The R&D department plays a pivotal role in driving Shelf to disrupt the market. We are looking for Machine Learning experts that are able to deliver end to end with a blend of experience: Python engineering, ML engineering, and pragmatic Data science and Machine learning research. You will ship end-to-end features—from problem framing and experimentation to service deployment, and ongoing operations—quickly and with high quality. Your work will power ML- and LLM-driven services used by top enterprises like Amazon, Mayo Clinic, AmFam, and Nespresso.

This role requires strong Python engineering capabilities coupled with a strong ability to deliver robust ML solutions, along with ML research literacy to choose sound methodologies, define metrics, and evaluate different approaches effectively.

You’ll work in an agile environment, move fast, and own what you ship.

Responsibilities

Own end-to-end delivery: ideate, research, prototype, productionize, and operate ML-powered services with an expectation to iterate and ship frequently
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, evaluate approaches along with
Transform ML models and LLM workflows (including RAG) into reusable, versioned, observable production services with CI/CD
Collaborate with Product Owners to shape our 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 that automate parts of the engineering workflow
Share learnings through demos, docs, and knowledge sessions; contribute to a culture of continuous improvement

Requirements

3+ years of professional experience researching and shipping ML-based solutions, with strong Python skills and a track record of delivering fast without sacrificing quality
Proven experience in owning research problems end-to-end, starting from initial data analysis, through iterative research phases to delivering on production
Practical NLP/LLM experience: transformers, embeddings, prompt design, and evaluation; ability to choose and justify metrics and methodologies
Strong backend fundamentals: designing RESTful services, schema design, data modeling, and performance tuning for SQL and NoSQL stores
Data processing skills: pandas/NumPy; experience with batch/stream processing and ETL orchestration (e.g., Airflow, Step Functions)
Strong English verbal and written communication

As a plus

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, or DeepLake
Background in reinforcement learning, agent frameworks, or autonomous agents
Publications, open-source contributions, GitHub portfolio

What Shelf Offers

B2B contract
Company Stock Options
Hardware: MacBook Pro
Modern technical stack. Develop open-source software
Premier AI development environment: GitHub Copilot, Claude Code, OpenAI, TypingMind, v0, MCP Servers, plus credits to experiment with emerging AI tools

Why Shelf

Leadership with deep knowledge management, AI, and enterprise SaaS expertise
Customers love us for innovative capabilities, reliability, and measurable business impact
$60M+ raised from top-tier investors including Tiger Global, Insight Partners, and Base10
High-velocity growth, tripling year over year for three consecutive years
100+ employees across the U.S. and Europe with ambitious hiring plans

About Shelf

There is no AI Strategy without a Data Strategy. Getting GenAI to work is mission-critical for most companies, but 90% of AI projects haven't deployed. Why? Poor data quality—it’s the #1 obstacle companies face getting GenAI into production.

Shelf unlocks AI readiness. We provide the core infrastructure that enables GenAI to be deployed at scale. We help companies deliver more accurate GenAI answers by eliminating bad data in documents and files before they go into an LLM and create bad answers.

We’re partnered with Microsoft, Salesforce, Snowflake, Databricks, OpenAI and other leaders bringing GenAI to the enterprise. Our mission is to empower humanity with better answers everywhere.

Senior Data Scientist, Machine Learning Engineer

Shelf