2 days ago
Base Salary
$90k - $234k/yr
Responsibilities
- Build agentic AI services with planning, tool use, retrieval, feedback loops, orchestration, memory, evaluation, and guardrails.
- Productionize LLMs, GNNs, and embedding services behind stable APIs and SDKs in collaboration with data science and machine learning engineering teams.
- Develop GPU-accelerated data pipelines with RAPIDS and distributed workloads with Ray or similar frameworks.
- Profile and optimize CPU, GPU, memory, and I/O performance using caching, vectorization, and asynchronous patterns.
- Design and maintain microservices for training, inference, vector indexing, and real-time decisioning.
- Implement observability, fault tolerance, autoscaling, cost-aware execution, and internal SDKs and CLIs.
- Establish testing and deployment practices including unit, integration, and end-to-end tests, canary releases, blue-green deployments, and rollbacks.
- Integrate feature stores, vector databases, artifact registries, and model catalogs while enforcing security, privacy, compliance, governance, and auditability.
- Partner with product, platform, and data science or machine learning engineering teams on requirements, SLAs, and success metrics.
- Document systems, contribute to design reviews, and mentor peers on AI systems, distributed computing, and performance engineering.
Requirements
- Bachelor’s degree in computer science, computer engineering, computer information systems, software engineering, or a related area plus two years of software engineering experience, or four years of software engineering experience.
- Four years of production backend or platform-services experience is stated in the role qualifications, preferably in AI or machine learning contexts.
- Proficiency in Python plus one of Go, Java, or C++.
- Experience with Ray, Spark, or Dask; RAPIDS including cuDF, cuML, and cuGraph; GPU-aware programming; FastAPI or Flask; Kubernetes; and Docker.
- Strong foundations in data structures and algorithms, concurrency, networking, and systems design.
- Preferred experience with agent frameworks, vector databases such as FAISS, Milvus, pgvector, or Pinecone, feature stores, LLM and embedding services, and evaluation harnesses.
- Preferred hands-on experience with Kubernetes autoscaling, HPA, KEDA, GPU scheduling, and GPU operators.
- Preferred performance-profiling experience with PyTorch profiler, Nsight, line-profiler, and Ray dashboard.
- Preferred experience with vLLM, Triton Inference Server, ONNX Runtime, or TensorRT for high-throughput inference.
- A master’s degree in computer science or a related field is preferred; accessibility knowledge including WCAG 2.2 AA and assistive technologies is also valued.
Benefits
- 401(k) match, stock purchase plan, performance incentive awards, paid maternity and parental leave, PTO, and multiple health plans.
- Medical, vision, dental, company-paid life insurance, short-term and long-term disability, company discounts, military leave pay, adoption and surrogacy expense reimbursement, and additional leave programs.
- Walmart-paid education benefits can cover high school completion through bachelor’s degrees, English Language Learning, certificates, tuition, books, and fees, subject to eligibility.
- The role is associated with Walmart hubs and emphasizes working together in person; the listed locations are Bentonville, Arkansas; Sunnyvale, California; and Bellevue, Washington.
Tech Stack
Categories
About Walmart
Walmart is a global omnichannel retailer that sells groceries, general merchandise, and services through Walmart and Sam's Club stores and online marketplaces for consumers and small businesses. Its model spans physical retail, e-commerce, advertising, and a vast supply chain, plus pharmacy, health, and financial services. Founded in 1962 and headquartered in Bentonville, Arkansas, Walmart Inc. is a public company on the NYSE (WMT) operating in the U.S. and multiple international markets.