Among computer and information research scientists, AI is already used for a large share of the tasks. Measured exposure for computer and information research scientists: 34% (high).

Share of this job’s tasks that AI is already used for at work, from real usage. It is not a forecast of job loss, and a low number means no use seen yet. Source: Anthropic Economic Index.

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About the job

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.

Essential Roles and 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

Key Competencies

Technical Curiosity & Self-Direction — Independently identifies what to learn next and learns it. Maintains a personal practice of experimentation with new AI tools, models, and frameworks well ahead of formal requirements.

Rapid Prototyping — Moves from concept to working demonstration in days or weeks, not months. Comfortable building rough, functional prototypes and iterating fast rather than engineering for production.

Technology Judgment — Evaluates emerging capabilities with skepticism and rigor. Separates genuine capability shifts from marketing, and understands the difference between AI built to sell as a product and AI suited for enterprise adoption. Calibrates recommendations to what fits an organization of SM Energy's size, and can articulate why something matters — or doesn't — for the business.

Communication & Demonstration — Translates frontier technology into clear, concrete terms for non-technical audiences. Builds demos that make capabilities tangible. Comfortable presenting to executive leadership.

Managing Ambiguity — Operates productively without a defined roadmap. Comfortable exploring problem spaces where the destination, tooling, and success criteria are all evolving.

Translation to Business Context — Connects emerging technology to real operational and business workflows. Applies and deepens domain understanding of upstream oil & gas to ground exploration in use cases that matter to the business.

Collaboration & Handoff Discipline — Works effectively in a two-person pod and hands off work cleanly — documenting context, supporting transition, and letting go — including when the outcome is a recommendation not to proceed.

Technology Knowledge

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

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

Education

Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent demonstrated technical experience

Relevant coursework, certifications, or demonstrated work in AI/ML technologies preferred

Typical Experience

3+ years of hands-on software or AI engineering experience; demonstrated capability is weighted over tenure

5+ years of upstream oil & gas or broader energy industry experience strongly preferred

10-15 years of experience in the Oil and Gas Industry

A sustained, self-directed record of experimentation with emerging AI technologies

Experience presenting technical work to non-technical or executive audiences preferred

SM Energy offers competitive compensation and benefits programs which include, but are not limited to, variable pay, health care coverage, retirement plan, protection coverage, time off and leave programs, training and development opportunities and a range of allowances connected to specific work situations. Details are available at Careers :: SM Energy Company (SM) (sm-energy.com) .

Applications will be accepted on an ongoing basis until the position is filled.