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Middle Machine Learning Engineer – Research Team

Machine Learning EngineerMachine Learning EngineerFull TimeRemoteTeam 501-1,000H1B No SponsorCompany SiteLinkedIn

Location

United States

Posted

4 days ago

Salary

Not specified

Bachelor Degree3 yrs expEnglishCloudDockerLinuxNumpyPandasPythonPy TorchScikit LearnTensorflow

Job Description

• Develop, train, and iterate on ML models for retrieval and ranking use cases. • Work with embedding-based deep learning models and classical ML approaches. • Perform data analysis, feature exploration, and systematic error analysis to improve model performance. • Build and maintain reproducible experiments and robust offline evaluation pipelines. • Optimize models for both offline metrics and online business KPIs. • Support and improve ML components in production, focusing on reliability and observability. • Design and operate batch and real-time training and inference workflows in a cloud environment. • Monitor model performance and data quality to detect drift or degradation. • Collaborate on scalable training and serving infrastructure to ensure low-latency performance. • Participate in incident analysis and contribute to long-term fixes for ML systems. • Assist in designing, running, and analyzing offline experiments and online A/B tests. • Work closely with Data Engineering to build efficient data pipelines and feature sets. • Participate in design reviews and code reviews to ensure maintainability and production readiness. • Partner with Product and Analytics to understand business goals and translate them into technical ML tasks.

Job Requirements

  • 3+ years of professional experience in Machine Learning or Applied Data Science.
  • Strong Python skills and experience writing clean, production-quality code.
  • Solid foundation in core ML tools: NumPy, Pandas, scikit-learn, etc.
  • Hands-on experience with deep learning frameworks (PyTorch or TensorFlow).
  • Practical experience with embedding models and similarity-based retrieval.
  • Experience with tree-based models (LightGBM, XGBoost).
  • Clear understanding of ML evaluation metrics, experimentation, and applied statistics.
  • Experience working with Git, Linux, Docker, and standard development workflows.

Benefits

  • Benefits Cafeteria — annual budget you allocate to:
  • Sports
  • Medical
  • Mental health
  • Home office
  • Languages.
  • Paid maternity/paternity leave + monthly childcare allowance.
  • 20+ vacation days, unlimited sick leave, emergency time off.
  • Remote-first + tech support + coworking compensation.
  • Team events (online/offline/offsite).
  • Learning culture with internal courses + growth programs.

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