Qualifications
Responsibilities
Benefits
You Are:
As a Senior Advanced AI Research Engineer, you sit at the boundary between AI systems research and production platform engineering. You investigate hard, open problems in agentic AI — and you close the loop: turning research findings into engineered prototypes, then into platform-ready capabilities that real workloads depend on. You are a strong Python engineer who can move fluently between an experiment and a well-structured service or SDK module. You write research artefacts and production code in the same week, and you understand why both matter.
The Work:
Applied Research & Innovation:
Investigate active innovation frontiers in agentic AI systems — for example, agent memory and knowledge persistence architectures, model selection and inference routing strategies, autonomy and goal-anchoring control planes, and long-horizon task reliability. The specific focus areas evolve with client demand and research opportunity.
Design and execute rigorous benchmarking and evaluation methodologies scoped to production-relevant agentic task profiles — covering dimensions such as tool use, structured output generation, multi-step reasoning, instruction following, and failure recovery.
Investigate efficiency and scalability frontiers — such as inference cost reduction, context management at scale, and retrieval architecture design — that determine whether agent workloads can be served commercially on attainable hardware.
Contribute to external publications, technical reports, and conference submissions that establish thought leadership and build the evidence base for client and platform decisions.
Translational Engineering:
Translate research findings into production-grade implementations: engineered Python services, Node.js/TypeScript SDK modules, or platform-integrated components that other engineers and agent workloads depend on.
Build well-defined provider interfaces and pluggable backends for research components — memory stores, retrieval layers, routing modules — so that experimental implementations can be iterated on and swapped independently of the platform code that depends on them.
Prototype and validate platform-level capabilities — such as inference routing policies, memory management layers, or agent control mechanisms — and carry them through from experiment to integrated, observable system component.
Instrument research prototypes with observability from the start — distributed tracing, cost accounting, and latency metrics — so findings are reproducible and platform integration is low-friction.
Platform Contribution & Integration:
Work alongside platform engineers to integrate validated research capabilities into production systems — contributing well-tested, documented Python and Node.js/TypeScript code through standard engineering workflows including code review, CI, and schema validation.
Identify platform gaps surfaced by research experiments — missing APIs, insufficient observability, constrained interfaces — and raise them as concrete, scoped engineering proposals.
Ensure that research-derived capabilities meet production standards: correct error handling, sensible defaults, documented contracts, and test coverage appropriate to their risk profile.
Collaboration & Communication:
Work closely with platform engineers, product managers, and enterprise architects to align research priorities with real client deployment blockers and platform roadmap needs.
Communicate research findings, architectural trade-offs, and prototype results clearly to both technical peers and non-technical stakeholders — in written artefacts, design reviews, and client-facing sessions.
Mentor junior engineers and researchers on experimental methodology, translational engineering practices, and production-quality code standards.
Travel may be required for this role. The amount of travel will vary from 0 to 100% depending on business need and client requirements.
This role requires working onsite in Mountain View, CA. Applicants must be local or willing to relocate to Mountain View area prior to joining.
Here's what you need
Bonus points if you have
Compensation at Accenture varies depending on a wide array of factors, which may include but are not limited to the specific office location, role, skill set, and level of experience. As required by local law, Accenture provides a reasonable range of compensation for roles that may be hired as set forth below. We anticipate this job posting will be posted until 08/31/2026.
Accenture offers a market competitive suite of benefits including medical, dental, vision, life, and long-term disability coverage, a 401(k) plan, bonus opportunities, paid holidays, and paid time off. See more information on our benefits here:
Role Location Annual Salary Range
California $132,500 to $338,300
Colorado $132,500 to $292,200
Connecticut $132,500 to $292,200
District of Columbia $141,100 to $311,200
Illinois $122,700 to $292,200
Maine $112,900 to $249,000
Maryland $132,500 to $292,200
Massachusetts $132,500 to $311,200
Minnesota $132,500 to $292,200
New York $122,700 to $338,300
New Jersey $141,100 to $338,300
Ohio $122,700 to $270,600
Virginia $122,700 to $311,200
Washington $141,100 to $311,200
Our team will look into this right away.
