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Physics Informed Machine Learning Jobs in Manhattan, NY

Everstar Inc

Founding AI Engineer

New York, NY · On-site

Physics-informed design safety analyses using world models that reason about thermal hydraulics ... Agentic workflows that compound over time, learning from each regulatory submission to improve the ...

ICF

Senior Climate Analytics Specialist

New York, NY

$95K - $117K/yr

Translate complex climate risk questions into tractable analytical and modeling frameworks, selecting appropriate statistical, machine learning, and physics-informed approaches * Design, train, and ...

Thalo Labs

Senior Machine Learning Engineer

New York, NY · On-site

$150K - $180K/yr

We're building the physical AI layer for HVAC: sensors plus physics-based intelligence that turn ... As our Senior Machine Learning Engineer, you'll own the intelligence layer of Thalo's platform. We ...

Thalo Labs

Senior Machine Learning Engineer

New York, NY

$150K - $180K/yr

We're building the physical AI layer for HVAC: sensors plus physics-based intelligence that turn ... As our Senior Machine Learning Engineer, you'll own the intelligence layer of Thalo's platform. We ...

Thalo Labs

Senior Machine Learning Engineer

New York, NY

$150K - $180K/yr

We're building the physical AI layer for HVAC: sensors plus physics-based intelligence that turn ... As our Senior Machine Learning Engineer, you'll own the intelligence layer of Thalo's platform. We ...

PhysicsX

Senior Machine Learning Engineer

New York, NY · On-site

$114K - $157K/yr

By enabling high-fidelity, multi-physics simulation through AI inference across the entire ... Who We're Looking For As a Senior Machine Learning Engineer in Delivery, you are an experienced ...

Applied Physics

Data Scientist

New York, NY · On-site

Applied Physics is seeking a Data Scientist experienced with a diverse array of data types to join ... analysis, machine learning, information visualization, as well as others. Responsibilities:

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How much do physics informed machine learning jobs pay per hour?

As of Aug 9, 2026, the average hourly pay for physics informed machine learning in Manhattan, NY is $22.14, according to ZipRecruiter salary data. Most workers in this role earn between $13.80 and $28.12 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive in the physics informed machine learning position, and why are they important?

To thrive in Physics Informed Machine Learning, you need a solid background in physics, strong mathematical and statistical skills, and experience with machine learning algorithms, typically supported by an advanced degree in a relevant field. Proficiency with programming languages like Python, frameworks such as TensorFlow or PyTorch, and familiarity with numerical simulation tools are commonly required. Effective problem-solving, clear communication, and the ability to collaborate with interdisciplinary teams make a significant impact in this role. These capabilities are essential for developing robust, interpretable machine learning models that leverage physical laws to solve complex, real-world problems.

What are the typical challenges faced by professionals working in physics informed machine learning roles?

Professionals in Physics Informed Machine Learning often encounter challenges integrating complex physical theories with advanced machine learning models, requiring deep domain knowledge and strong technical skills. Balancing model accuracy with computational efficiency and ensuring that models are both interpretable and generalizable can be demanding. Collaboration with domain experts, data scientists, and engineers is common, as projects often span multiple disciplines. Successfully navigating these challenges provides valuable experience and is highly regarded, often leading to further career advancement in research, engineering, or leadership positions.

What is a physics informed machine learning?

A Physics Informed Machine Learning (PIML) job involves developing AI models that integrate physics-based principles to improve accuracy, interpretability, and generalization. Professionals in this role use machine learning techniques alongside domain knowledge in physics, engineering, or applied sciences to solve complex problems in areas like fluid dynamics, materials science, and climate modeling. Responsibilities often include designing algorithms, implementing simulations, and validating results against experimental or real-world data. Employers typically seek expertise in deep learning, numerical methods, and programming languages like Python.

What are popular job titles related to Physics Informed Machine Learning jobs in Manhattan, NY? For Physics Informed Machine Learning jobs in Manhattan, NY, the most frequently searched job titles are:
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What cities near Manhattan, NY are hiring for Physics Informed Machine Learning jobs? Cities near Manhattan, NY with the most Physics Informed Machine Learning job openings:
Physics Informed Machine Learning jobs near you

Machine Learning, Vice President

Morgan Stanley

New York, NY • On-site

Full-time

Posted 16 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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