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Scientific Machine Learning Jobs in New York (NOW HIRING)

NxT Level

Senior Machine Learning Engineer

Manhattan, NY

$133K - $176K/yr

A degree in Computer Science, Machine Learning, or a related field, or equivalent professional experience. * A strong theoretical foundation in core Machine Learning concepts and techniques.

NxT Level

Senior Machine Learning Engineer

Manhattan, NY · On-site

$133K - $176K/yr

A degree in Computer Science, Machine Learning, or a related field, or equivalent professional experience. * A strong theoretical foundation in core Machine Learning concepts and techniques.

PhysicsX

Machine Learning Engineer

New York, NY · On-site

Working at the intersection of data science and software engineering, you translate R&D and project ... This Role As a Machine Learning Engineer, you'll work closely with our Data Scientists, Simulation ...

Point72

Machine Learning Engineer

New York, NY · On-site

PhD or PhD candidate in machine learning, computer science or other AI related research fields * Experience with sequential modeling and time series forecasting using deep learning * Experience with ...

Point72

Machine Learning Engineer

New York, NY · On-site

PhD or PhD candidate in machine learning, computer science or other AI related research fields * Experience with sequential modeling and time series forecasting using deep learning * Experience with ...

ExaCare AI

Machine Learning Engineer

Manhattan, NY · On-site

$110 - $150/hr

We are seeking a highly adaptable, creative, and well‑rounded Machine Learning Engineer to join ... Bachelor's or Master's degree in Computer Science, AI, Data Science, or a related technical field.

ExaCare Inc

Machine Learning Engineer

Manhattan, NY · On-site

$100 - $130/hr

Machine Learning Engineer ExaCare Inc - New York, New York, United States About this position ... Experience working closely with researchers, data scientists, or ML practitioners to productionize ...

exacare ai

Machine Learning Engineer

Manhattan, NY · On-site

They are seeking a Machine Learning Engineer focused on MLOps to operationalize and scale their ... scientists, or ML practitioners to productionize models • Strong software engineering ...

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Showing results 21-40

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Scientific Machine Learning information

What is scientific machine learning?

Scientific machine learning (SciML) is an interdisciplinary field that combines principles from machine learning and scientific computing to solve complex scientific and engineering problems. It involves developing algorithms and models that can learn from data and physical laws, such as differential equations, to make predictions, optimize systems, or gain insights into phenomena. SciML is widely used in areas like physics, biology, climate science, and engineering, enabling researchers to accelerate simulations and make data-driven discoveries. The field often leverages both traditional numerical methods and modern machine learning techniques, making it a rapidly evolving area of research.

What are some common challenges faced by professionals in scientific machine learning, and how can they be addressed?

Professionals in Scientific Machine Learning often encounter challenges such as integrating domain-specific scientific knowledge with machine learning models, managing large and complex datasets, and ensuring that models are interpretable and physically consistent. Collaboration with domain experts and interdisciplinary teams is essential to bridge knowledge gaps and validate results. To address these challenges, it is helpful to invest time in understanding the underlying scientific principles, keep up-to-date with advancements in both machine learning and scientific fields, and utilize specialized tools and frameworks designed for scientific data.

What are the key skills and qualifications needed to thrive as a scientific machine learning professional, and why are they important?

To thrive as a Scientific Machine Learning professional, you need a strong background in mathematics, statistics, programming (often Python), and domain-specific scientific knowledge, typically with a graduate degree in a STEM field. Proficiency in machine learning frameworks (such as TensorFlow or PyTorch), scientific computing tools (like NumPy, SciPy), and experience with high-performance computing are commonly required. Critical thinking, problem-solving, and collaborative communication are vital soft skills for designing experiments and interpreting complex data. These skills ensure robust, reproducible results and the ability to bridge scientific inquiry with advanced computational methods.

What is the difference between Scientific Machine Learning vs Data Scientist?

AspectScientific Machine LearningData Scientist
Required credentialsAdvanced degrees in CS, ML, or related fields; knowledge of scientific computingDegree in CS, statistics, or related fields; strong analytical skills
Work environmentResearch labs, academia, industry R&D teamsBusiness analytics, tech companies, consulting firms
Industry usageResearch, scientific computing, engineering simulationsBusiness insights, predictive modeling, data analysis

Scientific Machine Learning focuses on integrating scientific knowledge with machine learning techniques for research and engineering applications. Data Scientists analyze data to extract insights and build predictive models for business or operational purposes. While both roles require strong technical skills, Scientific Machine Learning emphasizes scientific computing and domain-specific modeling, whereas Data Scientists focus on data analysis and visualization.

What are popular job titles related to Scientific Machine Learning jobs in New York? For Scientific Machine Learning jobs in New York, the most frequently searched job titles are:
What job categories do people searching Scientific Machine Learning jobs in New York look for? The top searched job categories for Scientific Machine Learning jobs in New York are:
What cities in New York are hiring for Scientific Machine Learning jobs? Cities in New York with the most Scientific Machine Learning job openings:
Scientific Machine Learning jobs near you
Infographic showing various Scientific Machine Learning job openings in New York as of August 2026, with employment types broken down into 1% As Needed, 72% Full Time, 23% Part Time, 2% Temporary, and 2% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution.

Machine Learning, Vice President

Morgan Stanley

New York, NY • On-site

Full-time

Posted 19 days ago

Morgan Stanley rating

8.4

Company rating: 8.4 out of 10

Based on 155 frontline employees who took The Breakroom Quiz

30th of 150 rated financial services

Job description

Morgan Stanley is a leading global financial services firm providing a wide range of investment banking, securities, investment management and wealth management services. The Firm's employees serve clients worldwide including corporations, governments and individuals from more than 1,200 offices in 43 countries.
As a market leader, the talent and passion of our people is critical to our success. Together, we share a common set of values rooted in integrity, excellence and strong team ethic. Morgan Stanley can provide a superior foundation for building a professional career - a place for people to learn, achieve and grow. A philosophy that balances personal lifestyles, perspectives and needs is an important part of our culture.
The Machine Learning team in the Wealth Management (WM) Strategy & Analytics division at Morgan Stanley works on a breadth of applied AI research areas including but not limited to recommender systems, client personalization, graphical neural networks (GNNs), and natural language understanding/LLMs. We provide machine learning (ML) solutions to our internal stakeholders across all our clients channels (Advisor-led, Workplace, and Self-directed) and Product organizations (Investment Solutions, Bank) as well as functions (Marketing, Risk). Our ML scientists ideate, innovate, design, prototype, and ship ML solutions delivering delightful new experiences to 20M+ WM clients.
Responsibilities
  • Lead the design, development, and delivery of end-to-end machine learning solutions to address strategic business opportunities in Wealth Management, delivering measurable business outcomes.
  • Leverage AI/ML modeling and algorithms to deliver use cases supporting the growth plan across Client advisor and product strategy.
  • Build modeling solutions at speed and scale to solve complex business problems across large client and advisor populations.
  • Investigate, design, and create experimental prototypes focused on specific business domains and verticals.
  • Analyze large, complex data sets to quantitatively reveal underlying patterns, correlations, trends, and growth opportunities.
  • Strive to develop and experiment with state-of-the-art algorithms, including advanced machine learning, deep learning, recommender systems, and emerging AI approaches.
  • Support and enhance existing models to ensure improved performance, stability, scalability, and business impact.
  • Set up and conduct large-scale experiments, including A/B tests, to test hypotheses and drive business growth.
  • Validate machine learning models in collaboration with validation teams to ensure accuracy, reliability, explainability, and compliance with model governance standards.
  • Deploy machine learning models in production environments in collaboration with MLOps and technology teams, and monitor performance over time.
  • Participate in and lead code reviews, modeling reviews, and technical design discussions to raise engineering and modeling standards across the team.
  • Build, grow, and strengthen partnerships with business stakeholders, Marketing, Digital, Product, Risk, Legal, Compliance, Technology, and other cross-functional partners.
  • Create executive-ready presentations and analytical narratives to effectively communicate modeling results, business implications, and strategic recommendations to senior stakeholders.
  • Mentor junior data scientists and contribute to the development of team best practices, reusable modeling assets, and scalable AI/ML frameworks.

Qualifications
  • Master's degree or Ph.D. preferred in an analytical or technical field such as Computer Science, Engineering, Applied Mathematics, Physics, Statistics, Operations Research, or an equivalent quantitative discipline.
  • Minimum of 8 years of professional experience in data science, machine learning, AI, advanced analytics, or a related quantitative field.
  • Advanced knowledge of statistical and machine learning methods, particularly in modeling, classification, regression, recommender systems, clustering, deep learning, and experimental design.
  • Demonstrated hands-on experience building models at speed and scale to solve complex commercial or business problems.
  • Experience conceiving, implementing, deploying, and continually improving machine learning projects in production or production-like environments.
  • Minimum of 8 years of experience programming in SQL, Python, and/or R.
  • Proficiency in autonomously conducting applied ML research with commercial applications and translating business problems into scalable modeling solutions.
  • Strong familiarity with higher-level trends in artificial intelligence, generative AI, LLMs, and open-source AI/ML platforms.
  • Experience working with AWS, Azure, Google Cloud, or similar cloud platforms.
  • Experience with code versioning systems such as GitHub or Bitbucket, and experiment tracking systems such as MLflow or equivalent.
  • Proficiency with computer science fundamentals, including object-oriented design, data structures, and algorithmic design.
  • Strategic thinker and influencer with demonstrated leadership acumen, problem-solving skills, and ability to drive outcomes across cross-functional teams.
  • Strong communication skills with experience presenting technical concepts, modeling results, and business recommendations to senior business stakeholders.
  • Familiarity with visualization techniques and software to communicate analytical insights effectively.
  • Proficiency in English

Preferred
  • Experience with Cloud or Big Data technologies such as Azure, AWS, Google Coud, Hadoop, or an equivalent
  • Familiarity with Deep Learning frameworks (PyTorch, Tensorflow, PyTorch - Geometric, or equivalent).
  • Experience with Graphical Neural Networks, Reinforcement Learning, LLMs, Transformer based Models, or Recommender Systems is a plus.
  • Track record of publishing in peer-reviewed scientific journals

WHAT YOU CAN EXPECT FROM MORGAN STANLEY:
At Morgan Stanley, we raise, manage and allocate capital for our clients - helping them reach their goals. We do it in a way that's differentiated - and we've done that for 90 years. Our values - putting clients first, doing the right thing, leading with exceptional ideas, committing to diversity and inclusion, and giving back - aren't just beliefs, they guide the decisions we make every day to do what's best for our clients, communities and more than 80,000 employees in 1,200 offices across 42 countries. At Morgan Stanley, you'll find an opportunity to work alongside the best and the brightest, in an environment where you are supported and empowered. Our teams are relentless collaborators and creative thinkers, fueled by their diverse backgrounds and experiences. We are proud to support our employees and their families at every point along their work-life journey, offering some of the most attractive and comprehensive employee benefits and perks in the industry. There's also ample opportunity to move about the business for those who show passion and grit in their work.
To learn more about our offices across the globe, please copy and paste https://www.morganstanley.com/about-us/global-offices into your browser.
Expected base pay rates for the role will be between $115,000 and $190,000 per year at the commencement of employment. However, base pay if hired will be determined on an individualized basis and is only part of the total compensation package, which, depending on the position, may also include commission earnings, incentive compensation, discretionary bonuses, other short and long-term incentive packages, and other Morgan Stanley sponsored benefit programs.
Morgan Stanley is an equal opportunity employer committed to building and maintaining a workforce that is diverse in experience and background. Our recruiting efforts reflect our strong commitment to a culture of inclusion, where individuals are hired, developed, and advanced based on their skills and talents.
Our workforce reflects a broad cross-section of the global communities in which we operate, bringing a variety of backgrounds, talents, perspectives, and experiences.
For more information, please visit: https://www.morganstanley.com/people-opportunities/eeo.

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