Machine Learning Scientist Jobs
Machine Learning Scientist jobs are open across technology, healthcare, finance, and defense, from new-grad to principal and staff scientist levels, with common specializations in natural language processing, computer vision, and reinforcement learning. Find a role that fits from the openings 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!
Machine Learning Scientist Jobs by Experience Level
Top Cities Hiring Machine Learning Scientists
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Find JobsMachine Learning Scientist Job Market
Who's Hiring
- Meta21
- SentiLink12
- Scale AI11
- Lila Sciences10
- TikTok9
Top Industries Hiring
- Technology & Software40
- Biotechnology & Pharmaceuticals16
- Artificial Intelligence7
- Banking & Financial Services6
- Electronics & Hardware5
What Employers Look For
The qualifications that appear most often in machine learning scientist jobs.
- PhD or MS in computer science, statistics, or a closely related quantitative field
- Proficiency in Python with deep experience in PyTorch or TensorFlow
- Experience designing, training, and evaluating large-scale machine learning models
- Familiarity with cloud ML infrastructure such as AWS SageMaker, GCP Vertex AI, or Azure ML
- Strong publication record or demonstrated applied research output
- Experience with distributed training, model optimization, or MLOps pipelines
Tips for Your Machine Learning Scientist Job Search
Tailor your resume to research depth
Hiring managers for machine learning scientist roles expect to see publications, preprints, or internal research artifacts. List your contributions explicitly, including whether you were lead author, and tie each project to a measurable outcome like latency reduction or model accuracy gain.
Apply early to roles that fit
Migrate Mate lists machine learning scientist openings from across the United States in one place, so you can find roles that match and apply directly to each listing.
Filter openings by research versus applied focus
Some machine learning scientist roles are primarily research-oriented with publish-or-present expectations, while others are production-focused. Read job descriptions for phrases like 'deploy at scale' or 'publish in top-tier venues' to identify which track fits your background before applying.
Prepare a take-home or live coding portfolio
Many machine learning scientist interviews include a system design or modeling exercise. Have at least one end-to-end project you can walk through live, covering data preprocessing decisions, architecture choices, and how you evaluated tradeoffs against simpler baselines.
Negotiate compute access alongside compensation
When you reach the offer stage, ask specifically about GPU cluster access, cloud compute budgets, and time allocated to exploratory research. These resources directly affect your ability to do the work and are negotiable at many organizations, especially in research-heavy teams.
Follow up with a specific technical observation
After an interview, reference a concrete detail from a technical discussion rather than sending a generic thank-you. Mentioning a specific modeling tradeoff you discussed shows genuine engagement and reinforces your fit for a role that demands scientific rigor.
Machine Learning Scientist Jobs: Frequently Asked Questions
Which companies are hiring the most machine learning scientists?
The companies hiring the most machine learning scientists right now include Meta, SentiLink, and Scale AI, with the largest share of openings in California, Washington, and Massachusetts, based on current listings on Migrate Mate as of November 2026. Demand is concentrated in technology, healthcare AI, and financial services.
How many machine learning scientist jobs are remote?
About 47% of machine learning scientist openings are fully remote or hybrid as of November 2026, making it one of the more location-flexible research roles. Sub-areas like natural language processing and computer vision research tend to have the highest share of remote positions, while roles tied to proprietary hardware or on-site data tend to require in-person work.
How do you become a machine learning scientist?
Most machine learning scientists build their foundation through a graduate degree in computer science, statistics, or applied mathematics, followed by research experience through internships, lab work, or industry projects. Developing a public portfolio of end-to-end projects, contributing to open-source machine learning libraries, and publishing or presenting findings in research communities helps demonstrate applied depth beyond coursework.
Can you get hired as a machine learning scientist without much experience?
Entry-level machine learning scientist roles do exist, but they typically require demonstrated research output rather than just coursework. Strong candidates with limited industry experience compensate by publishing preprints, contributing meaningfully to open-source projects, completing competitive research internships, or producing detailed public project write-ups that show end-to-end problem-solving at a research level.
What does the machine learning scientist interview process look like?
Machine learning scientist interviews typically include a resume and research background screen, followed by technical rounds covering machine learning theory, coding exercises in Python, and system design or modeling case studies. Many processes also include a research presentation where you walk through a past project in depth. Final rounds often involve meetings with cross-functional stakeholders or research leads assessing scientific communication and collaboration fit.
Where can I find and apply to machine learning scientist jobs?
You can find and apply to machine learning scientist jobs on Migrate Mate, which lists current openings from across the United States. Find roles that match your background and specialization, then apply directly to each listing that fits.
See All 219+ Machine Learning Scientist Jobs
Find roles that match your experience and apply in just a few clicks.
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