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Graph Neural Network Internship Jobs in New York

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 ...

Starr

AI/ML Engineer

Manhattan, NY · On-site

... neural network, random forest, etc.) • 2+ years expert level R or Python analytics experience ... Preferred : • Internship or project experience in AI engineering, machine learning, or a related ...

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Graph Neural Network Internship information

What is a graph neural network internship?

A Graph Neural Network (GNN) Internship is a position designed for students or recent graduates to gain hands-on experience working with GNNs, a type of deep learning model that processes data structured as graphs. Interns typically participate in research, model development, and the application of GNNs to various problems such as social network analysis, recommendation systems, or molecular property prediction. The internship provides opportunities to collaborate with experienced researchers, learn cutting-edge techniques, and contribute to real-world projects involving graph-based machine learning.

What types of projects or tasks can I expect to work on during a graph neural network internship?

As a Graph Neural Network (GNN) intern, you will typically be involved in projects such as developing and optimizing GNN models for real-world datasets, implementing new neural network architectures, and conducting experiments to evaluate model performance. You may also assist with data preprocessing, feature engineering, and collaborating with data scientists and machine learning engineers to integrate GNN solutions into larger systems. Regular tasks include reviewing recent research, documenting findings, and presenting your results to the team. This internship offers an excellent opportunity to deepen your understanding of advanced machine learning methods while gaining hands-on experience in a collaborative research-focused environment.

What are the key skills and qualifications needed to thrive as a graph neural network intern, and why are they important?

To thrive as a Graph Neural Network Intern, you need a solid background in machine learning, data science, and programming languages such as Python, often supported by coursework or research experience in deep learning and graph theory. Familiarity with frameworks like PyTorch Geometric, TensorFlow, and libraries such as NetworkX, along with experience using Jupyter Notebooks and Git, is typically expected. Strong analytical thinking, problem-solving skills, and effective communication help interns collaborate with research teams and convey complex ideas clearly. These skills and qualifications are essential for contributing to cutting-edge AI projects and advancing research in graph-based machine learning.

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Graph Neural Network Internship jobs near you
Infographic showing various Graph Neural Network Internship job openings in New York as of August 2026, with employment types broken down into 1% As Needed, 80% Full Time, 14% Part Time, and 5% Contract. Highlights an 93% Physical, 2% Hybrid, and 5% 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 19 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