[Remote] Senior Machine Learning Engineer

Note: The job is a remote job and is open to candidates in USA. Tekmetric provides cloud-based software that helps automotive repair shops manage operations, payments, customer engagement, and growth. The Senior Machine Learning Engineer will build production AI systems, including retrieval-augmented generation pipelines, semantic search, recommendation models, embedding infrastructure, evaluation frameworks, and AWS-based ML integrations.

Responsibilities

• Design and build RAG pipelines — chunk store schema, Redshift-sourced population, and the signal-based retrieval layer that assembles context for LLM-powered suggestions in real time
• Own the semantic search infrastructure over operational data: embedding pipeline, chunking strategy, hybrid BM25+vector retrieval, and the reranking layer that feeds LLM tool calls (PGvector)
• Build a recommendation model end-to-end — training pipeline (feature extraction, model development, evaluation) and the SageMaker inference path that surfaces relevant suggestions at the right moment in the user workflow
• Design tool definitions, prompt assembly, and context selection for Bedrock-backed AI features, working directly with application engineers who wire these into the product
• Compute and serve ML features that recommendation models and customer analytics depend on: entity-level historical aggregates, account behavioral profiles, and engagement history
• Build and maintain batch and incremental embedding pipelines via Bedrock (Titan, Cohere) with CDC-based freshness, keeping the vector store current with the operational database
• Build evaluation frameworks — offline retrieval metrics, model quality tracking, and A/B infrastructure needed to measure whether AI features are driving business outcomes
• Collaborate with data engineers to integrate ML models into data pipelines and surface results through search and analytics APIs

Skills

• 4+ years in ML engineering with clear experience taking a model or ML feature from experimentation into production — not just research or notebooks
• Retrieval systems depth — embeddings, vector similarity, and ANN search at the mechanism level; ability to reason about BM25 vs. dense retrieval, chunking tradeoffs, and when reranking adds value (PGvector, FAISS, Qdrant, Weaviate, Pinecone)
• LLM application patterns — hands-on experience with RAG, tool use/function calling, prompt engineering, and context assembly; debugging retrieval pipelines where model output was wrong because the context was wrong (Bedrock, OpenAI, Anthropic SDK, LangChain)
• Strong Python and ML tooling — production-quality training and inference code, not just exploratory notebooks (PyTorch, scikit-learn, Hugging Face, XGBoost)
• Data engineering fundamentals — writing the SQL or Spark job that extracts training data without handing it off; understanding point-in-time correctness and feature leakage
• ML infrastructure on AWS — deploying and operating models in a real cloud environment (SageMaker, Bedrock, S3, Lambda)
• Workflow orchestration — building ML pipelines that run on a schedule, handle failure gracefully, and do not require manual intervention to stay healthy (Airflow, Prefect, or Kubeflow)
• Fine-tuning or PEFT — experience adapting embedding or generative models to a domain-specific corpus
• Graph databases — Apache AGE, Neo4j, or similar; understanding how graph traversal complements vector similarity for multi-hop relationship queries
• Streaming and CDC — Kinesis, Kafka, or Debezium; keeping a vector store or feature store current with a live operational database
• Recommendation systems — collaborative filtering, two-tower models, or contextual bandits in a production setting
• Domain experience in operational data — automotive, field service, logistics, healthcare; any domain where records are sparse, the vocabulary is specialized, and keyword search reliably fails

Benefits

• Hybrid and remote work models based on proximity to office hubs
• Travel to team and company-wide offsites several times a year is fully supported
• Generous Paid Time Off
• Paid maternity, parental bonding, and medical leave for employees or their loved ones
• Comprehensive health benefits, including Medical, Dental, Vision, and Prescription coverage
• Employee-only health plans covering 100% of premiums
• Family health plans covering 50% of costs
• Free, confidential counseling through BetterHelp
• 401(k) Retirement Savings Plan with 100% employer match on contributions up to 6%
• Flexible Spending Accounts (FSA)
• Health Savings Accounts (HSA)
• Life and Accidental Death & Dismemberment (AD&D) Insurance
• Up to $60/month toward fitness, mental health, or almost anything that helps you feel your best
• After one year of employment, a $300 home office setup bonus
• Support for continuing education
• Fun activities

Company Overview

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