W1siziisimnvbxbpbgvkx3rozw1lx2fzc2v0cy9jbnrlbgxldgvjig5ldy9qcgcvbmv3lwjhbm5lci1kzwzhdwx0lmpwzyjdxq

Machine Learning Engineer - Cleantech

  • Location

    San Francisco

  • Sector:

    Data Science

  • Salary:

    Circa $200,000

  • Contact:

    Jason Rumney

  • Contact email:

    jason@intelletec.com

  • Job ref:

    AAJR29203

  • Startdate:

    ASAP

  • Consultant:

    Jason Rumney

Our client is a Series A fast-growing cleantech company based in San Francisco who are solving the big problems around food waste and the fresh food supply chain--focusing first in grocery. They use cutting-edge AI (they've been published in ICML!) combined with thoughtful design to enhance decision-making and optimize store workflows. The results are powerful: in live deployments in grocery stores, we have demonstrated the potential to double profits and reduce food waste by 50%+!

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.

Skills:

  • 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.

Background

 

About 30-40% of food produced worldwide is thrown away, causing nearly a trillion dollars of economic losses, trillions of gallons of wasted water, and billions of tons of greenhouse gas emissions. In the US, about 40% of all food waste occurs at the retail level and downstream, largely driven by insufficient technology and manual processes.

 

Our client seeks to tackle some of these big problems around food waste. Born out of Stanford's Computer Science PhD program, They are the first Fresh food supply chain company. They bring the cutting edge of artificial intelligence to Fresh food to minimize food waste.