Senior Machine Learning Engineer

About the job

Join a cybersecurity company specializing in protecting connected vehicles and smart mobility ecosystems.

You’ll build and scale production-grade ML solutions, primarily on large-scale tabular automotive data for quality and cybersecurity products. You’ll help define the engineering standards that enable the Data Science team to deliver at scale, with opportunities to own selected ML features end-to-end.

You’ll work with massive (often live-streaming) automotive datasets and real-world constraints around latency, CPU, memory, and I/O.

What You'll Be Doing

• Act as the DS engineering axis: drive designs with focus on performance (I/O, CPU, memory, latency, cost).
• Lead heavy ML engineering efforts when needed (optimization, scaling, reliability), while collaborating with other team members and supporting them in their ML-related projects.
• Own selected ML features and projects end-to-end, including DS work (EDA, features, modeling, evaluation) plus production delivery, monitoring and iteration.
• Build production tabular - ML components: training, batch/near-real-time scoring, inference services, and shared libraries.
• Set standards and tooling for profiling and preventing performance regressions.
• Partner with the data engineers on data / ML contracts (schemas, SLAs, formats/partitioning) between pipelines and ML components.
• Raise the bar via mentoring, documentation, and a strong code/design review culture, in the Data Science team

Requirements

BSc in Computer Science (or equivalent)

5+ years as an ML Engineer / Software Engineer (ML) or similar

Strong Python engineering skills (clean code/architecture, testing, maintainability)

Experience with distributed processing frameworks (e.g, Spark/PySpark, Dask, Trino), and table performance tradeoffs (e.g, partitioning, sorting).

Hands-on experience with modern tabular tooling (e.g., Polars, DuckDB, PyArrow) and performance-oriented patterns

Hands-on tabular ML experience in production: SQL, EDA, feature engineering, tuning, offline/online evaluation

Orchestration experience (Airflow / Prefect / Dagster / Argo) in production pipelines- an advantage

Experience with serving/streaming (gRPC/REST, async, backpressure) and deployable model formats (ONNX/TorchScript) for portable inference - An advantage

Experience with ML lifecycle tooling (e.g., MLflow) - An advantage

Understanding of GBDT for tabular ML (XGBoost / LightGBM / CatBoost) and production tradeoffs, as well as deep learning frameworks in production (PyTorch / TensorFlow) - An advantage

Job No. 417970

CV to [email protected]