(USA) Distinguished, Data Scientist - Agentic AI Systems Engineering & Model Post-Training

Position Summary...

What you'll do...

• Own the architecture, implementation, evaluation, launch, and operation of complex, autonomous multi-agent applications that tackle our highest-value enterprise initiatives—from the first working full-stack proof of concept through dependable, production-scale service.
• Build systems in which specialized agents can reason, plan across long horizons, coordinate work, use tools, retrieve enterprise knowledge, maintain secure and durable session and workflow state, manage context growth, ask for human approval when needed, and cancel, resume, replay, or recover safely from failure.
• Build a policy-first agent runtime and control plane with deterministic allow, deny, and ask decisions; least-privilege tool and data access; scoped identity and authorization; auditable human approvals; bounded subagent delegation; and safe cancellation, user steering, and retry behavior for long-running work.
• Own the full product path as a full-stack systems engineer: agent and model logic, backend services and APIs, the data layer (database and schema design, data modeling, migrations, and data integrations), telemetry, monitoring, and thoughtful React/TypeScript experiences for associates and operators. Where the workflow demands it, design equally usable AI-native CLI/TUI or headless, structured-output interfaces that support automation, operations, and CI/CD-style integration.

• Build and own the Discovery Loop within our Autonomous Supply Chain Engine. This is the flagship capability this role exists to build: you will own its execution model, its knowledge and context layer, its evaluation loop, and the learning path that turns human feedback and business outcomes into measurable improvement.
• Partner directly with supply chain experts, scientists, data owners, operations, and engineering stakeholders to discover the real workflow, prototype rapidly with real users, measure value and risk, and harden successful solutions without losing speed.

• Own the design and build of advanced multi-agent harnesses, runtimes, and orchestration capabilities using frameworks such as Pydantic AI, LangGraph, LangChain, AutoGen, or LlamaIndex, or purpose-built custom infrastructure. Building your own runtime where that is the right call is a strength, not a gap. Establish typed contracts, testability, scalability, operability, and developer ergonomics as non-negotiable platform properties.
• Engineer explicit, inspectable agent execution. Design graph-based, state-machine-based, event-driven, planner/executor, or equivalent execution models as the problem warrants, with explicit state, typed dependencies and handoffs, conditional branching, fan-out and fan-in to subagents, retry and repair paths, loop detection, cost and time budgets, and hard termination conditions. Design external verification into the system—test runners, execution results, transaction outcomes, deterministic checks, and expert human review—because a model reviewing its own output is not verification.
• Build governed knowledge and context infrastructure for durable agent memory. Choose and combine the right representations—knowledge and context graphs, vector retrieval, relational and temporal models, or hybrids—with an explicit schema and ontology treated as a product contract. Own entity resolution and conflict handling, construction and enrichment pipelines from structured and unstructured sources, provenance and temporal validity, schema validation and evolution, and multi-hop retrieval that answers questions flat retrieval cannot.
• Build and operate agent skills, tool adapters, hooks and extension points, Model Context Protocol (MCP) clients and servers, structured outputs, function calling, enterprise APIs, identity, and secrets management. Own MCP lifecycle and reliability end to end: secure configuration and authentication, per-agent tool binding, schema compatibility, tool discovery, timeouts, health monitoring, retries, circuit breaking, quarantine, cleanup, failure isolation, and auditable operations.
• Create governed extension ecosystems for agents, skills, commands, plugins, hooks, tool adapters, and reusable workflows. Define stable contracts, compatibility and versioning strategies, secure installation and update paths, isolation boundaries, rollback behavior, and observability so extensibility does not become an uncontrolled code-execution surface.
• Treat agent quality as an engineering discipline: golden tasks, offline benchmarks, online experiments, adversarial and regression testing, failure analysis, measurable quality thresholds, and release gates.
• Establish practical AgentOps / LLMOps practices for prompt and tool versioning, tracing, evaluation datasets, workflow reliability, cost and latency controls, incident learning, and continuous improvement of long-running autonomous systems. Instrument the system so engineers and operators can answer, with evidence, what the agent did, why it was permitted, which model, tool, and policy version was involved, what data and integrations were used, what it cost, where it failed, and how to reproduce or remediate the outcome safely.

• Improve model reasoning and quality through hands-on post-training: reinforcement learning (RLHF/RLAIF), preference optimization, supervised fine-tuning, distillation, and reward and grader design to improve reasoning, tool-use reliability, and domain-specialized behavior across frontier models and smaller, domain-specialized models.
• Build the model-improvement flywheel. Turn production interaction traces, tool-use trajectories, human feedback, and successful and failed reasoning paths—together with Walmart's proprietary enterprise and operational data, synthetic data, and curated evaluation sets—into governed training and evaluation datasets. Use them to post-train, distill, evaluate, and redeploy increasingly capable domain-specialized models back into the agentic systems, so the platform you build continuously improves the models that power it. Own the data governance, provenance, privacy, and access controls that make this safe at enterprise scale.
• Design provider-aware, model-agnostic execution and routing layers that account for model capabilities—including multimodal inputs and outputs across text, images, and documents—context limits, structured outputs, streaming behavior, credentials, rate limits, transient failures, and explicit quality, latency, and cost trade-offs.

• Design systems on Google Cloud Platform (GCP) and Walmart internal technologies that are secure, fault tolerant, cost-aware, observable, highly available, and low latency.
• Build responsible enterprise-agent behavior through least-privilege access, explicit authorization boundaries, complete audit trails, data-protection controls, defenses against prompt injection and untrusted instructions, approval controls for consequential actions, and reversible recovery paths.
• Design distributed data, streaming, and telemetry systems using technologies such as Kafka, Flink, Spark, OpenTelemetry, and Grafana; use operational signals to improve product quality, reliability, and user trust.
• Set quality, performance, and release standards with layered unit, integration, API, workflow, and user-journey testing; adversarial safety testing; benchmark- and profile-driven performance work; CI/CD quality gates; reliable rollout and rollback practices; and incident-response readiness.
• Deliver accessible associate-facing experiences that meet applicable Walmart accessibility standards, including WCAG 2.2 AA where applicable.

• Set a multi-year technical direction for shared agent foundations and the products built on them while staying hands-on—owning architecture, critical-path code, technical spikes, design reviews, and production incidents.
• Raise the engineering bar through clear technical writing, rigorous code and architecture reviews, reusable platform capabilities, and hands-on mentoring of Principal, Staff, and senior engineers.
• Make consequential technical trade-offs based on evidence, security, maintainability, measurable outcomes, and user needs—not novelty for novelty's sake.
• Turn ambitious ideas into responsibly operated products through high standards, constructive collaboration, direct communication, and relentless follow-through.

• 10+ years of hands-on software engineering and architecture experience; 12+ years preferred. Demonstrated Distinguished, Fellow, Principal, Senior Staff, or equivalent scope: you have led difficult technical work across organizations and taken direct, hands-on ownership of shipping and operating complex systems.
• Exceptional end-to-end builder ownership. You are capable of personally building the critical path of a frontier-grade, full-stack agentic platform end to end—agent runtime, CLI/TUI, web frontend, backend services and APIs, and the underlying data layer—while creating leverage for the engineers building alongside you. You can take an ambiguous operational problem from first principles to a working prototype, make the trade-offs clear, earn stakeholder trust, and do the unglamorous hardening required to run a reliable production service.
• Co-equal depth in agentic AI engineering, software engineering, and machine learning engineering: ML and LLM fundamentals, experimental design, retrieval and ranking, model evaluation, production trade-offs, and sound judgment about when an LLM is not the right solution.
• Deep, hands-on experience with model post-training—reinforcement learning (RLHF/RLAIF), preference optimization, supervised fine-tuning, distillation, and reward and grader design—applied to improve reasoning, tool-use reliability, and domain-specialized model behavior, including creating smaller domain-specialized models.
• Deep production experience with stateful, policy-controlled agent runtimes and tool-using multi-agent systems for autonomous long-horizon work: durable session lifecycle, context-window management, retrieval and RAG, guardrails, evaluation systems, secure delegation, safe tool use, interruption handling, and recovery—built on established orchestration frameworks or on custom infrastructure of your own design, including MCP and production tool and integration lifecycle management.
• Broad applied ML depth across several areas such as reinforcement learning, multimodal modeling, retrieval and ranking, forecasting, optimization and operations research, experimentation, or model evaluation—together with production ML lifecycle and MLOps practices, and judgment about when a classical model beats an LLM.
• Expert Python plus strong production backend capability in one or more of Go, Java, or TypeScript, with true full-stack capability across React/TypeScript frontend, APIs, and the data layer.
• Demonstrated depth in distributed systems and production reliability, including streaming and batch platforms, Kubernetes/Docker, CI/CD, telemetry and observability, SLOs/SLIs, availability, latency and cost management, secure enterprise integrations, incident learning, and layered testing.
• Deep fluency with AI-native software-development workflows and coding agents such as Claude Code, Codex, Cursor, Copilot, or equivalent tools, applied with rigorous engineering judgment, code review, evaluation, and testing.
• Demonstrated evidence of exceptional hands-on depth and organization-level impact. We accept many forms of proof: shipped systems you can speak to in depth, an architecture and code walkthrough, open-source contributions where disclosure is permitted, technical demonstrations, publications, or patents. We recognize that much frontier work is confidential; a public portfolio is welcome but is not required. Be prepared to describe work that measurably raised the capability of teams beyond your own, and how that impact was sustained.
• High agency, technical creativity, intellectual rigor, and sustained execution. You are self-motivated and proactive in ambiguity, and you are an approachable, ethical teammate who listens well, creates momentum, and brings partners with you.

• Applicants must meet one of the corporate eligibility paths below. The role-specific hiring bar above—including 10+ years of hands-on engineering and architecture experience—governs selection for this Distinguished position.
• Master's degree in Computer Science, Computer Engineering, Computer Information Systems, Software Engineering, Machine Learning, Artificial Intelligence, or a related area and 4 years of experience in software engineering or a related area; or
• 8 years of experience in software engineering or a related area.

• Ph.D. in Computer Science, Computer Engineering, Machine Learning, Artificial Intelligence, or a closely related discipline.
• Experience building developer-facing agentic platforms, SDKs, or coding and automation agents used by other engineers.
• Graph and knowledge-representation depth is a strong plus. Knowledge and context graphs, ontology and schema design, entity resolution, multi-hop retrieval, graph databases and query languages, or graph ML and network analysis.
• Deep experience designing and operating ML/LLM platforms, agent runtimes, model-serving systems, retrieval and ranking platforms, or high-scale distributed data systems.
• Meaningful open-source leadership, technical talks, developer tools, technical writing, patents, or high-impact research publications in agentic AI, ML systems, or software platforms.
• Evidence of building innovative technologies or applications that improve complex operational workflows for frontline users, associates, customers, or other high-impact users.

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Minimum Qualifications...

Outlined below are the required minimum qualifications for this position. If none are listed, there are no minimum qualifications.

Preferred Qualifications...

Outlined below are the optional preferred qualifications for this position. If none are listed, there are no preferred qualifications.

Primary Location...