ML Research Engineer Jobs in Washington
ML Research Engineer jobs in Washington rank among the most active in the country, concentrated in artificial intelligence, cloud infrastructure, and autonomous systems across employers at every level from recent PhD graduates through principal researchers. Seattle and Redmond anchor the market, with Bellevue growing steadily, and established names like Microsoft, Amazon, and Google maintain large research divisions in the region. The most in-demand specializations are large language model development, reinforcement learning, and applied ML for computer vision and natural language processing. Find a role that fits below and apply directly.
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A-Alpha Bio is an early-stage, rapidly growing biotechnology platform company headquartered in Seattle, WA. We are focused on building the foundational data layer for AI-driven protein engineering. We provide biopharma companies and model builders with access to ground truth experimental datasets, including affinity and structure, for training, benchmarking and validating AI protein models. In parallel, we apply our data and models directly with select partners to engineer next-generation dual-specific and molecular glue therapeutics, and to discover and optimize differentiated biologics.
A-Alpha Bio is headquartered in Seattle, WA, but remote applicants are welcome to apply. Candidates must reside in the United States.
Starting Salary Range: $164,000 - $206,000 annually
Exemption Status: Exempt
Location Expectations: Remote-Eligible (expected travel to Seattle HQ once per quarter)
Reporting To: Director, Machine Learning Research
Role Description
A-Alpha Bio is seeking a Senior Scientist, Machine Learning Research to turn large-scale protein interaction data into insights and models that advance antibody and protein engineering. The role focuses on developing innovative AI/ML methods that learn from millions of protein-protein interaction data points to predict and engineer protein binding. The Senior Scientist will take a technical lead on challenging AI/ML projects within the ML Research team, including de novo design of antibodies or minibinders as well as models for affinity prediction and developability optimization. The Senior Scientist will work together with other ML Scientists to design and execute experiments (both in silico and in vitro with the help of wet-lab colleagues), support long-term AI/ML research initiatives, and communicate scientific results to the ML Research team and broader company, as well as to external collaborators and partners.
Key Responsibilities
- Develop AI/ML methods. Design and implement novel AI/ML approaches for the prediction and engineering of protein-protein interactions, drawing on experimental measurements, protein sequences, and structural information, and use these approaches to solve challenges in drug discovery and optimization.
- Evaluate rigorously. Design benchmarks and evaluations that assess model performance, uncertainty, and generalization, using appropriate baselines and guarding against data leakage so that conclusions are scientifically defensible and reproducible.
- Deliver results. Generate deliverables (such as data packages, model executables, and technical reports) to a level of quality suitable for use within the company and by external collaborators, in accordance with assigned timelines.
- Contribute code and data. Expertly contribute to the shared company codebase and database, helping teammates understand judgements and decisions, authoring and reviewing merge requests, documenting methods, organizing data, and ensuring that new capabilities and data are effectively rolled out to the rest of the team.
- Advance our scientific capabilities. Apply scientific discipline and critical thinking to propose and analyze experiments, stay current with emerging technologies and trends, and identify and develop innovative computational approaches that increase the value of AlphaSeq data for protein engineering.
- Lead, collaborate, and communicate. Take a technical leadership role on assigned projects interfacing across departments, provide mentorship to colleagues, help write and publish technical blog articles or scientific papers, and represent A-Alpha Bio publicly at conferences and/or in scientific discussions with key collaborators.
Qualifications & Experience
Required
- PhD in a quantitative or life sciences field (biology, chemistry, physics, computer science, etc.) with 4+ years of professional experience in software development and AI/ML, including 2+ years of hands-on experience with AI/ML using biological sequence and/or structure data; or Bachelor's degree in a quantitative or life sciences field with 12+ years of relevant experience, including 6+ years of professional experience in software development and AI/ML; or an equivalent combination of education and experience
- Strong experience with deep learning, spanning modern software frameworks (PyTorch, JAX), model architectures and approaches (variational autoencoders [VAEs], transformers, graph neural networks [GNNs], diffusion, and flow matching), and common protein engineering tasks (protein language models, inverse folding, and protein structure prediction)
- Strong proficiency with data science best practices, including wrangling and transforming data, model training, model selection, cross-validation, and deployment at scale
- Excellent verbal and written communication skills, including the ability to explain methods, findings, and limitations to computational and experimental audiences, as well as to external collaborators and partners
Preferred
- Expertise with cloud computing, particularly Amazon Web Services (AWS)
- Proficiency with best practices using large language models (LLMs) and agentic AI tools to assist in code generation and modeling
- Experience analyzing large experimental datasets and making scientifically defensible decisions about data quality, statistical methods, and interpretation
- Thorough understanding of concepts from biology, chemistry, and physics that govern molecular properties and interactions
- Familiarity with the state of the art in antibody engineering, protein engineering, synthetic biology, or other high-throughput biology environments with particular emphasis on the relationship between sequence, structure, and function
In 2018, A-Alpha Bio spun out of the University of Washington's Institute for Protein Design and Center for Synthetic Biology – both communities filled with brilliant and highly entrepreneurial scientists. Since the company's inception, we have built and scaled AlphaSeq, our high-throughput platform for quantitatively measuring protein-protein interactions at unprecedented scale. What began as a technology for generating large, standardized affinity datasets has grown into a broader data engine for AI-driven protein engineering. New members of the team should expect a fast-paced, highly dynamic, and interdisciplinary work environment. We are building a diverse, meritocratic, and supportive team that shares our values of speaking the truth, shooting for the moon, taking the reins, playing for the team, and improving the world.
A-Alpha Bio is headquartered in a custom designed lab and office space in downtown Seattle's Belltown neighborhood. We are fortunate to be situated in a region that is renowned for its premier outdoor activities, including hiking, biking, climbing, skiing, kayaking, sailing, and more. At A-Alpha, you'll find many who love the outdoors, but also gamers, amateur chefs, explorers, sports fans, bakers, crafters, photographers, brewers, jetsetters, animal lovers, and artists. Whatever your passions, you belong at A-Alpha Bio!
A-Alpha Bio is an early-stage, rapidly growing biotechnology platform company headquartered in Seattle, WA. We are focused on building the foundational data layer for AI-driven protein engineering. We provide biopharma companies and model builders with access to ground truth experimental datasets, including affinity and structure, for training, benchmarking and validating AI protein models. In parallel, we apply our data and models directly with select partners to engineer next-generation dual-specific and molecular glue therapeutics, and to discover and optimize differentiated biologics.
A-Alpha Bio is headquartered in Seattle, WA, but remote applicants are welcome to apply. Candidates must reside in the United States.
Starting Salary Range: $164,000 - $206,000 annually
Exemption Status: Exempt
Location Expectations: Remote-Eligible (expected travel to Seattle HQ once per quarter)
Reporting To: Director, Machine Learning Research
Role Description
A-Alpha Bio is seeking a Senior Scientist, Machine Learning Research to turn large-scale protein interaction data into insights and models that advance antibody and protein engineering. The role focuses on developing innovative AI/ML methods that learn from millions of protein-protein interaction data points to predict and engineer protein binding. The Senior Scientist will take a technical lead on challenging AI/ML projects within the ML Research team, including de novo design of antibodies or minibinders as well as models for affinity prediction and developability optimization. The Senior Scientist will work together with other ML Scientists to design and execute experiments (both in silico and in vitro with the help of wet-lab colleagues), support long-term AI/ML research initiatives, and communicate scientific results to the ML Research team and broader company, as well as to external collaborators and partners.
Key Responsibilities
- Develop AI/ML methods. Design and implement novel AI/ML approaches for the prediction and engineering of protein-protein interactions, drawing on experimental measurements, protein sequences, and structural information, and use these approaches to solve challenges in drug discovery and optimization.
- Evaluate rigorously. Design benchmarks and evaluations that assess model performance, uncertainty, and generalization, using appropriate baselines and guarding against data leakage so that conclusions are scientifically defensible and reproducible.
- Deliver results. Generate deliverables (such as data packages, model executables, and technical reports) to a level of quality suitable for use within the company and by external collaborators, in accordance with assigned timelines.
- Contribute code and data. Expertly contribute to the shared company codebase and database, helping teammates understand judgements and decisions, authoring and reviewing merge requests, documenting methods, organizing data, and ensuring that new capabilities and data are effectively rolled out to the rest of the team.
- Advance our scientific capabilities. Apply scientific discipline and critical thinking to propose and analyze experiments, stay current with emerging technologies and trends, and identify and develop innovative computational approaches that increase the value of AlphaSeq data for protein engineering.
- Lead, collaborate, and communicate. Take a technical leadership role on assigned projects interfacing across departments, provide mentorship to colleagues, help write and publish technical blog articles or scientific papers, and represent A-Alpha Bio publicly at conferences and/or in scientific discussions with key collaborators.
Qualifications & Experience
Required
- PhD in a quantitative or life sciences field (biology, chemistry, physics, computer science, etc.) with 4+ years of professional experience in software development and AI/ML, including 2+ years of hands-on experience with AI/ML using biological sequence and/or structure data; or Bachelor's degree in a quantitative or life sciences field with 12+ years of relevant experience, including 6+ years of professional experience in software development and AI/ML; or an equivalent combination of education and experience
- Strong experience with deep learning, spanning modern software frameworks (PyTorch, JAX), model architectures and approaches (variational autoencoders [VAEs], transformers, graph neural networks [GNNs], diffusion, and flow matching), and common protein engineering tasks (protein language models, inverse folding, and protein structure prediction)
- Strong proficiency with data science best practices, including wrangling and transforming data, model training, model selection, cross-validation, and deployment at scale
- Excellent verbal and written communication skills, including the ability to explain methods, findings, and limitations to computational and experimental audiences, as well as to external collaborators and partners
Preferred
- Expertise with cloud computing, particularly Amazon Web Services (AWS)
- Proficiency with best practices using large language models (LLMs) and agentic AI tools to assist in code generation and modeling
- Experience analyzing large experimental datasets and making scientifically defensible decisions about data quality, statistical methods, and interpretation
- Thorough understanding of concepts from biology, chemistry, and physics that govern molecular properties and interactions
- Familiarity with the state of the art in antibody engineering, protein engineering, synthetic biology, or other high-throughput biology environments with particular emphasis on the relationship between sequence, structure, and function
In 2018, A-Alpha Bio spun out of the University of Washington's Institute for Protein Design and Center for Synthetic Biology – both communities filled with brilliant and highly entrepreneurial scientists. Since the company's inception, we have built and scaled AlphaSeq, our high-throughput platform for quantitatively measuring protein-protein interactions at unprecedented scale. What began as a technology for generating large, standardized affinity datasets has grown into a broader data engine for AI-driven protein engineering. New members of the team should expect a fast-paced, highly dynamic, and interdisciplinary work environment. We are building a diverse, meritocratic, and supportive team that shares our values of speaking the truth, shooting for the moon, taking the reins, playing for the team, and improving the world.
A-Alpha Bio is headquartered in a custom designed lab and office space in downtown Seattle's Belltown neighborhood. We are fortunate to be situated in a region that is renowned for its premier outdoor activities, including hiking, biking, climbing, skiing, kayaking, sailing, and more. At A-Alpha, you'll find many who love the outdoors, but also gamers, amateur chefs, explorers, sports fans, bakers, crafters, photographers, brewers, jetsetters, animal lovers, and artists. Whatever your passions, you belong at A-Alpha Bio!
See All 14 ML Research Engineer Jobs in Washington
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Find ML Research Engineer JobsML Research Engineer Jobs by City in Washington
Where Washington roles are concentrated, by current openings.
ML Research Engineer Job Market in Washington
A snapshot from current Washington openings, updated as new roles post.
Who's Hiring
Top Industries Hiring
- Technology & Software
- Electronics & Hardware
- Artificial Intelligence
What Washington Employers Look For
The qualifications that appear most often in ML research engineer jobs across Washington.
- PhD or Master's degree in machine learning, computer science, or a closely related field
- Proven experience designing and training large-scale deep learning models end to end
- Proficiency in Python and frameworks such as PyTorch or TensorFlow for research workflows
- Strong publication record or equivalent demonstrated research contributions in ML
- Experience with distributed training infrastructure and cloud platforms such as AWS or Azure
- Ability to collaborate across applied science, engineering, and product teams on research projects
ML Research Engineer Jobs in Washington: Frequently Asked Questions
How do you become a ml research engineer in Washington?
ML research engineer roles in Washington do not require a state-issued license or registration. The standard path is a Master's or PhD in machine learning, computer science, statistics, or a related field, followed by demonstrated research experience through publications, open-source contributions, or internships. Washington's concentration of large tech research labs means candidates who have completed graduate research with industry relevance, particularly in deep learning or applied AI, are well positioned to enter the market.
Which companies hire ml research engineers in Washington?
Employers hiring ml research engineers in Washington right now include Meta, Scale AI, and Apple, based on current listings on Migrate Mate as of November 2026. Washington's density of established technology headquarters and cloud computing divisions means research engineering roles appear across both large platform companies and specialized AI-focused teams within those organizations.
Which Washington cities have the most ml research engineer jobs?
Seattle, Redmond, and Bellevue have the most ml research engineer openings in Washington. Seattle drives the largest share because it is home to the headquarters and major research campuses of Amazon and Google, while Redmond hosts Microsoft's core AI and cloud research divisions, and Bellevue has grown as a secondary tech hub as those same companies expand their footprints across the Eastside corridor.
Are there remote ml research engineer jobs in Washington?
Yes, and more than most fields. About 17% of ml research engineer openings tied to Washington are remote or hybrid as of November 2026, reflecting the desk-based and computation-driven nature of the work. Research and modeling tasks that rely on cloud infrastructure and collaborative codebases tend to be the most remote-friendly, while roles requiring close collaboration with hardware or robotics teams are more likely to require on-site presence.
How can I get hired as a ml research engineer in Washington with little or no experience?
The most realistic entry path is a research internship or residency program at one of Washington's major tech employers, with Microsoft Research, Amazon Science, and Google Research all running structured programs for graduate students and recent graduates. Candidates without formal experience can also transition from adjacent roles such as data scientist, software engineer with ML exposure, or research assistant. Building a public portfolio of reproducible experiments and contributing to open-source ML projects signals research readiness to Washington hiring teams.
Where can I find and apply to ml research engineer jobs in Washington?
You can find and apply to ml research engineer jobs in Washington on Migrate Mate, which lists current Washington openings updated regularly. Search the listings to find roles that match your background and apply directly to the ones that fit.
See All 14 ML Research Engineer Jobs in Washington
Find roles in Washington that match your experience and apply in just a few clicks.
Find ML Research Engineer Jobs