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Orchard

Machine Learning Engineer

San Francisco$135k–$210kfulltimemidAdded 1 month ago

About this role

Orchard Robotics, a startup focused on automating farms with AI, is seeking a Machine Learning Engineer to develop solutions for processing large-scale farm image data. The role involves building ETL pipelines and infrastructure for model training to optimize data collection and improve performance.

What you'll do

  • Build and maintain scalable ETL pipelines for processing image datasets
  • Develop infrastructure for model training, evaluation, and inference
  • Design intelligent active sampling systems for data optimization
  • Stay updated with advancements in computer vision
  • Collaborate with teams to integrate ML solutions
  • Support various parts of the software stack as needed

What they're looking for

  • 2+ years of experience in data pipelines and ML infrastructure
  • Proficient in Python and ML frameworks (e.g., PyTorch)
  • Strong experience with data engineering tools
  • Familiarity with cloud platforms and containerization
  • Experience with real-world training data
  • Knowledge of MLops software
  • Ability to learn quickly and work independently
  • Enthusiasm for taking on diverse responsibilities

Benefits

  • Generous equity compensation
  • Comprehensive health, vision, and dental coverage
  • 100% premium coverage
  • Close-knit team environment
  • Opportunity to make a positive impact
  • Fast-paced work culture
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Orchard

Orchard builds AI-powered camera systems and robotics to automate and enhance agricultural efficiency on farms. The company is hiring robotics engineers, machine learning engineers, and software engineers to develop perception systems, build data infrastructure, and deploy these technologies across U.S. farms.

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Likely interview questions

  • Walk us through a production ML pipeline you've built end-to-end. How did you handle data quality issues and model drift?
  • Describe your experience working with large-scale, real-world image datasets. What challenges did you face and how did you solve them?
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