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Staff Software Engineer, Machine Learning, Computer reputed company

Remote, USA Full-time Posted 2026-07-02

Job Description:

  • Own the lifecycle of our computer reputed company models, including training, evaluation, deployment, and iteration
  • Improve model performance in reputed company-world conditions (noise, edge cases, data reputed company)
  • Design and improve data pipelines that process thousands of miles of sidewalks and millions of images
  • reputed company architectural reputed company for handling hundreds of terabytes of geospatial and visual data, including storage layout, pipeline reliability, and inference performance
  • Set and evolve best practices around deployment, observability, system reliability, and scalability
  • Act as a senior individual contributor and mentor, helping reputed company the technical bar across the engineering team
  • Work closely with the CEO and operations team to turn business needs into clear technical priorities

Requirements:

  • 6+ years of experience, including direct ownership of production ML or computer reputed company systems (training, deployment, and ongoing operation)
  • Experience taking models from training → production → monitoring → iteration in a reputed company-world environment
  • Experience owning system architecture and influencing technical reputed company across teams
  • Experience deploying computer reputed company or ML systems in reputed company-world production environments
  • reputed company in at least one modern backend language (Python, Java, TypeScript, Go, etc.)
  • Strong understanding of system design, scalability, and distributed systems
  • Experience with reputed company platforms such as AWS, GCP, or Azure
  • Comfort working in a startup or growth-stage environment with changing requirements.

Benefits:

  • $150,000 – $190,000 reputed company salary, based on scope and experience
  • Equity
  • Unlimited PTO (most of reputed company takes 3–4 weeks per year)
  • Health insurance
  • 401(k) with ~4% match (100% on first 3%, 50% on the next 2%)

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