Senior Advanced AI Research Engineer

Accenture•St. Louis, MO

20d•$132,500 - $338,300•Onsite

About The Position

The Senior Advanced AI Research Engineer works at the intersection of AI systems research and production platform engineering. The role investigates open problems in agentic AI, turns findings into engineered prototypes, and develops platform-ready capabilities for real workloads. It combines research artifacts and production code, with an emphasis on strong Python engineering and the ability to move between experiments and well-structured services or SDK modules. The role explores areas such as agent memory and knowledge persistence, model selection and inference routing, autonomy and goal-anchoring control planes, long-horizon task reliability, production-relevant evaluation, and efficiency and scalability. It also translates validated research into production-grade services, SDK modules, and platform components; integrates these with platform systems; and communicates findings and trade-offs to technical and non-technical audiences. The position is onsite in Mountain View, California. Applicants must be local or willing to relocate to the Mountain View area before joining. Travel may range from 0% to 100%, depending on business needs and client requirements.

Requirements

Bachelor's degree or equivalent minimum 12 years of work experience, or minimum 6 years of work experience with an Associate's degree, in Computer Science, Computer Engineering, or a related field.
Minimum 5 years of experience with Python and/or Node.js/TypeScript building and shipping production backend services, research prototypes, or AI/ML systems.
Minimum 5 years of hands-on experience with AI or ML systems, such as large language models, agent frameworks, inference serving, or retrieval and memory architectures.

Nice To Haves

6 or more years of engineering experience spanning research and production contexts, with demonstrated ability to ship research into running systems.
Deep experience in an applied AI systems research area, such as agent memory and knowledge management, inference efficiency and model routing, agentic evaluation methodology, or long-horizon task and autonomy research.
3 or more years of applied research and a track record of translating findings into platform-integrated or published artifacts, such as prototypes, open-source contributions, internal frameworks, or peer-reviewed papers.
Hands-on experience with async Python (such as FastAPI or asyncio), containerization and Kubernetes, vector and relational databases, and distributed tracing instrumentation (such as OpenTelemetry).
Familiarity with modern AI agent framework ecosystems and agent communication protocols.
Master's or PhD in Computer Science, Computer Engineering, or a related field is strongly preferred.

Responsibilities

Investigate emerging agentic AI research areas, including memory and knowledge persistence, model selection and inference routing, autonomy and goal-anchoring controls, and long-horizon task reliability.
Design and conduct rigorous benchmarks and evaluation methodologies for production-relevant agentic tasks, including tool use, structured output, multi-step reasoning, instruction following, and failure recovery.
Investigate efficiency and scalability, including inference cost reduction, context management, and retrieval architecture.
Contribute to external publications, technical reports, and conference submissions.
Translate research findings into production-grade Python services, Node.js/TypeScript SDK modules, or platform-integrated components.
Build provider interfaces and pluggable backends for components such as memory stores, retrieval layers, and routing modules.
Prototype and validate platform capabilities and carry them from experiment through integration as observable system components.
Instrument research prototypes with distributed tracing, cost accounting, and latency metrics.
Collaborate with platform engineers to integrate validated capabilities into production systems, contributing documented and tested code through code review, CI, and schema validation.
Identify platform gaps surfaced by research and propose scoped engineering improvements.
Ensure research-derived capabilities have appropriate error handling, defaults, documented contracts, and test coverage.
Align research priorities with client deployment blockers and platform roadmap needs in collaboration with platform engineers, product managers, and enterprise architects.
Communicate findings, architectural trade-offs, and prototype results in written materials, design reviews, and client-facing sessions.
Mentor junior engineers and researchers in experimental methodology, translational engineering, and production-quality coding practices.
Travel as required by business needs and client requirements, potentially from 0% to 100%.