For people who believe eating healthier should be effortless and tasty, TheNotCompany (“NotCo”) – ranked in the Top 50 Most Innovative Companies by Fast Company - is a five-year old foodtech company that develops an AI technology and conducts cutting-edge science to enable the creation of unprecedented plant-based products, in a mission to restart the food industry. 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, Colombia, Mexico and the US and Canada; our latest $235m funding round will fuel our further growth in North America.
We are growing rapidly in new ways and markets. We are motivated by change and purpose. We are ready to roll up our sleeves and do what it takes to start something new. Be part of the transformation!
We are currently looking for a Machine Learning Engineer. This position is located in the San Francisco area.
As a Machine Learning Engineer in 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.
Role & Responsibilities:
- 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 milks 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 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 an 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.
Requirements:
- BS/MS in Computer Science or a related field with a focus on machine learning.
- 2-5 years of working experience in an Machine Learning group (either in academia or industry).
- Strong data science analytical skills with a traditional data science stack (Python, pandas, numpy, scipy, sklearn, jupyter, matplotlib).
- Strong experience working with optimizers (blackbox optimizer, gradient-based/gradient descent, Bayesian Optimization) packages.
- 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
If our purpose and way of working vibrate with you, and you want to be part of this story, apply to join this squad.
WHY NOT!
