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Graph Neural Network Jobs in New York (NOW HIRING)

Tatari

Principal Product Manager, AI/ML

New York, NY · On-site

$190K - $225K/yr

Proven track record building and scaling recommendation systems and/or graph neural network (GNN) products in production, at meaningful user/data scale. * Experience shaping product strategy and ...

Tatari

Principal Product Manager, AI/ML

New York, NY · Hybrid

$190K - $225K/yr

Proven track record building and scaling recommendation systems and/or graph neural network (GNN) products in production, at meaningful user/data scale. * Experience shaping product strategy and ...

Cisco

Applied AI Scientist

Stamford, CT · On-site

Our work spans networking, security, observability, and customer experience - designing and ... Large-scale graph representation learning and Graph Neural Networks (GNNs) (e.g., GCN/GAT/GraphSAGE ...

Prudential

Lead, Machine Learning Engineer

Newark, NJ · On-site

$107K - $141K/yr

... Neural network, NLP, computer vision, and predictive analytics * Model Performance Management ... Understanding big data ecosystems, relational, NOSQL and graph databases, unstructured and semi ...

All JobsGraph Neural Network JobsGraph Neural Network Jobs in New York

Graph Neural Network information

What are the key skills and qualifications needed to thrive in the graph neural network position, and why are they important?

To excel as a Graph Neural Network Engineer, you need a strong background in machine learning, graph theory, neural networks, and proficiency in programming languages such as Python. Familiarity with deep learning frameworks like PyTorch or TensorFlow, and experience with specialized libraries such as DGL or PyTorch Geometric are highly valued. Excellent problem-solving skills, teamwork, and the ability to communicate complex concepts to both technical and non-technical stakeholders will help you stand out. These combined abilities enable professionals to design, implement, and deploy cutting-edge GNN models that address complex, real-world data-structure challenges across various industries.

What does a typical project workflow look like for a graph neural network engineer?

A typical project workflow for a Graph Neural Network Engineer involves collaborating with data scientists and domain experts to understand the problem, preprocessing and visualizing graph-structured data, and selecting appropriate model architectures. The role often includes building, training, and evaluating GNN models, iterating on hyperparameters, and deploying models to production environments. Throughout the process, you will engage in code reviews, document findings, and present results to stakeholders. Teamwork and effective communication are essential, as projects frequently require close collaboration with researchers, software engineers, and business units to ensure solutions meet practical needs and performance goals.

What is a graph neural network?

A Graph Neural Network (GNN) job typically involves designing, implementing, and optimizing neural network models that operate on graph-structured data. Professionals in this role apply GNNs to tasks like recommendation systems, fraud detection, social network analysis, and molecular property prediction. Responsibilities often include data preprocessing, model architecture selection, training, evaluation, and deployment. Strong knowledge of machine learning, deep learning frameworks (such as PyTorch or TensorFlow), and graph theory is essential.

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Graph Neural Network jobs near you
Infographic showing various Graph Neural Network job openings in New York as of July 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution.

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

Achira

New York, NY • On-site

$164K - $259K/yr

Full-time

Re-posted 3 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