Sr Staff Machine Learning Engineer Jobs in New York
Sr Staff Machine Learning Engineer jobs in New York are among the most competitive and actively recruited positions in the country, concentrated in financial services, media technology, healthcare AI, and enterprise software across New York City, Buffalo, and Albany. Firms like Google, JPMorgan Chase, and IBM maintain significant engineering presences in New York and consistently hire at this senior-staff level. The most sought-after specializations in New York right now are large language model development, MLOps infrastructure, and real-time recommendation systems. Find a role that fits below and apply directly.
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Showing 5 of 24+ Sr Staff Machine Learning 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 24 Sr Staff Machine Learning Engineer Jobs in New York
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Find JobsSr Staff Machine Learning Engineer Jobs by City in New York
Where New York roles are concentrated, by current openings.
Sr Staff Machine Learning 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
- Insurance
- Retail
- Manufacturing
- Media & Entertainment
What New York Employers Look For
The qualifications that appear most often in sr staff machine learning engineer jobs across New York.
- Bachelor's or master's degree in computer science, statistics, or a closely related quantitative field
- Eight or more years of machine learning engineering experience with demonstrated leadership at staff or principal level
- Deep proficiency in Python and at least one major ML framework such as PyTorch or TensorFlow
- Experience designing and deploying large-scale ML systems in cloud environments such as AWS or Google Cloud
- Proven ability to mentor engineers and define technical strategy across cross-functional New York teams
- Familiarity with MLOps practices including model monitoring, CI/CD pipelines, and feature store architecture
Sr Staff Machine Learning Engineer Jobs in New York: Frequently Asked Questions
How do you become a sr staff machine learning engineer in New York?
Reaching the sr staff machine learning engineer level in New York typically requires a strong quantitative degree followed by progressive engineering roles that include technical leadership. New York does not require a state-issued license for this role. Most candidates build toward it by leading ML platform or product projects at a New York employer, earning recognition for cross-team impact, and demonstrating the ability to set engineering direction rather than simply execute on it.
How much do sr staff machine learning engineers make in New York?
Sr staff machine learning 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 sr staff machine learning engineers in New York?
Employers hiring sr staff machine learning engineers in New York right now include Uber, GEICO, and Radar, based on current listings on Migrate Mate as of November 2026. New York's density of financial institutions, media companies, and enterprise tech firms means demand at this level stays relatively consistent throughout the year compared with other markets.
Which New York cities have the most sr staff machine learning engineer jobs?
New York and Brooklyn account for the largest share of sr staff machine learning engineer openings in New York. New York City drives the majority of demand through its concentration of financial services firms, advertising technology companies, and large tech engineering offices, while secondary markets like Buffalo and Albany reflect the presence of healthcare systems and state government technology initiatives that are expanding applied ML teams.
Are there remote sr staff machine learning engineer jobs in New York?
Yes, and more than most fields. About 83% of sr staff machine learning engineer openings tied to New York are remote or hybrid as of November 2026, reflecting how well the analytical and architectural nature of the role translates to distributed work. The parts of the role most commonly offered fully remote include model design, code review, and cross-functional technical leadership, while on-site expectations tend to cluster around strategic planning cycles or team onboarding periods.
How can I get hired as a sr staff machine learning engineer in New York with little or no experience?
The most realistic entry path is a junior or mid-level machine learning engineer role at a New York employer, using it to build a portfolio of shipped models and measurable impact. Large New York institutions such as Cornell Tech's industry partnerships and NYU's applied ML programs offer project pipelines that connect students to employers. Adjacent roles like data scientist, software engineer on an ML platform team, or ML research associate at a financial services firm in New York frequently serve as the practical on-ramp to senior-track positions.
Where can I find and apply to sr staff machine learning engineer jobs in New York?
You can find and apply to sr staff machine learning engineer jobs in New York on Migrate Mate, which lists current New York openings updated regularly. Find roles that match your experience and seniority level and apply directly to the ones that fit.
See All 24 Sr Staff Machine Learning 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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