Senior Machine Learning Engineer at Upstream Security

Herzliya, Tel-Aviv District, Israel -

Full Time

Start Date

Immediate

Expiry Date

16 May, 26

Salary

0.0

Posted On

15 Feb, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python Engineering, System Thinking, Profiling, Distributed Processing, Spark/PySpark, Dask, Trino, Polars, DuckDB, PyArrow, Tabular ML, SQL, Feature Engineering, Airflow, Prefect, Dagster

Industry

Computer and Network Security

Description

Upstream delivers a cloud-based, AI-powered data management platform purpose-built for connected vehicles, smart mobility, and the IoT ecosystem. By leveraging mobility data, Upstream empowers customers with advanced, AI-driven applications across various use cases, including proactive vehicle quality monitoring and detection, cybersecurity detection and response (XDR), misuse detection, usage-based insurance, and more. Upstream is looking for an experienced Machine Learning Engineer to 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. This is a highly collaborative role, and you’ll partner closely with data science and data engineering teams. You’ll be driving technical alignment through clear communication and mentoring others via design and code reviews. This role is full-time and based in Herzliya, Israel. Responsibilities 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 5+ years as an ML Engineer / Software Engineer (ML) or similar BSc in Computer Science (or equivalent) Production-first, end-to-end ownership; experience operating production systems Strong Python engineering skills (clean code/architecture, testing, maintainability) Strong systems thinking and profiling skills (distributed basics, concurrency, memory, reliability; diagnose/optimize bottlenecks) 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 Upstream is an equal opportunity employer. All candidates for employment will be considered without regard to race, color, religion, sex, national origin, physical or mental disability, veteran status, or any other basis protected by applicable federal, state or local law.

Responsibilities

The engineer will act as the Data Science engineering axis, driving designs focused on performance metrics like I/O, CPU, memory, latency, and cost, while leading heavy ML engineering efforts for optimization and scaling. They will own selected ML features end-to-end, including development, production delivery, monitoring, and iteration, and build production ML components for training and scoring.