real tasks for everyday users.

Rather than building another chatbot, A1 is creating long‑running AI workflows that manage conversations, coordinate actions, maintain context, and interact with external services — all with minimal user input.

As a Machine Learning Engineer, you will own critical ML subsystems in production. This is a hands‑on, high‑impact role focused on depth and reliability at scale.

• Build core ML systems powering a proactive, long‑horizon AI product.

• Own the full lifecycle: data preparation, training, evaluation, inference, iteration.

• Turn research ideas into production systems that run reliably.

• Debug model failures and system issues using real production signals.

• Ship quickly, measure outcomes, refine, and repeat.

• Collaborate closely with research, product, and engineering teams.

• Mentor and review work from other ML engineers.

• Work under real production constraints: latency, cost, reliability, safety.

full-time

remote

Our client A1 is a small, world‑class team with high talent density. They move quickly, make decisions collectively, and balance shipping high‑quality work with rapid learning. Structure, sound judgment, and the ability to execute independently are highly valued.

Machine Learning

• 3–4 interviews with technical team members.

• Conducted virtually and/or onsite.

• Transparent and efficient decision process.

• Successful candidates will receive an offer to join a team building AI that delivers practical benefits to billions of users globally.

• Python

• PyTorch / JAX

• GPU‑based training and inference systems

• Experience building and shipping ML systems used by real users.

• Strong understanding of how modern ML models behave — and misbehave — in production.

• Ability to write production‑quality code and think in systems, not scripts.

• Independent ownership: driving work across the finish line.

• Fast learner, clear communicator, iterative mindset.

• 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 in ML systems based on real‑world signals and user feedback.

• Technical guidance and mentorship that raises the overall ML engineering standard.

• Seamless integration of ML features into products that meet business goals.