Principal Graph Analytics Engineer

GM • Milford, MI

12h • Hybrid

About The Position

We are seeking a Principal Engineer with deep expertise in Graph Theory to lead the design of advanced algorithms and data structures for complex, large-scale systems within the Vehicle Systems & Embedded Engineering (VSEE) organization, supporting our transition to a next-generation Software Defined Vehicle (SDV) architecture. This role is ideal for a hands-on technical leader with a PhD in Mathematics, Computer Science, or related field with a strong research record in graph theory and a track record of successfully applying that work to real-world engineering problems. You will be the principal technical authority for how we model, analyze, and exploit large-scale vehicle, network, and backend graphs, guiding top-level architectural decisions that shape how the broader technical community applies graph-based analysis to solve difficult SDV engineering challenges. You will shape technical strategy, mentor senior engineers, and partner with cross-functional stakeholders to deliver high-impact solutions grounded in rigorous mathematics. This is not a pure research role; success requires translating advanced theoretical work into scalable, maintainable production systems and influencing how multiple teams adopt and apply these methods. While not expected to be the primary implementer for all systems, you will remain technically hands-on through prototyping, reviews, and critical-path contributions. In the context of SDV, example problem spaces include: Analyzing software, service, and configuration dependency graphs to understand impact, risk, blast radius, and safe rollout strategies (including OTA updates) Optimizing compute, network, and storage placement and performing fault and safety analysis using graph-based representations of in-vehicle and offboard systems Building and querying graph-based knowledge and lifecycle models that connect requirements, architecture, implementation, verification, and fleet data for SDV

Requirements

• Master's in Mathematics, Computer Science, Operations Research, or a closely related field, with a strong focus on graph theory.
• 8+ years of post-education experience in algorithm design and large-scale systems, with a proven track record of applying graph-theoretic concepts to real engineering problems and taking models from prototype to production.
• Deep expertise in graph-based algorithms and optimization, including: Core graph-based algorithms (e.g., paths, flows, connectivity, matchings, spanning trees, graph search, spectral methods). Network and combinatorial optimization, with practical approaches to NP-complete and NP-hard problems (e.g., relaxations, approximation algorithms, heuristics). Analysis of algorithms: time/space complexity and probabilistic/approximation analysis.
• Strong software engineering skills: Proficiency in at least one systems or production language (e.g., C++, Rust, Java, Go) and one high-level language (e.g., Python). Experience with data structures, distributed systems concepts, and performance optimization.
• Demonstrated ability to: Lead multi-team, multi-quarter initiatives as the primary technical owner. Communicate complex mathematical ideas clearly to both technical and non-technical stakeholders. Make pragmatic trade-offs between theoretical optimality and engineering constraints.

Nice To Haves

• PhD in Mathematics, Computer Science, Operations Research, or a closely related field, with a strong focus on graph theory.
• 10+ years of post-education experience in algorithm design and large-scale systems, with a proven track record of applying graph-theoretic concepts to real engineering problems and taking models from prototype to production.
• Experience building or working with graph databases, graph processing frameworks, or large-scale graph analytics platforms.
• Contributions to peer-reviewed publications in graph theory, algorithms, or related areas.
• Experience in one or more domains where graphs are central, such as: Routing, logistics, and network design Recommender systems and knowledge graphs Dependency analysis and configuration management Reliability, resilience, or fault propagation in complex networks
• Prior experience as a Principal / Staff / Distinguished Engineer or equivalent, with: A track record of raising the technical bar across teams. Experience mentoring PhD-level talent and senior engineers.

Responsibilities

• Set graph-based technical direction : Own the graph-theoretic architecture and strategy for SDV-relevant systems and platforms, including standards for correctness, scalability, and reliability.
• Apply advanced graph theory to SDV problems: Formulate key SDV challenges such as graph-based modeling or combinatorial optimization problems and drive advanced graph-based algorithms (including for NP-hard classes) from prototype to production.
• Design and review graph-based systems: Lead end-to-end designs that use graph-based models across data, compute, and APIs, partnering with platform and infrastructure teams to ensure workloads are efficient, observable, and resilient.
• Elevate the graph-based technical community: Mentor senior and staff engineers, align cross-functional teams on problem framing and solution approaches, and provide internal and external thought leadership (design reviews, talks, publications).

Benefits

• GM offers a variety of health and wellbeing benefit programs. Benefit options include medical, dental, vision, Health Savings Account, Flexible Spending Accounts, retirement savings plan, sickness and accident benefits, life insurance, paid vacation & holidays, tuition assistance programs, employee assistance program, GM vehicle discounts and more.

What This Job Offers

• Job Type: Full-time
• Career Level: Principal