Machine Learning Research Engineer II

Summary

Join Technergetics as a Machine Learning Research Engineer II and design, deploy advanced AI capabilities within a high-performing team. You will build intelligent systems transforming unstructured and structured data into actionable knowledge using a knowledge graph. Develop and integrate NER, topic modeling, correlation algorithms, and a recommendation system to power intelligent applications. This remote-friendly position is open to US citizens only due to security clearance requirements. The salary range is $90,000-$125,000 annually, depending on qualifications. Technergetics offers a comprehensive benefits package.

Requirements

• Graduation from an accredited college or university with a bachelor’s degree in a computer science, computer engineering or closely related discipline
• At minimum one year of experience building and deploying ML solutions
• Strong Python skills with experience in ML/NLP libraries (e.g., spaCy, HuggingFace Transformers, scikit-learn)
• Hands-on experience with named entity recognition (NER), topic modeling, and document classification
• Experience working with unstructured and structured data sources at scale
• Familiarity with knowledge graphs, graph databases, and entity-relation modeling
• Proven experience building recommendation systems or content-based/personalized ranking algorithms
• Hands-on experience with semantic search, vector indexing, or embedding-based LLM search architectures
• Solid understanding of integrating ML services into larger software systems

Responsibilities

• Design and implement ML pipelines to extract entities, topics, and relationships from unstructured text (e.g., PDFs, reports) and structured data sources
• Build scalable ingestion systems for integrating document-based and API-driven data streams into a unified context layer
• Apply and fine-tune NER, topic modeling, and clustering techniques using modern frameworks (spaCy, HuggingFace, scikit-learn, etc.)
• Correlate and link extracted data into a graph-based knowledge representation using platforms like Memgraph or Neo4j
• Develop and deploy recommendation systems to suggest relevant content, actions, or knowledge graph entities based on user profiles, extracted insights, or contextual cues
• Implement LLM-powered search capabilities that leverage embeddings, vector databases, and semantic understanding for intelligent querying across documents and graph data
• Integrate ML outputs into full-stack applications built on React, Go, GraphQL, and PostgreSQL
• Work with LangChain and LLM APIs (OpenAI, vLLM, Ollama) to enrich query capabilities and agent reasoning
• Collaborate with infrastructure engineers to containerize and automate deployments via Docker and GitLab CI/CD

Preferred Qualifications

• Experience using LangChain and vector database frameworks in developing retrieval-augmented generation (RAG) pipelines
• Familiarity with backend and infrastructure tools (Go, GraphQL, Docker, GitLab CI/CD)
• Background in deploying AI-powered UIs or intelligent agents in end-user applications
• Exposure to mission-oriented or domain-specific knowledge modeling (e.g., defense, logistics, health, etc.)

Benefits

• Health, life, disability, dental, and vision insurance coverage
• A 401(k) policy with a 3% company contribution & 3% company match
• Paid Time Off (including a PTO “gift day” for your birthday)
• 11 Federal Holidays per year
• Three weeks paid maternity/paternity leave
• Annual technology “allowances”
• Referral bonuses
• Professional recognition awards
• Healthcare stipends
• Tuition/education reimbursement (once specific requirements are met)
• Flexible daily start and stop times for most projects and positions