Who you are:

• Strong foundation in machine learning, statistics and experiment design.
• Experience building models for real business or product problems, not only academic benchmarks.
• Comfortable working with structured and unstructured data: feature engineering, dataset construction, labeling quality, leakage checks, and train/validation/test discipline.
• Able to compare approaches with clear metrics, error analysis, and sound judgment about tradeoffs (accuracy, latency, cost, maintainability).
• Interest in modern ML, including classical ML, deep learning, and LLM / GenAI workflows where relevant (fine-tuning, RAG, evaluation, prompt/versioning).
• Proficient in Python and able to write clean, modular, testable code.
• Experience developing and deploying ML solutions in a cloud environment, especially AWS.
• Comfortable moving from prototype to production: packaging models, building inference paths, monitoring performance, and iterating after launch.
• Independent engineer who can own work from problem framing experimentation implementation rollout.
• Excellent written and spoken English.
• Enjoy working closely with engineers, product partners, and other data scientists.
• Clear communicator who can explain methods, results, and limitations to technical and non-technical audiences.
• Master’s degree in Science or Engineering (Computer Science, Mathematics, Physics, Statistics, or similar), or equivalent practical experience.

Nice to have:

• Experience with scikit-learn, PyTorch, TensorFlow, XGBoost, or similar modeling stacks.
• Familiarity with ML experiment tracking and reproducibility (e.g. MLflow, W&B).
• Experience with SQL, data warehouses/lakes, and pipeline tools such as Airflow, dbt, or Spark.
• Exposure to feature stores, embedding pipelines, or vector search for retrieval-based systems.
• Experience building HTTP/gRPC APIs or lightweight services around model inference.
• Working knowledge of Docker, basic orchestration, and CI/CD (e.g. GitLab CI).
• Experience in agile, remote and async team environments.
• Publications, patents, Kaggle/competition results, or open-source ML contributions.

What you might like about this role:

• Hands-on modeling work with room to explore, benchmark, and improve real systems.
• Collaboration on ML patent submissions and participation in weekly ML / research paper review meetings.
• A multicultural, engineering-focused team with strong peer support.
• High trust and autonomy—clear goals, freedom in how to reach them.
• Internal product impact: meaningful projects that improve developer and user experience, not endless maintenance tickets.
• Short approval cycles and solid product partnership.
• A healthy meeting policy and emphasis on protecting focus time.
• Flexible hours, remote/home office options, and a calm, engineers-only office when on-site.
• Competitive compensation, including stock options.