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Geometric Deep Learning Jobs in New York (NOW HIRING)

Visible Hands

Machine Learning Engineer

Manhattan, NY · On-site

$150 - $190/hr

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 ...

Root Access, Inc.

Machine Learning Engineer

Manhattan, NY · On-site

$140 - $190/hr

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 ...

Clearview AI, Inc.

Senior Machine Learning Engineer

New York, NY · Remote

$114K - $157K/yr

  • Medical

  • Dental

  • Vision

  • Retirement

  • PTO

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

Clearview AI

Senior Machine Learning Engineer

Manhattan, NY · On-site

$115K - $158K/yr

  • Medical

  • Dental

  • Vision

  • Retirement

  • PTO

... 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 ...

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 ...

Cisco

Applied AI Scientist

Stamford, CT

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

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 are the key skills and qualifications needed to thrive as a geometric deep learning engineer?

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.

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 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 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 job categories do people searching Geometric Deep Learning jobs in New York look for?

The top searched job categories for Geometric Deep Learning jobs in New York 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:

Geometric Deep Learning jobs near you
Infographic showing various Geometric Deep Learning job openings in New York as of August 2026, with employment types broken down into 60% Full Time, 13% Part Time, 9% Temporary, and 18% Contract. Highlights an 66% In-person, 11% Hybrid, and 23% Remote job distribution.

ML Research Scientist (MLRS) - Representation Learning for Molecular AI

Achira

New York, NY • On-site

$164K - $259K/yr

Full-time

Re-posted 17 days ago

Job description

Why Achira
At Achira, we are building a team of world-class scientists, ML researchers, and engineers to move beyond the beaten path and build a frontier lab for Physical AI for molecules. We are actively exploring the next frontier of model architectures for AI x Chemistry: developing world models for the physical microcosm. Our goal is to make biology at the molecular level something that can be learned, predicted, and designed.
At Achira, you'll operate at the frontier scale of massive compute, massive data, and massive ambition. You'll own impactful work end-to-end, from ideation to architecture to deployment on distributed infrastructure. We are a well-funded, talent-dense organization that values rigor, speed, execution, and an ownership mindset. We're looking for new members who share our sense of relentless urgency and are natural collaborators who value team success.
About the Role
We're looking for deep learning researchers who want to build rich representations of atomistic systems to help teach AI about the microscopic world. You will collaborate with domain experts in molecular modeling to develop Achira's next-generation foundation models. You'll work at the intersection of model architecture, data, and training strategy to find new points on the Pareto frontier of representational richness, robustness, accuracy, and speed for microscale world models.
While we prefer candidates willing to work from our San Francisco office, highly skilled candidates may be considered for working from New York City with travel to San Francisco as needed. Both locations are offered as hybrid roles, spending at least some of your time working from the office in collaboration with coworkers. Travel is part of all roles at Achira, both to conferences and corporate on-site activities.
What You'll Do
  • Build a robust pre-, mid-, and post-training curriculum that ensures foundation model performance and impact.
  • Create reinforcement learning strategies to help models focus their capacity where it matters most, especially when the training data doesn't cover the domain of applicability.
  • Develop expressive representations of molecular and atomistic structure and dynamics, including equivariant graph neural networks, geometric transformers, and latent encoders that capture physical symmetries and constraints.
  • Prototype, benchmark, and iterate rapidly to transform research ideas into reusable and scalable components across Achira's ecosystem.
  • Collaborate with physicists and chemists to ensure models are grounded in real physics.
  • Work with research engineers and the infrastructure team to identify where research ideas will need support in order to deliver effective results.

About You
  • Drive to apply modern ML techniques to solve problems at the frontier of the microscopic world.
  • A willingness to follow the data and embrace an empirical approach to the bitter lesson.
  • A pragmatic approach to inductive bias (eg. physical priors, equivariance) in model building.
  • Experience designing, running and analyzing ML experiments at scale.
  • Experience with 3D geometric deep learning.
  • Machine learning researcher with professional experience (post-degree) in an industry setting.
  • Demonstrated research impact through conference talks or publications (in machine learning venues), open-source contributions, or released models.
  • Strong interdisciplinary communication and presentation skills and the ability to translate ideas and concepts to colleagues from non-ML backgrounds.
  • Proficiency in Python and modern ML frameworks (PyTorch, JAX).
  • Experience collaborating on research projects across multi-person teams.
  • Desire and comfort with working on frontier problems in physical AI to invent the blueprint for how they will be tackled.

Nice to Have
Achira values excellent ML researchers from many backgrounds, and expect members of the team to contribute complementary strengths. If the work excites you, we encourage you to apply, even if you hit none of the bonus features listed below!
  • Experience with self-supervised representation learning techniques.
  • Experience with Bayesian deep learning techniques and uncertainty quantification.
  • Experience developing or applying generative models for 3D systems.
  • Experience with equivariant graph neural network architectures (NequIP, MACE, SchNet, PaiNN, or similar).
  • Prior experience working in or with researchers in the domains of computational chemistry, biology, or materials science.
  • Experience working with multi-cloud distributed compute systems.
  • Experience working with multi-site distributed company team.

Apply