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Machine Learning EngineerServiceUp Inc. • New York, New York, United States
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Machine Learning Engineer

ServiceUp Inc.
  • US
    New York, New York, United States
  • US
    New York, New York, United States
Jetzt Bewerben

Über

ServiceUp is reimagining a $200B+ industry with AI-powered orchestration for fleet and insurance vehicle repair. We’re the all-in-one platform bringing modern efficiency to a complex market. We’re trusted by some of the world’s largest and most sophisticated fleets and insurers.
Our AI-powered platform automates the entire repair lifecycle, from the moment a vehicle needs service until it’s back on the road. By removing bottlenecks, providing real-time visibility, and streamlining decisions, we help customers reduce downtime, control costs, and keep operations running smoothly.
As one of the 100 fastest-growing startups in the United States, and backed by $55M in Series B funding , we're leading this transformation. Join us and help shape the future of vehicle repair.
About the Role You’ll own the full lifecycle of machine learning at ServiceUp, from exploration and model development through production deployment and iteration. You’ll collaborate closely with product, design, and engineering to identify where ML can create value and translate model outputs into features customers use. Our data stack today includes PostgreSQL, BigQuery, and Hex, but this is our first dedicated ML hire. We’ll look to you to help shape the ML tooling and infrastructure.
What You’ll Do
Own and expand the model portfolio. Drive the ML engineering process from idea to production.
Define success metrics and evaluation frameworks for each model.
Productionize ML models. Take experiments to production with real-time inference, monitoring, retraining pipelines, and phased rollouts with feature flags and guardrails.
Build and evaluate classifiers with rigorous offline and online methods.
Design feature engineering pipelines across repair orders, shop performance, fleet behavior, vehicle attributes, and cost benchmarks.
Own model evaluation including cross validation, XAI, confidence calibration, bias detection, A/B testing, and shadow mode comparison against human decisions.
What You’ll Bring
7+ years of applied ML experience with a track record of shipping models to production.
Strong statistics and ML fundamentals. Bias-variance tradeoffs, class imbalance, calibration, and knowing when a simpler model wins.
Deep experience with tabular data, feature engineering on structured data, and messy real-world datasets.
Production ML experience. Feature drift, data quality, monitoring, and the gap between offline metrics and real-world performance.
Proficiency with ML tooling and frameworks. Our stack is Python-based today, but we care more about shipping production-quality code than any specific language.
SQL fluency for data exploration, feature building, and model validation.
Excellent communication and collaboration skills in a remote environment.
Nice to Have
Domain experience in marketplaces, fleet operations, or repair/maintenance workflows.
Anomaly and fraud detection experience.
Practical LLM experience for explanation generation or data enrichment (not as the core model).
Why You’ll Love Working Here We live our values every day: Team First, Work Smart, Own It, Be Bold, Push Boundaries . If that sounds like you, you’ll fit right in.
What We Offer
Competitive pay with equity
Medical, dental, and vision coverage
Flexible PTO and company holidays
Remote-friendly setup with home office support
Learning and development budget
Wellness stipend to support your health and well-being
Paid parental leave
Benefits may vary by location and role.
If you’re motivated by impact and aligned with our values, we want to hear from you. You don’t need to meet every single requirement — we care about your skills, drive, and how you work with a team.
Service Up is an equal opportunity employer committed to a diverse, inclusive workplace where everyone can do their best work.
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  • New York, New York, United States

Sprachkenntnisse

  • English
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