Senior AI R&D Engineer - AAET

SM-Energy Company•Denver, CO

6h•$180,000 - $220,000

About The Position

SM Energy is seeking a hands-on AI R&D Engineer to explore, evaluate, and prototype emerging AI capabilities ahead of the mainstream. This individual contributor role sits in a small, two-person R&D pod within our Advanced Analytics and Emerging Technologies team, reporting to the team's IT Manager. The pod's job is to run ahead of the rest of the technology organization — spotting what's coming (agentic AI was visible well before it was widely adopted), proving it against real SM Energy workflows and data, and putting working capabilities in the hands of the broader organization to try. This is a builder's role for a natural tinkerer: someone with real fluency in the modern AI stack who experiments with new tools because they can't help it. Ownership often may end at proof of concept — validated prototypes transition to the pod's Senior AI Engineer and delivery teams to be carried into production, keeping this role free to return to the frontier. This role requires a flexible personality. The agenda for what's next is set in conjunction with leadership and can be highly varied depending on enterprise needs. Projects can range from development of enterprise-level LLM skills to testing out loop engineering techniques, and more. Upstream oil & gas experience is strongly preferred. Equally important is judgment about fit. There is a real difference between AI built to be sold as a product and AI that makes sense for enterprise adoption — this role requires seeing both sides and recognizing what fits a company of SM Energy's size. The pod moves fast, tests aggressively in a sandbox environment, and comes back with honest recommendations: sometimes "adopt this," and just as often "here's what it can do — but I don't recommend it at our scale." Not everything the pod builds will be implemented, and a well-supported "no" is as valuable an outcome as a successful handoff. This is not a productization, patent, or IP-development role.

Requirements

Strong software engineering fundamentals: Python and/or TypeScript, APIs and integration patterns, version control, cloud platforms (Azure familiarity a plus)
Demonstrated hands-on experience with the modern AI stack, such as: LLM APIs and SDKs, agentic frameworks and orchestration, Model Context Protocol (MCP) or similar tool-use protocols, retrieval-augmented generation, and evaluation and testing approaches for AI systems
A visible record of building — side projects, open-source contributions, shipped experiments, or equivalent demonstrated work is weighted more heavily than years of experience
Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent demonstrated technical experience
3+ years of hands-on software or AI engineering experience; demonstrated capability is weighted over tenure

Nice To Haves

Familiarity with enterprise AI platforms and developer tooling (e.g., Claude, Azure AI services, agent development kits) preferred
Awareness of enterprise data platforms (e.g., Snowflake) and how AI capabilities connect to governed data preferred
Relevant coursework, certifications, or demonstrated work in AI/ML technologies preferred
5+ years of upstream oil & gas or broader energy industry experience strongly preferred
A sustained, self-directed record of experimentation with emerging AI technologies
Experience presenting technical work to non-technical or executive audiences preferred

Responsibilities

Continuously scan and evaluate the emerging AI landscape — models, agent frameworks, protocols, developer tooling, and vendor offerings — and maintain a working point of view on what matters for SM Energy and when
Rapidly build working proofs of concept that demonstrate emerging AI capabilities against real SM Energy workflows, data, and use cases
Transition validated prototypes to the pod's Senior AI Engineer and delivery teams, providing defined handoff support before returning focus to new exploration where possible
Present findings through live demonstrations, briefings, and written summaries for audiences ranging from engineering teams to executive leadership
Stand up early-access capabilities, sandboxes, and experimental tooling for the broader technology organization to evaluate hands-on
Partner with team leadership and business stakeholders to prioritize the exploration agenda — balancing directed initiatives, executive and business requests, and self-directed investigation — then run independently and return with clear recommendations
Engage directly with AI vendors and platform providers — evaluating roadmaps, participating in early-access and preview programs, and representing SM Energy's technical requirements
Distinguish durable capability shifts from hype, and deliver honest, enterprise-calibrated recommendations — including negative results ("we tested it; it's not ready") and fit-for-scale calls ("it works, but not for an organization our size")
Document experiments, findings, and reusable patterns so knowledge compounds across the pod and the broader team
Other duties as assigned