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Graph Neural Network Jobs (NOW HIRING)

dsm-firmenich

$100K - $140K/yr

Apply diverse neural network architectures and multi-objective optimization algorithms to solve ... You apply modern tools such as git, generative AI and agentic workflows, graph and relational data ...

Avride

Lead AI Infrastructure Engineer

Austin, TX · On-site

$101K - $133K/yr

The vast part of the execution graph is implemented as a chain of neural network operations. The onboard mode relies on stable latencies of the inference of those networks, while in simulation we ...

GMPPU

ERS ML Design Optimization Engineer

Concord, NC · On-site

Build neural network surrogates (e.g., PINNs, graph nets) emulating ERS physics across thermal, electrical, degradation behaviors. * Implement tool-agnostic GA/BO optimization loops for multi ...

quadric, Inc

Deep Learning Compiler Engineer

Burlingame, CA · On-site

$110K - $270K/yr

Unlike other NPUs or neural network accelerators in the industry today that can only accelerate a portion of a machine learning graph, the Quadric GPNPU executes both NN graph code and conventional C ...

quadric, Inc

Design Verification Intern

Burlingame, CA · On-site

$45 - $60/hr

Unlike other NPUs or neural network accelerators in the industry today that can only accelerate a portion of a machine learning graph, the Quadric GPNPU executes both NN graph code and conventional C ...

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How much do graph neural network jobs pay per hour?

As of Sep 15, 2026, the average hourly pay for graph neural network in the United States is $26.64, according to ZipRecruiter salary data. Most workers in this role earn between $22.60 and $29.09 per hour, depending on experience, location, and employer.

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.

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 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.

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Graph Neural Network jobs near you
Infographic showing various Graph Neural Network job openings in the United States as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 80% Full Time, 11% Part Time, and 7% Contract. Highlights an 89% Physical, 2% Hybrid, and 9% Remote job distribution, with an average salary of $55,420 per year, or $26.6 per hour.

Associate Director - ERCOT Market Subject Matter Expert (Part-Time/ Contract)

Remote

Nagarro
IT Services • 1 - 5K employees
Learn more about Nagarroexternal

Full-time

Re-posted 4 days ago

Nagarro rating

6.6

Company rating: 6.6 out of 10

Based on 5 frontline employees who took The Breakroom Quiz

Job description

Company Description

We are a Digital Product Engineering company that is scaling in a big way! We build products, services, and experiences that inspire, excite, and delight. We work at scale - across all devices and digital mediums, and our people exist everywhere in the world (18000+ experts across 40 countries, to be exact). Our work culture is dynamic and non-hierarchical. We are looking for great new colleagues. That is where you come in!

Role Overview

Nagarro is seeking an experienced ERCOT Market Subject Matter Expert to support a focused Proof of Concept for congestion driver attribution across the ERCOT nodal network.

The engagement aims to develop a Graph Neural Network-based solution that combines physical power-system fundamentals, market participant behaviour, and ERCOT transmission-network topology to identify and explain the key drivers of congestion.

The ERCOT SME will work closely with Graph ML engineers, data engineers, power-market analysts, and project leadership to ensure that the analytical models are grounded in ERCOT market principles and produce interpretable, actionable outputs.

Objectives of the Role

The ERCOT Market SME will be responsible for:

  • Providing domain expertise on ERCOT market operations, congestion mechanisms, and nodal pricing.
  • Guiding the interpretation of transmission constraints, shift factors, shadow prices, binding intervals, and congestion propagation.
  • Supporting the definition and validation of congestion-driver categories.
  • Translating ERCOT market behaviour into functional and analytical requirements for the data science and Graph ML teams.
  • Ensuring that model outputs are understandable and relevant to power-market analysts and trading stakeholders.
  • Validating congestion attributions against independently verifiable historical ERCOT market events.
  • Supporting the assessment of the model's readiness for future nodal price-forecasting use cases.
Job Description

ERCOT Market and Congestion Expertise

  • Explain ERCOT nodal market design, settlement-point pricing, transmission congestion, and Locational Marginal Pricing components.
  • Analyse binding transmission constraints, contingency conditions, shift-factor exposures, shadow prices, and historical binding hours.
  • Support the identification of congestion caused by generation outages, renewable oversupply, load concentration, transmission outages, contingencies, and market participant behaviour.
  • Interpret participant-level and aggregated bid-and-offer disclosures within the context of congestion and shadow-price formation.

Model and Data Support

  • Work with the Graph ML team to define appropriate node, edge, transmission, market, and temporal attributes for the ERCOT network graph.
  • Review the use of ERCOT transmission models, shift-factor matrices, contingency files, line ratings, outage feeds, market disclosures, and historical congestion information.
  • Define a practical taxonomy for congestion-driver attribution.
  • Support the separation and interpretation of physical and behavioural contributors to observed congestion.
  • Help establish business rules, assumptions, thresholds, and domain constraints for model development.

Validation and Interpretation

  • Validate model-generated congestion attributions against known historical events, including documented unit outages, transmission outages, curtailment events, and contingency-driven constraints.
  • Review propagation paths and assess whether identified node and interface impacts are electrically and commercially plausible.
  • Evaluate the accuracy and usefulness of model explanations, confidence scores, and shadow-price attribution.
  • Participate in back-testing reviews and assist in comparing model performance against baseline approaches.
  • Ensure that model outputs can be interpreted by market analysts without requiring advanced machine-learning knowledge.

Stakeholder Collaboration

  • Collaborate with the client's trading, analytics, and power-market teams during architecture reviews and validation checkpoints.
  • Participate in regular working sessions with Nagarro's Graph ML engineers, data engineers, and project leadership.
  • Present findings, assumptions, limitations, and recommendations in clear business and market terminology.
  • Support risk identification and timely escalation of issues related to market data, modelling assumptions, or ERCOT-specific interpretation.
Qualifications
  • Demonstrated professional experience working with the ERCOT wholesale electricity market, with a strong understanding of congestion modelling and nodal market operations.
  • Practical knowledge of:
  • Locational Marginal Pricing and congestion components
  • Shift factors and transmission-interface exposure
  • Binding constraints, contingencies, and constraint shadow prices
  • Day-Ahead and Real-Time Market operations
  • Generation, load, and transmission outages
  • ERCOT bid-and-offer disclosures
  • Congestion Revenue Rights and related market information
  • Experience analysing historical congestion events and identifying the underlying physical, transmission, or market-behaviour drivers.
  • Experience in one or more areas such as power-market trading, congestion analytics, forecasting, market simulation, production-cost modelling, power-flow analysis, or transmission-network modelling.
  • Familiarity with ERCOT datasets, including network-model files, outage reports, market disclosures, constraint reports, and other publicly available market information.
  • Ability to translate complex power-market concepts into clear requirements and validation criteria for data engineering, analytics, and machine-learning teams.
  • Experience collaborating with data scientists, machine-learning engineers, trading teams, or advanced analytics stakeholders.
  • Strong analytical, communication, and stakeholder-management skills.
  • Exposure to nodal price forecasting, explainable AI, graph-based analytics, or AI-led power-market applications would be advantageous.
Additional Information

Disclaimer: Nagarro is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. All qualified applicants will be afforded equal employment opportunities without discrimination based on race, creed, color, national origin, sex, age, disability, or marital status.

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