Machine Learning Engineer

Company: NotCo
Location: San Francisco, California, United States
Type: Full-time
Posted: 05.JUN.2021
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Summary

For people who believe eating healthier should be effortless and tasty, TheNotCompany - ranked in the Top 50 Most Innovative Companies by Fa...

Description

For people who believe eating healthier should be effortless and tasty, TheNotCompany - ranked in the Top 50 Most Innovative Companies by Fast Company is a five-year-old foodtech company that uses AI technology to help create sustainable & plant-based products in a mission to reduce global greenhouse emissions by taking animals out of the equation. Our proprietary technology helps us to find mouthwatering plant-based formulas that mimic traditional animal-based foods.


Since forming in 2015, we have launched several different plant-based products through our technology platform, Giuseppe, including NotMilk, NotMayo, and NotBurger. NotCo is a global company with offices & product sales in Chile, Brazil, and Argentina; our latest $85m funding round has fueled our growth in the United States, starting with the launch of NotMilk at Whole Foods Market in late 2020.

We are currently looking for a Machine Learning Engineer. This position is located in the San Francisco area.


As a Machine Learning Engineer, you will be part of the NotCo AI group. You will work with a multifaceted team of ML & Software Engineers, UI/UX Designers, Experimental Chefs, and Food Scientists to revolutionize the way plant-based and sustainable foods are created. Our tech platform utilizes predictive & generative models, optimization algorithms, as well as in-house data warehousing infrastructure that allows us to relate nutritional, physicochemical, and molecular data to find new discoveries for novel plant-based products.


Specifically, this person will:

  • Research, develop, and deploy machine learning systems onto the Giuseppe platform for food science & technology applications.
  • Brainstorm with Experimental Chefs & Food Scientists to find data and ML-driven solutions for problems arising with new product development. We develop products ranging from plant-based milk to plant-based meats, so there are always engaging challenges to solve.
  • Lead new machine learning initiatives end-to-end, from proposal to deployment. This requires having strong research, project management & communications skills and the potential to lead a small team.
  • Analyze and debug issues with currently deployed models & suggest improvements to data, algorithm, or workflows.
  • Identify new datasets that could help improve the performance of current algorithms.
  • Read & implement recent ML papers that are relevant to our scope of work (e.g. implement a self-supervised algorithm proposed in a 2020 ICML paper).
  • Dive into the domain of food science: be interested in learning about proteins, volatile molecules & the food industry in general.
  • Contribute in general to department and organization projects.

The ideal candidate will:

  • BS/MS in Computer Science or a related field with a focus on machine learning
  • ML-focused Bioengineers or Analytical Chemistry backgrounds are also urged to apply
  • 3-5 years of working experience in an Applied Machine Learning group
  • Strong data science analytical skills with a traditional data science stack (Python, pandas, numpy, scipy, sklearn, jupyter, matplotlib)
  • Strong background in software engineering, unit & functional testing (Python, unittest, nose)
  • Prior experience with implementing & training Deep Learning models (Pytorch, TensorFlow); prior experience with training models on cloud infrastructure on GPU/TPU
  • Familiarity with model packaging & deployment (Docker, Kubernetes, Google Cloud Platform)
  • In-depth knowledge in one or several of the following areas preferred: Representational Learning, Generative Models, Transformers, bandit algorithms & convex optimizers
  • Authorization to work in the U.S. required

We are an equal opportunity employer. All applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status.

 
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