Machine Learning Engineer | Python | Pytorch | Distributed Training | Optimisation | GPU | Hybrid, San Jose, CA

Machine Learning Engineer | Python | Pytorch | Distributed Training | Optimisation | GPU | Hybrid, San Jose, CA

Title : Machine Learning Engineer

Location : San Jose, CA

Responsibilities :

• Productize and optimize models from Research into reliable, performant, and cost-efficient services with clear SLOs (latency, availability, cost).
• Scale training across nodes / GPUs (DDP / FSDP / ZeRO, pipeline / tensor parallelism) and own throughput / time-to-train using profiling and optimization.
• Implement model-efficiency techniques (quantization, distillation, pruning, KV-cache, Flash Attention) for training and inference without materially degrading quality.
• Build and maintain model-serving systems (vLLM / Triton / TGI / ONNX / TensorRT / AITemplate) with batching, streaming, caching, and memory management.
• Integrate with vector / feature stores and data pipelines (FAISS / Milvus / Pinecone / pgvector; Parquet / Delta) as needed for production.
• Define and track performance and cost KPIs; run continuous improvement loops and capacity planning.
• Partner with ML Ops on CI / CD, telemetry / observability, model registries; partner with Scientists on reproducible handoffs and evaluations.

Educational Qualifications :

Industry Experience :

Technical Skills :

Machine Learning Engineer | Python | Pytorch | Distributed Training | Optimisation | GPU | Hybrid, San Jose, CA