Machine Learning Research Engineer, Scientific AI

Just Food Company•Emeryville, CA

8d•$160,000 - $200,000•Onsite

About The Position

Just Food Company is seeking an exceptional Machine Learning Research Engineer to build and apply machine learning systems that will fundamentally change how the company conducts scientific research and operates. This role has two primary mandates: R&D, focusing on building ML systems to identify, evaluate, and validate proteins with valuable food functionalities, creating a faster learning loop between prediction and experimentation; and applying AI across the company to improve workflows, knowledge access, decision-making, and execution. The role is hands-on, offering significant ownership and autonomy at the intersection of machine learning, scientific research, and practical implementation.

Requirements

Strong foundations in machine learning and demonstrated experience developing modern machine learning models and systems.
Excellent Python skills and hands-on experience with PyTorch, JAX, TensorFlow, or a comparable machine learning framework.
Experience taking machine learning problems from data and experimentation through evaluation and implementation.
Ability to reason across data, model architecture, training, evaluation, inference, and deployment.
Ability to select methods based on the structure of a problem rather than defaulting to a particular model or technology.
Comfort working with sparse, noisy, heterogeneous, or partially observed data.
Strong software engineering fundamentals and a commitment to testing, reproducibility, maintainability, and performance.
Ability to balance open-ended research with practical implementation and iteration.
Intellectual curiosity and the ability to quickly develop fluency in unfamiliar scientific and business domains.
Strong communication skills and enthusiasm for working directly with scientists, product developers, operators, commercial teams, and company leadership.
Comfort operating with significant ownership and autonomy in a small, fast-moving organization.
Candidates may come from a range of educational and professional backgrounds.
A BS, MS, or PhD in Computer Science, Machine Learning, Computational Biology, Bioinformatics, Applied Mathematics, Statistics, or a related quantitative field is relevant, but we value demonstrated ability over a particular degree or academic pedigree.
We are open to candidates at different stages of their careers. You may have developed your expertise through industry, academic research, open-source work, independent projects, or a combination of these experiences.
Candidates must be authorized to work in the United States without current or future employer sponsorship.
Must verify identity and eligibility to work in the United States and complete the required Form I-9 upon hire.

Nice To Haves

Protein representation learning, protein language models, graph or geometric deep learning, diffusion or other generative models, multimodal learning, active learning, or Bayesian optimization.
Biological sequences, protein structures, molecular graphs, biochemical measurements, or other structured scientific data.
Computational biology, bioinformatics, protein engineering, structural biology, chemistry, food science, ingredient discovery, or another scientific ML domain.
Designing computational approaches alongside laboratory scientists and incorporating experimental results into subsequent modeling.
Language-model applications, retrieval systems, tool-using agents, or internal AI applications.
Data pipelines, GPU workloads, cloud infrastructure, distributed training, or scientific computing.
Research, open-source projects, publications, patents, datasets, or production machine learning systems that demonstrate exceptional technical ability.
Previous food science experience is not required, nor is experience with every technology or model family listed in this posting.

Responsibilities

Develop models that predict food functionality using protein sequence, structure, biochemical/biomaterial properties, formulations including other proteins/ingredients, and experimental data.
Build representations and models that capture relationships among proteins, ingredients, formulations, processing conditions, and functional outcomes.
Explore approaches such as protein representation learning, graph and geometric deep learning, generative and diffusion models, multimodal learning, active learning, and Bayesian optimization where appropriate.
Partner directly with experimental scientists to design studies, evaluate predictions, and incorporate experimental results into subsequent modeling.
Build data pipelines, training infrastructure, evaluation frameworks, and research tools to support iterative model development.
Develop prospective evaluations to determine whether model predictions improve protein selection and experimental decision-making.
Translate successful research into practical tools that scientists and product developers can use.
Partner with product development, sales, operations, and other teams to identify high-value opportunities for AI.
Build practical systems that improve knowledge access, analysis, planning, reporting, and execution.
Develop applications and agents that can perform useful work using our data, knowledge, and systems.
Rapidly prototype potential solutions, evaluate their value, and turn the strongest ideas into reliable tools.
Apply appropriate permissions, testing, human oversight, and measurement to deployed systems.
Help establish shared infrastructure and development practices that allow AI systems to be built and used reliably across the company.
Measure success through adoption, time saved, decision quality, scientific impact, and work completed — not simply the number of tools launched.