Senior Data Scientist, Machine Learning Engineer

Shelf provides core infrastructure enabling GenAI deployment at scale by improving data quality for better AI answers. They partner with Microsoft, Salesforce, Snowflake, Databricks, OpenAI, and others to bring GenAI to enterprises.

• 3+ years professional experience researching and shipping ML-based solutions with strong Python skills
• Proven experience owning research problems end-to-end from data analysis to production delivery
• Practical NLP/LLM experience: transformers, embeddings, prompt design, evaluation, metric and methodology justification
• Strong backend fundamentals: designing RESTful services, schema design, data modeling, performance tuning for SQL and NoSQL
• Data processing skills: pandas, NumPy; experience with batch/stream processing and ETL orchestration (e.g., Airflow, Step Functions)
• Strong English verbal and written communication

Nice to have:

• LLM ops and safety: eval frameworks (e.g., RAGAS), guardrails, red-teaming, prompt optimization at scale
• Model optimization: quantization, distillation, pruning; GPU/accelerator-aware serving
• Experience with AWS ML stack (SageMaker, Batch, Step Functions, Lambda, SQS/SNS, DynamoDB, ECS, EC2, S3)
• Vector databases and search: Pinecone, Elasticsearch, pgvector, FAISS, DeepLake
• Background in reinforcement learning, agent frameworks, or autonomous agents
• Publications, open-source contributions, GitHub portfolio

• Own end-to-end delivery: ideate, research, prototype, productionize, and operate ML-powered services with frequent iteration and shipping
• Stand up robust training/evaluation pipelines: dataset curation, labeling/feedback loops, experiment tracking, offline/online metrics, and A/B testing
• Solve problems using sound methodology and evaluate approaches
• Transform ML models and LLM workflows (including RAG) into reusable, versioned, observable production services with CI/CD
• Collaborate with Product Owners to shape product and requirements
• Conduct and receive code reviews; champion engineering excellence, testing discipline, and documentation
• Leverage AI coding assistants to accelerate development and create internal agents automating parts of the engineering workflow
• Share learnings through demos, documentation, and knowledge sessions; contribute to continuous improvement culture