Senior Machine Learning Engineer

CarOnSale

  • Brazil, IN
  • 8 days ago
  • Remote

    Highlights

    A Snowflake data warehouse feeds a SageMaker managed feature store, and models reach production through governed CI/CD promotion lanes on Terraform-managed AWS infrastructure. Hands-on experience with a managed ML platform - SageMaker, Vertex AI, Databricks or Azure ML - plus feature stores, CI/CD for machine learning, AWS and Terraform.

    Numbers & Facts

    LocationBrazil, IN (
    Remote
    )

    Description

    Senior Machine Learning Engineer (m/w/d) - Freelance (PJ), Brazil

    You don't want another contract that ends at the notebook. You want the pager, the promotion lane and the authority to say a model isn't ready. This one is based in Brazil, 40 hours a week on your own invoice, and everything after handoff is yours.

    Location: Remote from Brazil - you must be based in Brazil. 40 hours per week, Monday to Friday, with daily overlap into the Berlin working day.

    About us

    CarOnSale is the AI-powered platform for B2B used car trading in Europe. Over 40,000 buyers from more than 20 countries trade on our platform - and 85% of inventory is exclusive to us. We connect software, pricing intelligence, logistics and financing in one layer - as the operating system for an entire industry.

    One Platform. One Profit Engine.

    The platform you build in

    Our machine learning runs on one shared, central platform - not a separate pipeline per model. Five canonical stages: data extraction, validation, transformation, training and evaluation. A Snowflake data warehouse feeds a SageMaker managed feature store, and models reach production through governed CI/CD promotion lanes on Terraform-managed AWS infrastructure. Four models serve production today. Fifteen to twenty by mid-2027. Your job is to build inside that platform and make it stronger, so the next model costs less to ship than the last one.

    Your responsibilities

    • You own models from handoff through to production: packaging, deployment, monitoring, and the decision on whether a model is ready to serve
    • You keep production models reliable - drift detection, performance monitoring, alerting and incident response when something moves
    • You own the serving and inference path: fitted pipeline artifacts, inference entry points, monitoring hooks and feature-store parity
    • You review model design and evaluation methodology before anything ships, and catch data leakage, backward-window errors and weak evaluation during development, while they are still cheap to fix
    • You extend the shared platform so it stays useful for every model, without project-specific logic leaking into shared code
    • You set the engineering standards the platform runs on as it scales across the organisation

    What you bring

    • 2+ years in production machine learning engineering, with real ownership of models after handoff - not only training them
    • Strong Python: typed, tested, production-grade code, and you review the work of others
    • Enough machine learning depth to challenge a pipeline on problem framing, feature engineering, model selection and evaluation methodology
    • Hands-on experience with a managed ML platform - SageMaker, Vertex AI, Databricks or Azure ML - plus feature stores, CI/CD for machine learning, AWS and Terraform
    • An AI-native way of working: you use tools like Claude, ChatGPT or Copilot actively in your daily work
    • English at C1 level, written and spoken. German is not required - we work in English
    • You are based in Brazil and invoice through your own company. We work directly with you, not through intermediary or umbrella services

    Nice to have

    • Snowflake and dbt - you can pick both up here
    • Experience mentoring colleagues or reviewing their work
    • Comfort operating where the answer is not defined yet

    What to expect from us

    • A full-time engagement: 40 hours per week, Monday to Friday, invoiced monthly against your own company
    • You are treated like a full member of the team - standups, bi-weekly sprints, and all company communication
    • Fully remote from anywhere in Brazil
    • An English-speaking engineering team with short decision paths
    • Direct ownership of models serving a live product, not a proof of concept
    • Structured onboarding with a buddy from the team

    Apply now - your CV is enough.

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