Senior Data Scientist Machine Learning Engineer
Required skills
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
Preferred skills
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
Technologies
Python, ML engineering, pragmatic Data science and Machine learning research, Python engineering, ML models, LLM workflows (including RAG), RESTful services, SQL and NoSQL stores, pandas/NumPy, Airflow, Step Functions, AWS ML stack (SageMaker, Batch, Step Functions, Lambda, SQS/SNS, DynamoDB, ECS, EC2, S3), Vector databases and search (Pinecone, Elasticsearch, pgvector, FAISS, DeepLake), GitHub Copilot, Claude Code, OpenAI, TypingMind, v0, MCP Servers
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
Seniority
Senior
Domain
AI, Machine Learning, Data Science, NLP, LLM, Enterprise SaaS, GenAI, Data Quality, AI Infrastructure