Walmart is building the next generation of AI powered experiences on smart TVs, including on-device large language models, real-time content understanding, privacy-preserving audience insights, and interactive generative graphics. The AdTech M&R data team needs to deliver reporting and measurement for Advertisers to analyze and optimize campaigns, leveraging scalable data pipelines, machine learning techniques, and data analysis to make sense of broadly defined problems.
Requirements
- 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, DPSGD, HE, SMPC) and ability to translate compliance constraints into code.
- Experience optimizing models for ARM Cortex‚ NEON, NPU, or VVC ASICs inside smart‚ TV SoCs.
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.
- building scalable data pipelines
- using machine learning techniques and data science
Other
- 5 years building and shipping ML or DL products (or 2 with a PhD degree).
- Bachelor or higher in CS, EE, Math, or related field.
- Background in ad tech or retail media optimization (incrementally real time ROAS auction dynamics).
- 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).
