ZipRecruiter

Log In

1

Scientific Machine Learning Jobs in New York (NOW HIRING)

Root Access Inc

Machine Learning Engineer

New York, NY · On-site

D. in Computer Science, Mathematics, EE, Physics, or a related quantitative field with a focus on Scientific Machine Learning (SciML). * Deep Learning Frameworks: 4+ years of expert-level experience ...

Forhyre

Machine Learning Engineer

New York, NY

Study and transform data science prototypes * Design machine learning systems * Research and ... implement appropriate ML algorithms and tools * Develop machine learning applications according to ...

SysMind Tech

Machine Learning

Manhattan, NY · On-site

$85/hr

Stay updated with the latest trends and technologies in data science and machine learning. Basic Qualifications: Proficient in Python, Pandas, NumPy, Scikit-Learn, PySpark Bachelor s degree in ...

Spotify

Machine Learning Engineer

New York, NY · On-site +1

$170K - $212K/yr

You have a collaborative mindset, enjoy working closely with research scientists, machine learning engineers, and data engineers to innovate and improve models. * You have experience in optimizing ...

Spotify

Machine Learning Engineer

New York, NY · On-site +1

$170K - $212K/yr

You have a collaborative mindset, enjoy working closely with research scientists, machine learning engineers, and data engineers to innovate and improve models. * You have experience in optimizing ...

Hang

Machine Learning Engineer

New York, NY · On-site +1

This person will implement and develop machine learning models to enhance our platform ... Work closely with software engineers, data scientists, and product managers to integrate ML models ...

next page

Showing results 1-20

All JobsScientific Machine Learning JobsScientific Machine Learning Jobs in New York

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

Machine Learning Engineer

Root Access Inc

New York, NY • On-site

Full-time

Re-posted 10 days ago

Job description

About the company
Root Access is a frontier electronics company. We are a NYC-based startup funded by top investors. Our team is a passionate mix of engineers across electrical, firmware, software, and machine learning.
Core Responsibilities
  • Architect Physics Foundation Models: Design and train deep learning models.
  • Build the ECAD Data Pipeline: Develop high-performance asset pipelines to convert geometric, discrete, and multi-layer PCB files (ODB++, IPC-2581, STEP, Gerber) into continuous space data.
  • Multi-Modal Architecture Integration: Collaborate on connecting upstream Graph Neural Networks (GNNs) or LLMs mapping schematic topologies to downstream spatial physics engines.
  • Optimize for Real-Time Execution: Optimize training and inference pipelines on GPU clusters.

Required Technical Skills & Qualifications
  • Education: Master's or Ph.D. in Computer Science, Mathematics, EE, Physics, or a related quantitative field with a focus on Scientific Machine Learning (SciML).
  • Deep Learning Frameworks: 4+ years of expert-level experience with PyTorch or JAX.
  • SciML Expertise: Direct, hands-on experience building and training PINNs, FNOs, etc.
  • Mathematical Depth: Exceptional understanding of partial differential equations (PDEs), vector calculus, automatic differentiation (autograd), and numerical optimization algorithms (Adam, L-BFGS).
  • Data Pipelines: Strong proficiency in manipulating spatial or geometric datasets using Python libraries (NumPy, SciPy, Shapely, Open3D, or custom voxelization matrices).

Apply