• Location

    San Francisco

  • Sector:

    Data Science

  • Job type:


  • Salary:

    Circa $190,000

  • Contact:

    Amelia Jones

  • Contact email:


  • Job ref:


  • Consultant:

    Amelia Jones




We are working with a fast-growing tech company in San Francisco who are solving the big problems around food waste and the fresh food supply chain. They are using cutting-edge AI combined with thoughtful design to enhance decision-making and optimize store workflows. The results are powerful: in live deployments in grocery stores, they have demonstrated the potential to double profits and reduce food waste by 50%.


They are growing fast and are in partnership with 4 large regional grocers representing 500+ stores and >$10B in revenue. Backers include Innovation Endeavors (former Google CEO Eric Schmidt’s firm) and Baseline Ventures (first money in Stitchfix, SoFi, Heroku, Instagram).

Due to rapid growth, our client is seeking Senior Machine Learning Engineers to join the team.


What will you be doing?


  • Training machine learning models over billions of data points. Quantifying predictive uncertainty using probabilistic and Bayesian methods. Creating models that quickly generalize to new tasks using few-shot and meta- learning.

  • Training agents that execute decisions to optimize a reward over time. Implementing state-of-the-art model-based planning and reinforcement learning algorithms, including offline and off-policy methods that learn from human demonstrations.

  • Scaling machine learning systems to massive datasets using big data technologies such as Spark and Hadoop.

  • Building visualization and data exploration tools that automate the analysis and debugging of machine learning models.

What skills and experience do you need?

  • Masters or PhD in computer science, or equivalent.

  • 4+ years of work experience.

  • Strong programming and problem-solving skills.

  • Deep knowledge of machine learning, including both supervised and reinforcement learning. 

  • Specific subfields include deep learning, probabilistic and Bayesian methods, few-shot and meta- learning, model-based planning, and imitation learning. 

  • Proficiency with the Python machine learning stack, including numpy, scipy, pandas, scikit-learn, matplotlib, tensorflow, keras, pytorch.

  • Experience with reinforcement learning, model-based planning, and/or control theory is a plus.