Bjak - Senior Machine Learning Engineer
Requirements
• GPU-based training and inference systems • 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. • 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. • 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
Responsibilities
• Build core ML systems for proactive long-horizon AI product end-to-end: data preparation, training, evaluation, inference, and iteration. • Turn research ideas into working production systems reliably run in the field. • Debug model failures using real production signals to ensure system stability. • Iterate quickly by shipping ML models, measuring outcomes, refining based on feedback, and repeating this process for continuous improvement. • Collaborate closely with researchers, product teams, and other engineers to deliver tangible user impact through AI solutions. • Mentor and review work from other ML engineers using example code and technical judgment as a core owner of significant ML systems in production. • Work under real production constraints such as latency, cost, reliability, and safety considerations for the deployed models.
Apply in one click
Upload My Resume
Drop here or click to browse · Tap to choose · PDF, DOCX, DOC, RTF, TXT