Resumo da vaga

Senior Machine Learning Engineer

Requisitos e responsabilidades

Conteúdo da vaga extraído em seções para revisão mais rápida.

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.
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