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Geometric Deep Learning Jobs in New York (NOW HIRING)

Senior Machine Learning Engineer

Manhattan, NY · On-site

$115K - $158K/yr

... geometric and heuristic reasoning * Train and fine-tune models on large, diverse real-world image ... Strong experience using PyTorch, JAX, or other deep learning frameworks to develop and optimize ...

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 ...

Deep experience with graph representation learning, graph transformers (e.g., GCN/GAT/GraphSAGE ... Hands-on experience with frameworks such as PyTorch Geometric (PyG), DGL, GraphGym, GraphML systems ...

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Geometric Deep Learning information

What is geometric deep learning?

Geometric deep learning is a branch of machine learning focused on designing neural networks that operate on non-Euclidean data such as graphs and manifolds. It involves techniques like graph neural networks and requires understanding of both deep learning and geometric structures, often using tools like PyTorch or TensorFlow. Professionals in this field develop models for applications like social network analysis, 3D shape recognition, and molecular modeling.

What is the difference between Geometric Deep Learning vs Data Scientist?

AspectGeometric Deep LearningData Scientist
Required CredentialsAdvanced degrees in computer science, machine learning, or related fieldsBachelor's or master's in data science, statistics, or related fields
Work EnvironmentResearch labs, AI development teams, academiaBusiness analytics, product teams, consulting firms
Industry UsageAI, robotics, computer vision, graph analysisBusiness intelligence, marketing, finance, healthcare

Geometric Deep Learning focuses on applying deep learning techniques to non-Euclidean data like graphs and manifolds, often requiring advanced technical skills. Data Scientists analyze and interpret data to inform business decisions, typically working with structured data and statistical tools. While both roles involve data analysis, Geometric Deep Learning is more research-oriented and specialized in AI development, whereas Data Scientists focus on practical data insights across industries.

What are some common challenges faced when working on Geometric Deep Learning projects, and how can they be addressed?

One common challenge in Geometric Deep Learning is dealing with the complexity and diversity of data structures, such as graphs, point clouds, or manifolds. These data types often require specialized neural network architectures and custom preprocessing steps, which can be more complex than traditional deep learning tasks. Collaboration with domain experts and staying updated with the latest research are crucial for overcoming these obstacles. Additionally, debugging and visualizing the learning process can be more challenging, so employing robust evaluation metrics and visualization tools is highly recommended.

What are the key skills and qualifications needed to thrive as a Geometric Deep Learning Engineer, and why are they important?

To excel as a Geometric Deep Learning Engineer, you need a strong background in mathematics, machine learning, and computer science, typically supported by an advanced degree in a related field. Proficiency with deep learning frameworks like PyTorch or TensorFlow, as well as experience with graph neural networks (GNNs) and geometric data structures, is essential. Strong analytical thinking, problem-solving abilities, and collaborative communication are key soft skills for innovating and working with interdisciplinary teams. These skills are crucial for developing cutting-edge models that leverage geometric data, enabling impactful solutions across domains such as computer vision, biology, and social network analysis.

Which 5 jobs will survive AI?

Geometric Deep Learning specialists are likely to continue in demand due to their expertise in advanced neural network architectures and 3D data processing. Jobs involving complex problem-solving, creativity, and domain-specific knowledge—such as data scientists, AI researchers, software engineers, cybersecurity analysts, and healthcare professionals—are expected to persist as AI tools augment rather than replace these roles. Continuous learning and proficiency with AI frameworks like TensorFlow or PyTorch enhance job security in these fields.

What engineer makes $500,000 a year?

Senior engineers in specialized fields such as software engineering, data engineering, or machine learning engineering can earn $500,000 or more annually, especially with experience, advanced skills, and in high-demand industries like technology or finance. These roles often require expertise in programming, system design, and sometimes leadership or management responsibilities.
What are popular job titles related to Geometric Deep Learning jobs in New York? For Geometric Deep Learning jobs in New York, the most frequently searched job titles are:
What cities in New York are hiring for Geometric Deep Learning jobs? Cities in New York with the most Geometric Deep Learning job openings:
Infographic showing various Geometric Deep Learning job openings in New York as of July 2026, with employment types broken down into 72% Full Time, 26% Part Time, and 2% Contract. Highlights an 73% Physical, 3% Hybrid, and 24% Remote job distribution.
Machine Learning Research Scientist/Senior Machine Learning Research Scientist, Structure and Simula

Machine Learning Research Scientist/Senior Machine Learning Research Scientist, Structure and Simula

Genentech, Inc.

New York, NY • On-site

$168K - $312K/yr

Full-time

Posted 22 days ago


Genentech rating

8.8

Company rating: 8.8 out of 10

Based on 22 frontline employees who took The Breakroom Quiz

11th of 86 rated pharmaceutical


Job description

Advances in AI, data, and computational sciences are transforming drug discovery and development. Roche's Research and Early Development organisations at Genentech (gRED) and Pharma (pRED) have demonstrated how these technologies accelerate R&D, leveraging data and novel computational models to drive impact. Seamless data sharing and access to models across gRED and pRED are essential to maximising these opportunities. The new Computational Sciences Center of Excellence (CoE) is a strategic, unified group whose goal is to harness the transformative power of data and Artificial Intelligence (AI) to assist our scientists in both pRED and gRED to deliver more innovative and transformative medicines for patients worldwide.
The Opportunity
At Roche's AI for Drug Discovery (AIDD) group (Prescient Design), We are building state-of-the-art foundation models and scalable systems to fundamentally transform how large and small molecule therapeutics are designed.
We are looking for exceptional machine learning scientists who want to perform high quality research at the intersection of machine learning, structural biology, and physical sciences to directly accelerate drug discovery.
In this role, as a ML Scientist you will:
• Design and train foundation models at scale to answer challenging research questions in large-molecule drug discovery and protein engineering.
• Leverage massive structural biology and biophysical datasets, building novel architectures that capture complex geometric and physical priors.
• Contribute to publications and present scientific findings at internal and external venues.
• Solve real, pressing problems in drug discovery that enable new portfolio capabilities
In this role, as a Senior ML Scientist you will:
• Design and train foundation models at scale to answer challenging research questions in large-molecule drug discovery and protein engineering.
• Leverage massive structural biology and biophysical datasets, building novel architectures that capture complex geometric and physical priors.
• Contribute to cross-functional research teams across the Computational Sciences Center of Excellence.
• Drive publications and present scientific findings at internal and external venues.
• Solve real, pressing problems in drug discovery that enable new portfolio capabilities
Who you are
• PhD degree in Computational Biology, Computer Science, Chemistry, Physics or related disciplines, with up to 2 years of industry research experience (Scientist) or 2+ years of industry research experience (Senior Scientist).
• Demonstrated experience with Python and deep learning libraries such as PyTorch and/or JAX.
• Demonstrated experience architecting and training deep learning models, particularly utilizing modern approaches (e.g., multimodal representation learning, geometric deep learning, and diffusion models).
• Expertise in molecular dynamics simulations and classical force fields (e.g., AMBER, CHARMM, OpenFF), as well as hands-on experience with molecular modeling tools (e.g., OpenMM, Rosetta).
• Demonstrated research experience, including at least one first author publication (or equivalent).
• Strong communication and collaboration skills
• Public portfolio of computational projects (available on e.g. GitHub)
Relocation benefits are NOT available for this job posting
The expected salary range for this position, based on the primary location of New York for the ML Scientist, is $141,100 - 262,100, and for the Senior ML Scientist, is $160,900 - 298,700. The expected salary range for this position, based on the location of California for the ML Scientist, is $147,600 - 274,000, and for the Senior ML Scientist, is $168,100 - 312,300. Actual pay will be determined based on experience, qualifications, geographic location, and other job-related factors permitted by law. A discretionary annual bonus may be available based on individual and Company performance. This position also qualifies for the benefits detailed at the link provided below.
Benefits
#ComputationCoE
#tech4lifeComputationalScience
#tech4lifeAI
Genentech is an equal opportunity employer. It is our policy and practice to employ, promote, and otherwise treat any and all employees and applicants on the basis of merit, qualifications, and competence. The company's policy prohibits unlawful discrimination, including but not limited to, discrimination on the basis of Protected Veteran status, individuals with disabilities status, and consistent with all federal, state, or local laws.
If you have a disability and need an accommodation in relation to the online application process, please contact us by completing this form Accommodations for Applicants.

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About Genentech

Sourced by ZipRecruiter

A member of the Roche Group, Genentech has been at the forefront of the biotechnology industry for more than 40 years, using human genetic information to develop novel medicines for serious and life-threatening diseases. Genentech has multiple therapies on the market for cancer & other serious illnesses. Please take this opportunity to learn about Genentech where we believe that our employees are our most important asset & are dedicated to remaining a great place to work.

Industry

Scientific research and development services

Company size

10,000+ Employees

Headquarters location

South San Francisco, CA, US

Year founded

1976

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