Senior – Software Engineer
| Company | Walmart |
|---|---|
| Location | Bentonville, AR, USA, Sunnyvale, CA, USA |
| Salary | $90000 – $234000 |
| Type | Full-Time |
| Degrees | Bachelor’s, Master’s, PhD |
| Experience Level | Senior |
Requirements
- 5+ years building and shipping ML or DL products (or 2+ with a PhD degree)
- Proven track record in generative models (LLMs, diffusion, transformers, VAEs) and modern CV/NLP stacks
- C++ / Rust and Python expertise; comfortable refactoring kernels for efficiency on limited‑memory devices
- Hands‑on experience with edge inference (TensorRT, CoreML, TVM, ONNX, TFLite, WebGPU, or similar)
- Familiarity with CTV / ACR pipelines (frame grabbers, fingerprinting, video embeddings) or embedded multimedia systems
- Strong grasp of distributed training (DDP/Horovod) and MLOps (Kubeflow, Airflow, MLFlow, Feature stores)
- Knowledge of data‑privacy frameworks (FL, DP‑SGD, HE, SMPC) and ability to translate compliance constraints into code
- Bachelor’s or higher in CS, EE, Math, or related field
Responsibilities
- Design & train GenAI models targeting CTV use‑cases: on‑device LLM quantization, multimodal video‑text encoders, and diffusion‑based visual experience generators
- Deploy to edge environments – optimize PyTorch/TensorFlow models with ONNX‑RT/TVM, Arm NN, or Qualcomm SNPE; own CI/CD pipelines that push updates to millions of TVs
- Integrate privacy‑enhancing tech such as federated learning, encrypted feature extraction, and PSI to align with global data regulations and Walmart stack
- Prototype novel user features (e.g., conversational search, generative ad creatives, adaptive picture modes) in collaboration with product, design, and ad‑tech teams
- Publish & share – file patents, present at CVPR/NeurIPS, mentor junior ML engineers, and contribute to open‑source edge‑AI tooling
Preferred Qualifications
- Background in ad‑tech or retail media optimization (incrementally, real‑time ROAS, auction dynamics)
- Experience optimizing models for ARM Cortex‑A, NEON, NPU, or VVC ASICs inside smart‑TV SoCs
- Contributions to OSS projects in quantization, model compression, or video ML (e.g., bits‑and‑bytes, MLC‑AI, PyTorch‑Video)
- Familiarity with incrementality measurement, attribution, and real‑time auction dynamics (e.g., bid shading, floor price optimization)
- Working knowledge of data‑privacy frameworks (FL, DP‑SGD, HE, SMPC) and ID resolutions (UID2, RampID, SharedID)
- Publications/patents in generative media, multimodal learning, or ACR