Machine Learning Research Engineer at Just Food Company

Department: Research & DevelopmentEducation: education_optional

Compensation

$160,000 – $200,000/yr

Location: Emeryville, California
Type: Full-time
Level: not_specified
Posted: Aug 31, 2026

Description

Just Food Company is hiring a full-time Machine Learning Research Engineer to develop machine learning systems for protein and food-functionality research and to apply AI across product development, sales, operations, and other company functions. The role involves building predictive models, data pipelines, evaluation frameworks, research tools, and practical AI applications; it requires strong Python and machine learning framework skills, scientific problem-solving, software engineering fundamentals, and the ability to work with heterogeneous experimental data. The position is onsite in Emeryville, California, requires United States work authorization without employer sponsorship, and offers a base salary of $160,000–$200,000 per year plus equity and medical, dental, and vision benefits.

Required Skills

Python
PyTorch
JAX
TensorFlow
Protein representation learning
Graph deep learning
Active learning
Bayesian optimization
Data pipelines
Software engineering

Additional Information

Required Previous Experiences: Experience developing modern machine learning models and systems
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
Preferred Qualifications: Experience with protein representation learning, protein language models, graph or geometric deep learning, generative models, multimodal learning, active learning, or Bayesian optimization
Experience with biological sequences, protein structures, molecular graphs, biochemical measurements, or structured scientific data
Experience in computational biology, bioinformatics, protein engineering, structural biology, chemistry, food science, ingredient discovery, or another scientific machine learning domain
Experience designing computational approaches with laboratory scientists and incorporating experimental results into modeling
Experience with language-model applications, retrieval systems, tool-using agents, or internal AI applications
Experience with data pipelines, GPU workloads, cloud infrastructure, distributed training, or scientific computing
Experience with research, open-source projects, publications, patents, datasets, or production machine learning systems