Senior Machine Learning Engineer at Bjak

Warsaw, Masovian Voivodeship, Poland -

Full Time

Start Date

Immediate

Expiry Date

18 Oct, 26

Salary

0.0

Posted On

20 Jul, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, PyTorch, JAX, Machine Learning, GPU Training, Inference Systems, Data Preparation, Model Evaluation, System Design, Production Engineering, Multi-step Reasoning, Debugging, Scalability, Technical Mentorship, API Integration, Performance Optimization

Industry

Software Development

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.

Responsibilities

Design and ship reliable, scalable ML subsystems for a proactive AI assistant focusing on long-horizon workflows. Own the end-to-end lifecycle from data preparation and training to production inference and iteration.