(USA) Principal, Software Engineer

Position Summary...

Principal, Software Engineer - Generative AI / Machine Learning Engineer for Connected TV Innovation 

We’re building the next generation of AI‑powered experiences on smart TVs—think on‑device large‑language‑models, real‑time content understanding, privacy‑preserving audience insights, and interactive generative graphics that transform millions of households to discover, shop, and play. You will be one of the first engineers bridging deep‑learning research with production code on consumer electronics hardware. 

What you'll do...

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. 

Must‑Have Qualifications 

• 15+ 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. 

Nice‑to‑Have 

• 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).