Senior Machine Learning Engineer, Research Team
GR8 Tech
full-time
Posted on:
Location Type: Remote
Location: United States
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Job Level
About the role
- Take technical ownership of core components of recommendation and personalization systems (retrieval, ranking, evaluation).
- Design and evolve two-tower / embedding-based retrieval models and downstream rankers.
- Drive architectural and modeling decisions with a strong understanding of trade-offs between model quality, system complexity, latency, and cost.
- Define and promote best practices for ML system design, experimentation, evaluation, and deployment.
- Review ML designs, pipelines, and code with a focus on correctness, maintainability, and production readiness.
- Act as a technical point of reference for ML-related decisions within the team.
- Develop, train, and improve ML models for retrieval and ranking use cases.
- Work with embedding-based deep learning models and classical ML approaches.
- Perform in-depth data analysis, feature exploration, and systematic error analysis.
- Build reproducible experiments and robust offline evaluation pipelines.
- Optimize models for both offline metrics and online business KPIs.
- Design and operate batch and real-time training and inference workflows in a cloud environment, with awareness of scalability and cost trade-offs.
- Design, run, and analyze offline experiments and online A/B tests.
- Own ML components in production, with a strong focus on reliability, observability, and safe iteration.
- Monitor model performance and data quality in production.
- Collaborate on scalable training and serving infrastructure for ML systems.
- Participate in incident analysis related to ML systems and contribute to root-cause analysis and long-term fixes.
- Design ML systems with failure modes in mind, including fallbacks and graceful degradation.
- Work closely with Data Engineering on data pipelines and feature generation.
- Partner with Product and Analytics to translate business goals into clear ML objectives and success metrics.
- Act as a technical mentor for ML engineers, providing guidance on modeling, experimentation, and production ML.
- Provide constructive feedback through code reviews and design discussions, supporting the growth of the team.
Requirements
- 5+ years of experience in Machine Learning / Applied Data Science.
- Strong Python skills and experience writing production-quality ML code.
- Solid foundation in core ML and data science 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).
- Strong understanding of ML evaluation, experimentation, and applied statistics.
- Experience deploying, operating, and maintaining ML models in production environments.
- Proficiency with Git, Linux, Docker, and standard ML development workflows.
- Practical experience deploying ML systems in a cloud environment (AWS or equivalent).
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.
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
Machine LearningApplied Data SciencePythonNumPyPandasscikit-learndeep learningembedding modelsLightGBMXGBoost
Soft Skills
technical ownershipmentorshipcollaborationfeedbackcommunicationproblem-solvinganalytical thinkingdecision-makingadaptabilityleadership