Senior Advanced AI Research Engineer
for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). We need to include sections only when posting has real specific content. We have content for responsibilities (The Work), requirements/qualifications (Here's What You Need), benefits (Accenture offers...), pay (Compensation... with salary ranges), schedule? Not mentioned except travel may be required, amount varies, onsite requirement. That could be schedule? Not exactly. Might include "Schedule" if there is info about onsite requirement, travel. Could treat as "Work Location & Schedule". But instruction says only include sections when posting has real specific content. We have location info: "This role requires working onsite in Mountain View, CA. Applicants must be local or willing to relocate...". That's location, not schedule. Could embed in a
under a generic section? Perhaps we can have "About the role" covering intro, then "Responsibilities" (The Work), "Requirements" (Here's What You Need), "Benefits", "Pay". Possibly also "Qualifications" but that overlaps with Requirements. We'll follow typical sections: About the role (company overview), Responsibilities, Requirements, Benefits, Pay. Avoid duplication. We must not include boilerplate like EEO, accommodation request, etc. Remove those. We must preserve all substantive information: company description, role description, responsibilities bullet points, requirements bullet points, benefits list, pay ranges per location, location requirement, travel note. We need to format lists with
- . Use
for prose. We must not repeat job title as heading. No Job ID. We must not include navigation/legal/EEO etc. We must output only HTML fragment. Let's extract content. Company intro: "We Are We are at the forefront of a new era in enterprise AI — one defined not by model capability alone, but by the infrastructure, memory systems, and routing intelligence required to make autonomous AI agents trustworthy and commercially viable at scale. Our Data & AI practice brings together more than 45,000 professionals helping clients design, deploy, and govern AI systems across regulated industries. Our applied research function sits at the intersection of frontier AI research and production engineering — investigating the foundational challenges that will determine whether enterprise agentic AI succeeds or stalls." That's about the company/role context. Could be placed under
About the role
with. Then responsibilities: Under "The Work" there are bullet-like sentences separated by em dash? Actually paragraphs with colon headings: "Applied Research & Innovation:", then bullet-like sentences separated by line breaks? The text: "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." Then "Translational Engineering": bullet-like sentences. Then "Platform Contribution & Integration": bullet-like sentences. Then "Collaboration & Communication": bullet-like sentences. We can combine all responsibilities into a single
- with items for each bullet. Might be many items; we can break into logical groups but still
- . We'll produce list items for each distinct responsibility.
Let's list them:
- 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.
- 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.
- 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.
- 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.
We could put travel/location in a separate section maybe "Work Location & Travel". But instruction only lists certain headings; we could include under "About the role" or maybe under "Responsibilities". Probably better to include in a separate section "Work Location & Schedule" but not in list of allowed headings? The allowed headings include Schedule. Could treat location+travel as Schedule? Not ideal. Maybe we can include a short
under About the role covering location and travel. Now requirements/qualifications: "Here's What You Need" list: - Bachelor's degree (or equivalent minimum 12 years work experience, or minimum 6 years' work experience with Associate's degree) in Computer Science, Computer Engineering, or a related field. - Minimum of 5 years of experience with Python and/or Node.js/TypeScript, building and shipping production backend services, research prototypes, or AI/ML systems. - Minimum of 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. - Bonus points if you have 6+ years of engineering experience across both research and production contexts, with a demonstrated ability to ship research into running systems. - Deep experience in at least one area of applied AI systems research — such as agent memory and knowledge management, inference efficiency and model routing, agentic evaluation methodology, or long-horizon task and autonomy research. - 3+ years of applied research with a track record of translating findings into platform-integrated or published artefacts — prototypes, open-source contributions, internal frameworks, or peer-reviewed papers. - Hands-on experience with async Python (e.g. FastAPI, asyncio), containerisation and Kubernetes, vector and relational databases, and distributed tracing instrumentation (e.g. OpenTelemetry). - Familiarity with modern AI agent framework ecosystems and agent communication protocols — the specific tools matter less than the ability to work across multiple frameworks and evaluate them critically. - Master's or PhD in Computer Science, Computer Engineering, or a related field is strongly preferred. We'll put these under
Requirements
(or Qualifications). Use- .
Benefits: "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: U.S. Employee Benefits | Accenture"
We'll keep the link as is inside
. Pay: "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." Then list of salary ranges per location. We need to preserve all substantive information: include the salary ranges list. Should we include the posting end date? Probably yes as it's substantive. Could be placed in a
under Pay section. We must not include boilerplate like EEO, accommodation request, etc. Remove those. Now produce HTML. Structure:
About the role
We are at the forefront ...
This role requires working onsite in Mountain View, CA. Applicants must be local or willing to relocate to Mountain View area prior to joining. Travel may be required for this role; the amount of travel will vary from 0 to 100% depending on business need and client requirements.
Responsibilities
- ... ...
Requirements
- ... ...
Benefits
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: U.S. Employee Benefits | Accenture
Pay
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.
- California: $132,500 to $338,300
- Cleveland:
- .
Benefits: "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: U.S. Employee Benefits | Accenture"
We'll keep the link as is inside
- . We'll produce list items for each distinct responsibility.
Let's list them:
- 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.
- 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.
- 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.
- 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.
We could put travel/location in a separate section maybe "Work Location & Travel". But instruction only lists certain headings; we could include under "About the role" or maybe under "Responsibilities". Probably better to include in a separate section "Work Location & Schedule" but not in list of allowed headings? The allowed headings include Schedule. Could treat location+travel as Schedule? Not ideal. Maybe we can include a short