Senior Data Scientist, Machine Learning Engineer

Shelf has built an operating system for agentic AI that models policies, workflows, and operational logic into an AI Data Model enabling AI agents to reason and deliver precise, compliant, auditable outcomes at scale. Their platform is trusted by brands like Amazon, Nespresso, HelloFresh, and KeyBank, and they partner with Microsoft, Salesforce, OpenAI, Snowflake, and Databricks. The R&D department focuses on delivering end-to-end ML and LLM-driven services for top enterprises.

• 3+ years of professional experience researching and shipping ML-based solutions with strong Python skills
• Proven experience owning research problems end-to-end from data analysis through production
• Practical NLP/LLM experience including transformers, embeddings, prompt design, and evaluation; ability to choose and justify metrics and methodologies
• Strong backend fundamentals including designing RESTful services, schema design, data modeling, and performance tuning for SQL and NoSQL stores
• Data processing skills with pandas and 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 including eval frameworks (e.g., RAGAS), guardrails, red-teaming, prompt optimization at scale
• Model optimization techniques such as quantization, distillation, pruning; GPU/accelerator-aware serving
• Experience with AWS ML stack (SageMaker, Batch, Step Functions, Lambda, SQS/SNS, DynamoDB, ECS, EC2, S3)
• Experience with vector databases and search technologies like Pinecone, Elasticsearch, pgvector, FAISS, or DeepLake
• Background in reinforcement learning, agent frameworks, or autonomous agents
• Publications, open-source contributions, GitHub portfolio

• Own end-to-end delivery of ML-powered services including ideation, research, prototyping, production, and operation with frequent iteration and shipping
• Stand up robust training and evaluation pipelines including 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 a culture of continuous improvement