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Physics Based Machine Learning Jobs in New York (NOW HIRING)

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 ...

Meta

Computer Vision Engineer

New York, NY

$154K/yr

Experience with computer graphics, and physics-based/geometric modeling * Working knowledge of imaging systems and optics simulation * Direct background in machine learning, deep learning, neural ...

S&P Global, Inc.

Machine Learning Engineer

Manhattan, NY · On-site

$209 - $250.30/hr

Kensho Technologies LLC seeks a Machine Learning Engineer who will identify, research, prototype, and build predictive data-driven products based on statistical analysis and machine learning.

Spgi

Machine Learning Engineer

New York, NY · On-site +1

$209K - $250K/yr

Machine Learning Engineer Location: 55 Water Street, New York, NY 10041 (Telecommuting permitted ... Final base salary for this role will be based on the individual's geographic location, as well as ...

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Physics Based Machine Learning information

What does a physics based machine learning professional do?

Physics Based Machine Learning professionals often work on projects that involve applying machine learning techniques to physical systems, such as improving simulations in engineering, optimizing energy systems, or accelerating scientific research through data-driven modeling. Daily tasks might include developing algorithms that incorporate physical laws, analyzing simulation data, and collaborating with experts from engineering, data science, or research teams. The role can involve both theoretical and hands-on work, often requiring iterative testing and validation. This environment provides opportunities to tackle cutting-edge challenges, contribute to innovation, and potentially lead to career paths in research, product development, or advanced analytics.

What is a physics based machine learning?

A Physics Based Machine Learning job involves developing machine learning models that incorporate physical laws and domain knowledge to improve predictions and interpretability. Professionals in this field work at the intersection of physics, data science, and artificial intelligence to create models that are more robust, generalizable, and efficient, especially in scientific and engineering applications. Responsibilities often include data analysis, algorithm development, numerical simulations, and integrating physics-based constraints into ML models. These roles are common in industries like climate science, robotics, materials science, and computational physics.

What are the key skills and qualifications needed to thrive in physics based machine learning?

To thrive in Physics Based Machine Learning, you need advanced knowledge of physics, strong programming skills (Python, MATLAB, or C++), and a deep understanding of machine learning and statistical modeling, typically supported by a master's or PhD in physics, engineering, or a related field. Familiarity with simulation software, scientific computing libraries (such as TensorFlow, PyTorch, NumPy), and version control systems is essential. Strong problem-solving ability, effective communication, and cross-disciplinary collaboration skills set outstanding candidates apart. These competencies are crucial for designing robust, real-world models that integrate physical principles with data-driven techniques to solve complex problems.

What are popular job titles related to Physics Based Machine Learning jobs in New York? For Physics Based Machine Learning jobs in New York, the most frequently searched job titles are:
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Infographic showing various Physics Based Machine Learning job openings in New York as of August 2026, with employment types broken down into 79% Full Time, 7% Part Time, 7% Temporary, and 7% Contract. Highlights an 100% In-person job distribution.

Senior Machine Learning Engineer

Thalo Labs

New York, NY • On-site

$150K - $180K/yr

Full-time

Medical, Dental, Vision, Retirement, PTO

Posted 17 days ago

Job description

Who We Are:

The world is electrifying, and HVAC is at the center of it. Over the next decade, 100 to 200 million new heat pumps and HVAC units will become the backbone of a decarbonized world, but the industry has no way to keep them running well. The technician workforce has barely grown while the equipment base has multiplied, reactive repairs eat most of a tech's time, and half the installed base gets no real maintenance at all, wasting energy and driving billions in emergency costs. Thalo is fixing this. We're building the physical AI layer for HVAC: sensors plus physics-based intelligence that turn static equipment into self-monitoring systems and shift service from guesswork to data. Every sensor we deploy makes the platform smarter and builds a dataset on how equipment truly performs that no one else can.


We're a small team that has built self-driving cars at Waymo, worked on satellite imagery at Google, designed systems for John Deere, developed space missions for NASA, and led manufacturing design for Boom Supersonic jets. Now we're bringing that same rigor to one of the most important buildouts of our lifetime. In this role, the models you build decide whether a technician is sent to the right unit at the right time, and whether a building wastes energy or runs clean. It's a rare chance to work on a generational climate challenge, with first-of-its-kind data and a team of high performers who ship.


About the Role:

As our Senior Machine Learning Engineer, you'll own the intelligence layer of Thalo's platform. We generate hundreds of gigabytes of HVAC sensor data no one in the world has seen before, and your job is to turn it into the detection algorithms, physics-based models, and product features that tell our customers exactly what's wrong with their equipment and what to do about it.  


This is a hands-on, end-to-end role for someone who wants to own a problem from raw time-series data all the way to a shipped, customer-facing feature. You'll build and tune our issue-detection engine, put physics-based, ML, and LLM-powered models into production, establish how we evaluate and trust them, and work closely with our engineering, customer success, and business development teams to make sure the intelligence we ship is accurate, trustworthy, and genuinely useful in the field. You'll be a senior voice on a small, mighty team!

What we offer:

  • An immediate opportunity to make an impact fighting climate change with a mission-driven team.

  • An in-person, collaborative culture. In our midtown Manhattan office, we not only have a stocked pantry but we also dedicate time to connect with each other during weekly happy hours and quarterly offsites. 

  • National subsidized healthcare plans for medical, dental, and vision insurance.

  • Additional benefits include a 401(k) program, 12 weeks paid parental leave, and paid time off.

  • Free mental health and professional coaching appointments through Lyra.

  • At our ground-floor stage, our compensation structure places a strong emphasis on the value of high equity, with an annual compensation ranging from $150,000-$180,000.

What you'll do:

  • Own, extend, and improve Thalo's issue-detection engine spanning the electrical, refrigerant, and equipment-performance diagnostics at the core of our product

  • Research, develop, and implement ML, statistical, and LLM-based models in production, working directly with first-of-its-kind streaming sensor time-series data

  • Own our AI-evaluation practice: build labeled fault sets (from service outcomes, physics-vs-LLM disagreements, and field cross-checks), define accuracy metrics, and stand up an eval harness that regression-tests every prompt change, new detector, and model upgrade before it ships

  • Turn model outputs into clear, actionable insights and reports our field, CS, and BD teams can confidently put in front of customers

  • Continuously improve the data pipeline for large-scale ingestion, storage, transformation, and analysis so detection runs reliably and cost effectively as we scale

  • Partner closely with hardware, software, and business teams to connect field and customer insights back into the product  and document your work so the whole team can build on it

What you have:

  • 5+ years building and deploying ML or statistical models on production data, ideally in an early-stage startup environment

  • M.S. or higher in a quantitative discipline such as math, physics, statistics, or data science (or equivalent applied experience)

  • Strong applied experience with time-series or streaming sensor data, including anomaly detection, forecasting, signal processing, or similar

  • Hands-on experience shipping production features on frontier LLMs (e.g., prompt engineering, structured output, tool-use/agents, and RAG)  with the judgment to know when an LLM is the right tool versus a deterministic rule or a statistical model

  • Experience evaluating AI systems: building eval sets, measuring precision/recall, using LLM-as-judge, and guarding against regressions as prompts and models change

  • Fluency in Python and the modern data stack, with the software-engineering chops to ship production-grade code (not just notebooks)

  • A real customer instinct: the ability to translate a model output into a plain-English insight a technician or building operator will trust and act on

  • Curiosity about the physical world and the drive to understand the "why" behind the product, not just how to implement it

  • A self-directed, ownership mindset and a habit of documenting and sharing context 

Bonus points:

  • A passion for tackling climate change and promoting sustainability

  • HVAC, refrigeration, combustion, building-systems, or energy-domain experience (a strong plus, but something we're happy to help the right person learn)

  • Experience with agentic / tool-use systems, RAG over technical documentation, or LLM vision

  • Familiarity with LLM cost/latency optimization (prompt caching, batch inference) and model governance (managing upgrades, monitoring output/score drift, A/B-testing context changes)

  • Frontier-class LLM, open source LLM, and/or AWS Bedrock in production

  • Full-stack comfort to take a feature to the UI (React/TypeScript); time-series databases (InfluxDB, TimescaleDB) and tools like Grafana; a degree in a quantitative or engineering discipline

$150,000 - $180,000 a year

Commitment to Diversity, Equity, and Inclusion:

Thalo Labs is committed to diversity and building an equitable and inclusive environment for people of all backgrounds and experiences.  We think that a diverse team is critical to Thalo's success.  We especially encourage members of traditionally underrepresented communities to apply, including women, people of color, LGBTQ+ people, veterans, and people with disabilities.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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