The Role
We’re looking for a skilled ML Engineer / Data Scientist with 3+ years of software or applied ML experience to design, build, and improve machine learning solutions in a dynamic cloud environment, primarily on AWS. This role sits at the intersection of data science and engineering: exploring data, developing models, running rigorous experiments, and bringing the best approaches into production with a reliable, reproducible workflow. If strong Python skills, curiosity about hard modeling problems, and collaborative work in multicultural teams are a fit, this is a chance to do meaningful, end-to-end ML work—not just notebooks, and not just infrastructure.
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.
We’re hiring across multiple levels. Title, scope, and compensation depend on experience—from strong applied ML generalists to senior people who can lead modeling direction and mentor others.
We’re especially interested in candidates who are technically strong, intellectually curious, and motivated by difficult, ambiguous problems where good data science and solid engineering both matter.