Machine Learning Consultant
Michael Saunders & Company
full-time
Posted on:
Location Type: Remote
Location: Remote • 🇺🇸 United States
Visit company websiteSalary
💰 $85 per hour
Job Level
Mid-LevelSenior
Tech Stack
AirflowNoSQLNumpyPandasPythonPyTorchScikit-LearnSQL
About the role
- Design and implement data structures, embeddings, and taxonomies that enable efficient retrieval and contextualization of diverse datasets.
- Develop and maintain pipelines for ingestion, transformation, enrichment, and indexing — ensuring data is clean, discoverable, and ready for AI consumption.
- Build RAG (Retrieval-Augmented Generation) and semantic search pipelines using frameworks such as LangChain, LangGraph, or LangFuse, integrating structured and unstructured data.
- Implement automated tagging, entity recognition, and classification pipelines using Python, ML, and NLP techniques.
- Collaborate with AI and product teams to determine how insights should be surfaced and contextualized for “The Brain.”
- Prototype, test, and deploy retrieval and intelligence systems that connect insights to natural language queries in real time.
- Partner with engineers to integrate ML and retrieval systems into production APIs and applications.
- Contribute to Suzy’s evolving data ontology and knowledge graph, defining how knowledge is linked across qualitative and quantitative sources.
Requirements
- 5–10 years of experience in Machine Learning, Applied AI, or Data Engineering.
- Strong Python expertise, with hands-on experience using Pandas, NumPy, scikit-learn, PyTorch, or similar frameworks.
- Experience with LangChain, LangGraph, or LangFuse, and the ability to build and maintain RAG pipelines.
- Experience with large-scale mixed datasets, including both quantitative (structured) and qualitative (textual, unstructured) data.
- Deep understanding of embeddings, vector databases, and semantic search systems (e.g., FAISS, Weaviate, Pinecone, or Milvus).
- Proficiency in data modeling, schema design, and ontology/taxonomy development for complex knowledge representation.
- Hands-on implementation experience — capable of taking ideas from concept to working system.
- Experience with SQL/NoSQL databases, data pipelines (Airflow, dbt, or similar), and API design for ML systems.
- Comfort working in a fast-paced, experimental environment, balancing iteration with production readiness.
- A builder’s mindset — curiosity, creativity, and a drive to make data smarter, more accessible, and more actionable.
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard skills
Machine LearningApplied AIData EngineeringPythonPandasNumPyscikit-learnPyTorchembeddingssemantic search
Soft skills
collaborationproblem-solvingcreativitycuriosityadaptabilitycommunicationiterationproduction readinessbuilder's mindsetorganizational skills