Machine Learning Engineer Jobs
Machine Learning Engineer jobs are open across technology, finance, healthcare, and autonomous systems, from new-grad to staff and principal level, with specializations in natural language processing, computer vision, and recommendation systems. Find a role that fits from the openings below and apply directly.
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Showing 5 of 1,986+ Machine Learning Engineer jobs
INTRODUCTION
We believe that the way people interact with their finances will drastically improve in the next few years. We’re dedicated to empowering this transformation by building the tools and experiences that thousands of developers use to create their own products. Plaid powers the tools millions of people rely on to live a healthier financial life. We work with thousands of companies like Venmo, SoFi, several of the Fortune 500, and many of the largest banks to make it easy for people to connect their financial accounts to the apps and services they want to use. Plaid’s network covers 12,000 financial institutions across the US, Canada, UK and Europe. Founded in 2013, the company is headquartered in San Francisco with offices in New York, Seattle, Washington D.C., Raleigh, London, and Amsterdam.
ABOUT THE TEAM
We’re part of Plaid’s Data organization, building models that help millions of people better understand and grow their financial lives. We apply machine learning to high-impact product challenges, working across product areas to develop solutions that create meaningful customer value. We focus on credit, building models to the standards required in lending and supporting the next wave of cash-flow-based underwriting. We value technical expertise, creativity, user empathy, and teamwork, and we’re guided by Plaid’s principles of impact, growing together, openness and positivity, and inventing tomorrow.
ABOUT THE ROLE
As a Machine Learning Engineer on the Credit team, you’ll design, build, and deploy scalable machine learning solutions that support LendScore and the future of cash-flow-based underwriting. You’ll work across the full model lifecycle—from experimentation and offline training through production serving and monitoring—and partner with engineers and cross-functional teams to turn data and new modeling approaches into reliable systems.
YOUR ROLE AND RESPONSIBILITIES
- Develop and productionize AI and machine learning models, from offline training through online serving and ongoing monitoring.
- Experiment with modeling approaches and strategies for credit use cases, balancing model performance with the requirements of lending environments.
- Partner with engineering teams to ingest signals and build the systems needed to bring models into production.
- Contribute to both 0-to-1 problems and the scaling and improvement of established solutions.
- Collaborate with teams across Plaid to help define the machine learning roadmap.
- Dive into data and use evidence-based analysis to guide day-to-day decisions.
WHAT EXCITES YOU
- Building products that can empower millions of people through well-known and emerging fintech applications.
- Experimenting with modern machine learning techniques on meaningful credit and lending problems.
- Working across the full lifecycle of machine learning systems, from exploration to production and ongoing improvement.
- Taking on both greenfield challenges and opportunities to scale solutions that are already delivering value.
- Joining a high-ownership, bottom-up team where your work can shape the roadmap and drive business impact.
WHAT EXCITES US
- 2+ years of experience training and serving AI or machine learning models in a production environment.
- Experience in fintech lending and a strong understanding of how models are built and used in lending.
- Experience building or working with data-intensive backend applications in large, distributed systems.
- Ability to code and iterate independently using data infrastructure tools such as Python, Spark, Jupyter notebooks, and standard machine learning libraries.
- A track record of taking ownership and driving projects to business impact.
- Ability to work effectively with both technical and non-technical teams.
- Master’s degree or equivalent work experience in Computer Science, Mathematics, Engineering, or a closely related field.
- Nice to have: Experience in data analytics or data engineering.
Our mission at Plaid is to unlock financial freedom for everyone. To support that mission, we seek to build a diverse team of driven individuals who care deeply about making the financial ecosystem more equitable. We recognize that strong qualifications can come from both prior work experiences and lived experiences. We encourage you to apply to a role even if your experience doesn't fully match the job description. We are always looking for team members that will bring something unique to Plaid!
Plaid is proud to be an equal opportunity employer and values diversity at our company. We do not discriminate based on race, color, national origin, ethnicity, religion or religious belief, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, transgender status, sexual stereotypes, age, military or veteran status, disability, or other applicable legally protected characteristics. We also consider qualified applicants with criminal histories, consistent with applicable federal, state, and local laws.
Plaid is committed to providing reasonable accommodations for candidates with disabilities in our recruiting process. If you need any assistance with your application or interviews due to a disability, please let us know at accommodations@plaid.com.
Please review our Candidate Privacy Notice here.
Additional compensation in the form(s) of equity and/or commission are dependent on the position offered. Plaid provides a comprehensive benefit plan, including medical, dental, vision, and 401(k). Pay is based on factors such as (but not limited to) scope and responsibilities of the position, candidate's work experience and skillset, and location. Pay and benefits are subject to change at any time, consistent with the terms of any applicable compensation or benefit plans.
COMPENSATION RANGE
- Compensation Range: $211,680 - $272,160
INTRODUCTION
We believe that the way people interact with their finances will drastically improve in the next few years. We’re dedicated to empowering this transformation by building the tools and experiences that thousands of developers use to create their own products. Plaid powers the tools millions of people rely on to live a healthier financial life. We work with thousands of companies like Venmo, SoFi, several of the Fortune 500, and many of the largest banks to make it easy for people to connect their financial accounts to the apps and services they want to use. Plaid’s network covers 12,000 financial institutions across the US, Canada, UK and Europe. Founded in 2013, the company is headquartered in San Francisco with offices in New York, Seattle, Washington D.C., Raleigh, London, and Amsterdam.
ABOUT THE TEAM
We’re part of Plaid’s Data organization, building models that help millions of people better understand and grow their financial lives. We apply machine learning to high-impact product challenges, working across product areas to develop solutions that create meaningful customer value. We focus on credit, building models to the standards required in lending and supporting the next wave of cash-flow-based underwriting. We value technical expertise, creativity, user empathy, and teamwork, and we’re guided by Plaid’s principles of impact, growing together, openness and positivity, and inventing tomorrow.
ABOUT THE ROLE
As a Machine Learning Engineer on the Credit team, you’ll design, build, and deploy scalable machine learning solutions that support LendScore and the future of cash-flow-based underwriting. You’ll work across the full model lifecycle—from experimentation and offline training through production serving and monitoring—and partner with engineers and cross-functional teams to turn data and new modeling approaches into reliable systems.
YOUR ROLE AND RESPONSIBILITIES
- Develop and productionize AI and machine learning models, from offline training through online serving and ongoing monitoring.
- Experiment with modeling approaches and strategies for credit use cases, balancing model performance with the requirements of lending environments.
- Partner with engineering teams to ingest signals and build the systems needed to bring models into production.
- Contribute to both 0-to-1 problems and the scaling and improvement of established solutions.
- Collaborate with teams across Plaid to help define the machine learning roadmap.
- Dive into data and use evidence-based analysis to guide day-to-day decisions.
WHAT EXCITES YOU
- Building products that can empower millions of people through well-known and emerging fintech applications.
- Experimenting with modern machine learning techniques on meaningful credit and lending problems.
- Working across the full lifecycle of machine learning systems, from exploration to production and ongoing improvement.
- Taking on both greenfield challenges and opportunities to scale solutions that are already delivering value.
- Joining a high-ownership, bottom-up team where your work can shape the roadmap and drive business impact.
WHAT EXCITES US
- 2+ years of experience training and serving AI or machine learning models in a production environment.
- Experience in fintech lending and a strong understanding of how models are built and used in lending.
- Experience building or working with data-intensive backend applications in large, distributed systems.
- Ability to code and iterate independently using data infrastructure tools such as Python, Spark, Jupyter notebooks, and standard machine learning libraries.
- A track record of taking ownership and driving projects to business impact.
- Ability to work effectively with both technical and non-technical teams.
- Master’s degree or equivalent work experience in Computer Science, Mathematics, Engineering, or a closely related field.
- Nice to have: Experience in data analytics or data engineering.
Our mission at Plaid is to unlock financial freedom for everyone. To support that mission, we seek to build a diverse team of driven individuals who care deeply about making the financial ecosystem more equitable. We recognize that strong qualifications can come from both prior work experiences and lived experiences. We encourage you to apply to a role even if your experience doesn't fully match the job description. We are always looking for team members that will bring something unique to Plaid!
Plaid is proud to be an equal opportunity employer and values diversity at our company. We do not discriminate based on race, color, national origin, ethnicity, religion or religious belief, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, transgender status, sexual stereotypes, age, military or veteran status, disability, or other applicable legally protected characteristics. We also consider qualified applicants with criminal histories, consistent with applicable federal, state, and local laws.
Plaid is committed to providing reasonable accommodations for candidates with disabilities in our recruiting process. If you need any assistance with your application or interviews due to a disability, please let us know at accommodations@plaid.com.
Please review our Candidate Privacy Notice here.
Additional compensation in the form(s) of equity and/or commission are dependent on the position offered. Plaid provides a comprehensive benefit plan, including medical, dental, vision, and 401(k). Pay is based on factors such as (but not limited to) scope and responsibilities of the position, candidate's work experience and skillset, and location. Pay and benefits are subject to change at any time, consistent with the terms of any applicable compensation or benefit plans.
COMPENSATION RANGE
- Compensation Range: $211,680 - $272,160
Machine Learning Engineer Jobs by Experience Level
Top Cities Hiring Machine Learning Engineers
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Find JobsMachine Learning Engineer Job Market
Who's Hiring
- Meta93
- TikTok60
- Uber52
- Lockheed Martin48
- Amazon Web Services48
Top Industries Hiring
- Technology & Software87
- Electronics & Hardware20
- Science & Research8
- Biotechnology & Pharmaceuticals8
- Consulting & Professional Services8
What Employers Look For
The qualifications that appear most often in machine learning engineer jobs.
- Proficiency in Python and ML frameworks such as PyTorch or TensorFlow
- Experience building and deploying models in cloud environments like AWS, GCP, or Azure
- Familiarity with MLOps tools including MLflow, Kubeflow, or SageMaker
- Strong foundation in statistics, linear algebra, and machine learning theory
- Bachelor's or master's degree in computer science, mathematics, or a related field
- Experience with large-scale data processing using Spark, SQL, or distributed systems
Tips for Your Machine Learning Engineer Job Search
Tailor your resume to deployment depth
Hiring managers distinguish candidates who trained models from those who shipped them to production. Explicitly note the serving infrastructure you used, the scale you operated at, and whether you owned monitoring and retraining pipelines, not just model accuracy metrics.
Build a GitHub portfolio that shows end-to-end work
Recruiters and engineers scan repositories for evidence you can move from raw data to a deployed artifact. Include notebooks, a training script, an inference endpoint, and a brief README explaining the problem you solved and what tradeoffs you made.
Apply early to roles that fit
Migrate Mate lists machine learning engineer 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 the ML stack you know best
Job postings for machine learning engineers vary sharply by framework, cloud platform, and data scale. Prioritize listings that name PyTorch, TensorFlow, JAX, or the specific cloud ML services you have hands-on experience with rather than applying broadly.
Prepare for system design questions alongside coding rounds
Most machine learning engineer interview loops include at least one session on designing scalable ML systems, such as a real-time feature store or an online ranking pipeline. Practice articulating latency budgets, retraining frequency, and data consistency tradeoffs out loud before your first screen.
Negotiate around compute budgets and research time
Beyond base compensation, ask about GPU or TPU access, experiment tracking tooling, and whether engineers are allocated time for internal research. These factors affect your ability to do meaningful work and are often negotiable, especially at mid-size companies.
Machine Learning Engineer Jobs: Frequently Asked Questions
Which companies are hiring the most machine learning engineers?
The companies hiring the most machine learning engineers right now include Meta, TikTok, and Uber, with the largest share of openings in California, Washington, and New York, based on current listings on Migrate Mate as of November 2026. Demand is concentrated in companies scaling generative AI, recommendation systems, and computer vision products.
How many machine learning engineer jobs are remote?
About 75% of machine learning engineer openings are fully remote or hybrid as of November 2026, making it one of the more flexible roles in software. Positions focused on NLP research and MLOps tooling tend to offer the highest share of remote flexibility, while roles tied to robotics or on-premise infrastructure typically require in-person work.
How do you become a machine learning engineer?
Start by building a strong foundation in Python, linear algebra, and statistics, then work through core ML concepts using publicly available datasets and open-source frameworks like PyTorch or scikit-learn. Add hands-on projects that go beyond notebooks to include model deployment and monitoring. A portfolio showing end-to-end ML systems carries more weight in hiring than coursework alone.
Can you get a machine learning engineer job with little or no experience?
Yes, entry-level machine learning engineer roles exist, particularly at startups and in companies building internal ML tooling. Focus your portfolio on projects that solve a real problem and deploy to a live endpoint. Contributing to open-source ML libraries, competing in public benchmarks, and demonstrating strong software engineering fundamentals will distinguish you from other early-career candidates.
What does the machine learning engineer interview process look like?
Most machine learning engineer loops include a recruiter screen, a technical phone interview covering Python and ML fundamentals, a take-home or live coding exercise, an ML system design session, and a final round with cross-functional team members. System design interviews often focus on topics like feature pipelines, model serving, and retraining strategies rather than abstract algorithms.
Where can I find and apply to machine learning engineer jobs?
You can find and apply to machine learning engineer jobs on Migrate Mate, which lists current openings from across the United States. Find roles that match your experience and specialization, then apply directly to each listing. The openings on this page are updated regularly so you can act on new postings as they appear.
See All 1,986+ Machine Learning Engineer Jobs
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