Senior Machine Learning Engineer
Job description
About A1
There are over 5 billion users using basic applications today such email, notes, tasks that are not AI-native. Our mission is to build a proactive smart assistant for everyday users to bring intelligence to conversations, errands, organising and workflows, with minimal prompting.
Our product focuses on achieving high reliability for long-running workflows, persistent context, and real-world task completion. The system must handle multi-step reasoning, interact with external tools, and remain reliable despite non-deterministic model behavior. Our objective is to help users complete tasks daily enjoyable with over ~90%* reduced time.
Role
As a Senior Member of Technical Staff, Machine Learning, you are an independent owner of critical ML subsystems in production. You take ambiguous problems, design practical solutions, and ship systems that operate reliably at scale.
This is a hands-on, high-impact role focused on depth.
Focus
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
Tech Stack
Python
PyTorch / JAX
GPU-based training and inference systems
Ideal Experience
You have 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
ML models and systems in production 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 over time.
Drives measurable improvements in ML systems based on real-world signals and user feedback.
Provides mentorship and technical guidance to peers, raising the overall ML engineering standard.
Collaborates cross-functionally to ensure ML features integrate seamlessly into products and meet business goals.
How We Work
The best products today in the world were built by small, world class teams. We are a high talent density and hands-on team. We make decisions collectively, move at rapid speed, striking a balance between shipping high quality work and learning. Joining our team requires the ability to bring structure, exercise judgment, and execute independently. Our goal is to put in hands of our users a truly magical product
Interview process
If there appears to be a fit, we'll reach to schedule 3, but no more than 4 interviews.
Applications are evaluated by our technical team members. Interviews will be conducted via virtual meetings and/or onsite.
We value transparency and efficiency, so expect a prompt decision. If you've demonstrated the exceptional skills and mindset we're looking for, we'll extend an offer to join us. This isn't just a job offer; it's an invitation to be part of a team that's bringing AI to have practical benefits to billions globally.
Originally posted on Himalayas