ML Software Engineer Jobs in New York
ML Software Engineer jobs in New York are among the most active in the country, concentrated in finance, media, advertising technology, and enterprise software across experience levels from entry-level associate to principal engineer. The heaviest hiring happens in New York City, with additional demand in Buffalo and Albany tied to healthcare systems and state government modernization efforts. IBM, JPMorgan Chase, and Bloomberg are among the most consistent large employers of ml software engineers in New York, with strong demand in natural language processing, recommendation systems, and MLOps. Find a role that fits below and apply directly.
Find ML Software Engineer JobsOverview
Showing 4 of 15+ ML Software Engineer jobs
STAFF ENGINEER, MACHINE LEARNING
Current is a leading consumer fintech platform transforming financial access for everyday Americans with over 6 million members. We provide access to financial solutions that seamlessly work together to solve the needs of our members and enable all Americans to build better financial futures. Based in NYC, our results-driven environment drives us to build better products, grow faster and empower everyone on our team to have an impact on our business and mission to improve financial outcomes.
Current's Engineering team is dedicated to building our products and infrastructure. With our applications running on Google Cloud Kubernetes Engine, we support a proprietary banking core that can scale to handle millions of transactions a day. Our services run on MongoDB, with data exported to Google Cloud Storage and BigQuery for analytics. Batch feature computation runs on the JVM using Apache Beam and Dataflow, with dbt for the analytics estate and Airflow for orchestration. Our ML stack includes an in-house feature store and model registry with a serving proxy, and our data scientists develop models in Python.
Machine learning drives decisions across the business: underwriting for liquidity products, fraud detection and risk exposure, marketing acquisition and spend. Models that ship faster and behave predictably in production are worth real money and real member trust.
We are looking for a Staff Engineer, Machine Learning to join our Infrastructure team in New York. This role has a salary range of $240,000 - $325,000. You will lead ML engineering initiatives across Current, with the goal of optimizing our model lifecycle: improving how we build, validate, deploy, and change models, and making that path faster and more repeatable as our model portfolio grows. This is a hands-on individual contributor role without direct reports, with room to grow into a team. The ideal candidate has built and operated ML systems in production end to end, not only models, and has a track record of setting technical direction and delivering against it. This person should be comfortable leading from an ambiguous problem to a shipped solution, and should treat data scientists as their customer.
WHAT TO EXPECT:
- Owning technical direction for the ML stack end to end: feature definition and computation, training data generation, training infrastructure, model serving, and production monitoring, along with the contracts between them
- Building tooling for training/serving consistency across analytics, batch computation, and live serving, accounting for differences in data sources and timing
- Designing how every deployed model stays linked to its dataset, feature versions, labels, and training code, to the standard model risk management expects
- Enabling data scientists to generate reproducible, point-in-time-correct datasets and run standard validation without an engineering ticket
- Setting the working contracts between the groups that build, consume, and govern models, and keeping the stack legible to people who don't read the code
- Measuring delivery time, engineering effort, and rework, and using that evidence to prioritize improvements
- In your first year:
- Establishing a delivery baseline and proving the workflow on one production model with versioned features, a reproducible dataset, and reusable validation
- Extending those capabilities to additional models and measuring adoption and improvement against the baseline
- Standardizing model monitoring and defining production-readiness gates with Data Science, Risk, and service owners
- Evaluating build-versus-buy options for ML platform tooling against real production requirements
- Partnering daily with engineers across our squads and with data scientists and analysts, and regularly with Risk, Marketing, and Finance, who own the decisions our models support
ABOUT YOU:
- 3+ years experience building and operating ML systems in production, including feature pipelines, the training data path, the serving layer, and the monitoring around them
- A track record of improving ML delivery workflows, and the ability to explain the trade-offs, results, and lessons from those decisions
- 8+ years of overall software engineering experience, including strong production skills in Python and SQL, experience building production systems in a JVM language, and 3+ years of experience building and maintaining ML platforms
- Sound reasoning about time in data: point-in-time correctness, label leakage, feature availability, and training/serving skew
- Experience setting a long-term technical direction and turning it into an achievable roadmap, delivering useful improvements along the way
- Experience leading initiatives from an ambiguous problem through scoping, stakeholder agreement, and delivery
- Experience establishing engineering standards, mentoring engineers, and helping teams adopt shared infrastructure
- Strong communication skills, with the ability to explain trade-offs clearly and find workable solutions across engineering, data science, risk, marketing, and finance
- Fluency with AI tools, including coding agents, in your own engineering work, with the judgment to evaluate their output and own the quality of what you ship
- Feature store, feature platform, or ML platform experience at a company where models make consequential decisions is a plus
- Experience in financial services, credit, fraud, or another regulated decisioning domain, and familiarity with model risk management, is a plus
- Experience with streaming and change data capture, large-scale batch on Apache Beam or Spark, or distributed training is a plus
BENEFITS:
- Competitive salary
- Meaningful equity in the form of stock options
- 401(k) plan
- Discretionary performance bonus program
- Biannual performance reviews
- Medical, Dental and Vision premiums covered at 100% for you and your dependents
- Flexible time off and paid holidays
- Generous parental leave policy
- Commuter benefits
- Fitness benefits
- Healthcare and Dependent care FSA benefit
- Employee Assistance Programs focused on mental health
- Healthcare advocacy program for all employees
- Access to mental health apps
- Team building activities
- Our modern NYC based office with open floor plan, stocked kitchen, and catered lunches
STAFF ENGINEER, MACHINE LEARNING
Current is a leading consumer fintech platform transforming financial access for everyday Americans with over 6 million members. We provide access to financial solutions that seamlessly work together to solve the needs of our members and enable all Americans to build better financial futures. Based in NYC, our results-driven environment drives us to build better products, grow faster and empower everyone on our team to have an impact on our business and mission to improve financial outcomes.
Current's Engineering team is dedicated to building our products and infrastructure. With our applications running on Google Cloud Kubernetes Engine, we support a proprietary banking core that can scale to handle millions of transactions a day. Our services run on MongoDB, with data exported to Google Cloud Storage and BigQuery for analytics. Batch feature computation runs on the JVM using Apache Beam and Dataflow, with dbt for the analytics estate and Airflow for orchestration. Our ML stack includes an in-house feature store and model registry with a serving proxy, and our data scientists develop models in Python.
Machine learning drives decisions across the business: underwriting for liquidity products, fraud detection and risk exposure, marketing acquisition and spend. Models that ship faster and behave predictably in production are worth real money and real member trust.
We are looking for a Staff Engineer, Machine Learning to join our Infrastructure team in New York. This role has a salary range of $240,000 - $325,000. You will lead ML engineering initiatives across Current, with the goal of optimizing our model lifecycle: improving how we build, validate, deploy, and change models, and making that path faster and more repeatable as our model portfolio grows. This is a hands-on individual contributor role without direct reports, with room to grow into a team. The ideal candidate has built and operated ML systems in production end to end, not only models, and has a track record of setting technical direction and delivering against it. This person should be comfortable leading from an ambiguous problem to a shipped solution, and should treat data scientists as their customer.
WHAT TO EXPECT:
- Owning technical direction for the ML stack end to end: feature definition and computation, training data generation, training infrastructure, model serving, and production monitoring, along with the contracts between them
- Building tooling for training/serving consistency across analytics, batch computation, and live serving, accounting for differences in data sources and timing
- Designing how every deployed model stays linked to its dataset, feature versions, labels, and training code, to the standard model risk management expects
- Enabling data scientists to generate reproducible, point-in-time-correct datasets and run standard validation without an engineering ticket
- Setting the working contracts between the groups that build, consume, and govern models, and keeping the stack legible to people who don't read the code
- Measuring delivery time, engineering effort, and rework, and using that evidence to prioritize improvements
- In your first year:
- Establishing a delivery baseline and proving the workflow on one production model with versioned features, a reproducible dataset, and reusable validation
- Extending those capabilities to additional models and measuring adoption and improvement against the baseline
- Standardizing model monitoring and defining production-readiness gates with Data Science, Risk, and service owners
- Evaluating build-versus-buy options for ML platform tooling against real production requirements
- Partnering daily with engineers across our squads and with data scientists and analysts, and regularly with Risk, Marketing, and Finance, who own the decisions our models support
ABOUT YOU:
- 3+ years experience building and operating ML systems in production, including feature pipelines, the training data path, the serving layer, and the monitoring around them
- A track record of improving ML delivery workflows, and the ability to explain the trade-offs, results, and lessons from those decisions
- 8+ years of overall software engineering experience, including strong production skills in Python and SQL, experience building production systems in a JVM language, and 3+ years of experience building and maintaining ML platforms
- Sound reasoning about time in data: point-in-time correctness, label leakage, feature availability, and training/serving skew
- Experience setting a long-term technical direction and turning it into an achievable roadmap, delivering useful improvements along the way
- Experience leading initiatives from an ambiguous problem through scoping, stakeholder agreement, and delivery
- Experience establishing engineering standards, mentoring engineers, and helping teams adopt shared infrastructure
- Strong communication skills, with the ability to explain trade-offs clearly and find workable solutions across engineering, data science, risk, marketing, and finance
- Fluency with AI tools, including coding agents, in your own engineering work, with the judgment to evaluate their output and own the quality of what you ship
- Feature store, feature platform, or ML platform experience at a company where models make consequential decisions is a plus
- Experience in financial services, credit, fraud, or another regulated decisioning domain, and familiarity with model risk management, is a plus
- Experience with streaming and change data capture, large-scale batch on Apache Beam or Spark, or distributed training is a plus
BENEFITS:
- Competitive salary
- Meaningful equity in the form of stock options
- 401(k) plan
- Discretionary performance bonus program
- Biannual performance reviews
- Medical, Dental and Vision premiums covered at 100% for you and your dependents
- Flexible time off and paid holidays
- Generous parental leave policy
- Commuter benefits
- Fitness benefits
- Healthcare and Dependent care FSA benefit
- Employee Assistance Programs focused on mental health
- Healthcare advocacy program for all employees
- Access to mental health apps
- Team building activities
- Our modern NYC based office with open floor plan, stocked kitchen, and catered lunches
See All 15 ML Software Engineer Jobs in New York
Find roles in New York that match your experience and apply in just a few clicks.
Find ML Software Engineer JobsML Software Engineer Jobs by City in New York
Where New York roles are concentrated, by current openings.
ML Software Engineer Job Market in New York
A snapshot from current New York openings, updated as new roles post.
Who's Hiring
Top Industries Hiring
- Technology & Software
- Media & Entertainment
What New York Employers Look For
The qualifications that appear most often in ML software engineer jobs across New York.
- Bachelor's or master's degree in computer science, statistics, or a related quantitative field
- Proficiency in Python and core ML frameworks such as TensorFlow, PyTorch, or scikit-learn
- Experience designing, training, and deploying machine learning models in production environments
- Familiarity with cloud platforms including AWS, Google Cloud, or Azure for ML workloads
- Strong understanding of data pipelines, feature engineering, and model evaluation techniques
- Ability to collaborate cross-functionally with data engineers, product managers, and business stakeholders
ML Software Engineer Jobs in New York: Frequently Asked Questions
How do you become a ml software engineer in New York?
There is no state-issued license required to work as an ml software engineer in New York. The standard path is a bachelor's or master's degree in computer science, mathematics, or a related field, followed by demonstrated project experience through internships, open-source contributions, or a strong portfolio. New York employers in finance and media often prioritize candidates who can show end-to-end model deployment, not just academic coursework.
How much do ML software engineers make in New York?
ML software engineers in New York earn a median of about $166,180 a year, based on May 2025 Bureau of Labor Statistics wage data, ranging from around $97,430 for the lowest 10% to over $224,590 for the top 10%. Pay rises with experience, specialty, and employer.
Which companies hire ml software engineers in New York?
Employers hiring ml software engineers in New York right now include Meta, Tubi, and Uber, based on current listings on Migrate Mate as of November 2026. New York's density of financial services firms, advertising technology companies, and large media organizations makes it one of the most diverse hiring markets for machine learning talent in the country.
Which New York cities have the most ml software engineer jobs?
New York and Brooklyn have the most ml software engineer openings in New York. New York City drives the majority of demand through its concentration of investment banks, hedge funds, and tech companies, while Buffalo and Albany see steady openings tied to healthcare technology initiatives and state agency modernization projects that have drawn engineering teams outside the main metro.
Are there remote ml software engineer jobs in New York?
Yes, and more than most fields. About 67% of ml software engineer openings tied to New York are remote or hybrid as of November 2026, reflecting how well this work translates to distributed environments. Model development, experimentation, and code review are the parts of the role most commonly done fully remote, while on-site expectations tend to apply to embedded teams at trading firms or media companies with proprietary infrastructure.
How can I get hired as a ml software engineer in New York with little or no experience?
The most realistic entry path is a master's program with an industry capstone or internship at a New York employer, as firms like IBM and JPMorgan Chase run structured new-graduate programs that convert interns to full-time hires. Candidates transitioning from data analyst or software engineering roles can move laterally by completing a machine learning specialization and building a portfolio of deployed models. Contributing to open-source ML projects and targeting associate ML engineer postings at New York startups are also credible starting points.
Where can I find and apply to ml software engineer jobs in New York?
You can find and apply to ml software engineer jobs in New York on Migrate Mate, which lists current New York openings updated regularly. Search the listings, find roles that fit your experience and location, and apply directly to the employers posting them.
See All 15 ML Software Engineer Jobs in New York
Find roles in New York that match your experience and apply in just a few clicks.
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