Senior Machine Learning Engineer
Tekmetric · United States ·
- Seniority
- Senior
- Category
- ML ai
- Experience
- 4+ years
- Company size
- 51-200
Tekmetric · United States ·
Cboe · Chicago, IL
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Build and scale web scrapers and data pipelines in Python to extract, clean, and index auto-repair data for search using Elasticsearch, Spark, and AWS.
About Tekmetric
Tekmetric is the all-in-one, cloud-based software helping auto repair shops run smarter, grow faster, and serve customers better.
Built by a shop owner and shaped by the needs of the industry, Tekmetric brings together innovation, real-world experience, and a thriving community to help shops thrive - not just survive. From running a shop, to securing payments to engaging customers, our platform simplifies operations so shop owners can focus on what really matters: delivering exceptional service, earning trust, and growing sustainably.
Officially founded in Houston in 2017, Tekmetric has grown from a single shop’s vision to the industry’s leading solution - all by staying true to our values of transparency, integrity, innovation, and a service-first mindset.
But we’re not just building shop management software. We’re building a movement. We’re empowering repair shops to rise above the daily grind, create meaningful connections with their customers, and lead the industry forward - one interaction at a time.
Come build with us. Join the journey. Shape the future of auto repair.
Working the Tekmetric Way
At Tekmetric, we’re building a culture where winning matters - not for ego, but because when our customers win, we win together.
We move fast, stay curious, and take full ownership of our results — no excuses, no finger-pointing. If you thrive in ambiguity, take initiative, and view honest feedback as fuel for growth, you’ll feel right at home here.
We’re direct but respectful, ambitious yet grounded, and collaborative at every level. Everyone leads through impact and is encouraged to speak up, share ideas, and challenge assumptions (even your manager’s). This is a place for builders, not bystanders.
Success here takes focus, follow-through, and a willingness to roll up your sleeves — but if you’re driven by meaningful work and real results, it’s deeply rewarding. You’ll join a team that cares about the work, supports one another, and takes smart risks to achieve bold goals. Be yourself, stay mission-focused, and you’ll thrive. If that energizes you, we can’t wait to meet you.
Where We Work: At Tekmetric, great work happens anywhere, but great teams are built through intentional connection. We offer hybrid and remote work models based on your proximity to our office hubs. Because we value in-person collaboration, travel is an expected part of every role. We come together several times a year for team and company-wide offsites to align on goals and strengthen relationships. Attendance at these events is expected and fully supported.
What You’ll Do Tekmetric sits on a decade of granular operational data from thousands of automotive repair shops — millions of operational records spanning customer history, service patterns, and technician activity. We are building the ML layer on top of it: retrieval systems, recommendation models, embedding infrastructure, and the LLM integration layer that makes AI features feel native to the product workflow. This is a systems role for someone who builds production AI seriously.
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.
What You’ll Bring
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).
Nice to Have
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.
Why You'll Love Working With Us
Health & Wellness That Have You Covered:
Investing in Your Future (and Present):
Most importantly, we have a stellar team, a whole lot of fun, and work that actually matters to the shops and families we serve!
Tekmetric is an equal opportunity employer. We hire hard-working individuals, regardless of gender, race or color, ethnicity or national origin, age, disability, religion, sexual orientation, gender identity or expression, or veteran status. We know that when our employees feel appreciated and included, they can be more creative, innovative and successful.
We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation.
Tekmetric will never ask for your personal financial or banking information or require any financial commitment as part of our candidate search or application process. No job offers will be made without a formal interview process. All legitimate Tekmetric job postings are listed at . Recruiter communications come from @tekmetric.com email addresses. Please visit this link for further information Fraudulent Job Offers
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