Senior Machine Learning Engineer — San Francisco Bay Area

Project: Build, train, and deploy next-generation LLM-based recommender systems.

Responsibilities:

• Data to training sets — turn unstructured and structured traveler and catalog data into narrative text documents and synthetic data, at a scale of tens of millions of documents.

• Feature and context engineering — decide what information goes into each document and how to express it (market-relative price, tier, trip context).

• Large-scale distributed training on Amazon SageMaker AI, including HyperPod.

• Fine-tuning — LoRA/QLoRA adapters, supervised training, and reinforcement learning for ranking and reasoning.

• Deep learning modeling — model architecture, ranking heads, co-training and multi-task setups.

• Inference and deployment — low-latency, cost-efficient serving, including quantization, distillation and profiling.

• Evaluation and error analysis — build evaluation sets, run ablations, catch data leakage, and write up findings clearly for the customer and AWS science team.

• Data privacy — handle customer data safely, including removing personal data.

Required skills:

• Spark (PySpark) and the Hadoop ecosystem (Hive, HDFS) on corpora of tens of millions of documents

• Amazon SageMaker AI: processing, training, distributed training (HyperPod), deployment

• LLM fine-tuning (LoRA/QLoRA) and reinforcement learning (e.g. GRPO/PPO-style methods)

• Strong deep learning fundamentals in PyTorch: architectures, training dynamics, co-training

• Large-scale batch LLM inference and production inference (vLLM, Amazon Bedrock batch)

• Data privacy and security on AWS: personal-data removal, IAM, KMS, Lake Formation

• Clear written communication with customer and science stakeholders

Preferred skills:

• AWS Trainium / Inferentia (Neuron SDK)

• Recommender systems: ranking metrics (NDCG, MRR), sequential and generative recommenders

• Embedding quantization or semantic IDs

• Experience in a customer-facing or FDE roles