Machine Learning Research Engineer, Scientific AI

Just Food Company is on a mission to build a healthier, safer, and more sustainable food system—starting with what’s on your plate. From pioneering plant-based eggs to making real meat without slaughter, we’re reimagining the future of food in ways that are better for people and the planet.

We’re a lean, passionate team of scientists, chefs, engineers, and operators who believe that doing hard things well can make a meaningful difference. We’ve brought groundbreaking innovations to market, like Just Egg and GOOD Meat, and we’re just getting started.

If you’re excited by big challenges, real impact, and the chance to shape what the future tastes like—we’d love to meet you.

Learn more about us and our products →

About Us

Just Food Company uses science and technology to build a better food system.

Our work brings together protein science, food science, machine learning, product development, and commercial execution. We investigate proteins, understand how their properties translate into food functionality, and use those insights to create products that can compete on taste, performance, nutrition, affordability, and scale.

We believe machine learning can fundamentally change both how we conduct scientific research and how we operate as a company. We are looking for an exceptional Machine Learning Research Engineer to help us build that capability.

This role has two connected mandates.

The primary focus is R&D. You will build machine learning systems that help us identify, evaluate, and validate proteins with valuable food functionalities. Our goal is to create a faster learning loop between prediction and experimentation: identify promising proteins, predict how they may perform in food, select the most informative experiments, and use experimental results to continuously improve subsequent predictions and decisions.

The second mandate is applying AI across Just Food Company. You will work with teams across the organization to identify important workflows that AI can materially improve and build practical systems that help people access knowledge, analyze information, make decisions, and execute their work more effectively.

This is an opportunity for someone who wants to apply modern machine learning to challenging scientific problems while also helping shape how an organization uses AI more broadly.

About the Role

As a Machine Learning Research Engineer, you will work at the intersection of machine learning, scientific research, and practical implementation.

Within R&D, you will develop models that connect protein sequence, structure, biochemical properties, and experimental data to functional performance in food. You will work closely with protein scientists, food scientists, and product developers to determine which proteins to investigate, what experiments to run, and how to learn systematically from every result.

Depending on the problem, your work may involve protein representation learning, graph and geometric deep learning, multimodal models, generative or diffusion models, active learning, Bayesian optimization, or other approaches. We are not committed to a particular model family. We care about selecting the right method for the scientific problem and rigorously evaluating it against real experimental outcomes.

Beyond R&D, you will apply the same problem-solving mindset to opportunities across the company. Working directly with teams in product development, sales, operations, and other functions, you will identify high-value applications for AI, rapidly develop and evaluate solutions, and turn successful prototypes into reliable tools.

This is a highly hands-on role. You will have significant ownership and autonomy while working closely with scientists, functional teams, and company leadership.

What You'll Do

Accelerate Scientific Discovery

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.

Apply AI Across Just Food Company

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.

What We're Looking For

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.

Particularly Relevant Experience

You do not need experience in all of the areas below. Strong candidates may bring depth in one or more of the following:

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 infrastru