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Senior/Lead ML Applied Scientist

Intuition Machines, Inc. · Lavoro da casa, IT

Actively hiring Posted 5 months ago

Responsibilities

  • Build ML models that can scale to millions of requests per second while maintaining performance.
  • Translate business requirements into technical specifications.
  • Develop ML models that satisfy memory and compute constraints, evaluate them properly, and debug effectively.
  • Provide technical mentorship to other ML research engineers.
  • Iterate quickly, with a focus on shipping early and often, ensuring that new products or features can be deployed to millions of users.
  • Write clearly structured, maintainable, well-documented, and tested code, including unit, integration, and end-to-end tests.
  • Participate in code reviews and architecture & design sessions. Stay updated on recent technological developments and assess their applicability.
  • Provide technical input to the research roadmap.
  • 5+ years of professional experience in applied ML.
  • Proven experience with the entire modeling lifecycle: building, evaluating, and debugging large ML models.
  • Experience with large-scale categorical and structured data.
  • Expertise in real-time ML models, incremental learning, and online learning.
  • Strong understanding of ML fundamentals: bias-variance tradeoffs, loss functions, evaluation metrics, etc.
  • Bachelor’s degree in a technical field (or equivalent practical experience).
  • Thoughtful, self-directed individual who is comfortable making technical decisions independently.

Preferred qualifications

  • Strong grasp of the math required for ML (linear algebra, probability theory, statistics, matrix calculus).
  • Software engineering/development experience with large-scale distributed systems.
  • Ability to collaborate with ML engineers to integrate your work into our infrastructure, including automating observability, deployment, quality, and security.

Benefits

  • Fully remote position with flexible working hours.
  • An inspiring team of colleagues spread all over the world.
  • Pleasant, modern development and deployment workflows: ship early, ship often.
  • High impact: lots of users, happy customers, high growth, and cutting edge R&D.
  • Flat organization, direct interaction with customer teams.

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