Senior Algorithm Engineer - Model Training & Optimization

We are looking for a Senior ML Algorithm Engineer to lead the development and optimization of machine learning models for challenging real-world problems.

In this role, you will work hands-on across the full model-development lifecycle: understanding the task, designing and adapting algorithms, building effective training strategies, analyzing data and failure modes, and improving model quality and efficiency through rigorous experimentation.

This is an algorithm-focused role for someone who enjoys getting deeply involved in the details of model training and optimization. You will not simply operate an existing training infrastructure—you will investigate open-ended problems and develop practical solutions involving model architecture, data and sample selection, training objectives, optimization methods, and evaluation

Mobileye changes the way we drive, from preventing accidents to semi and fully autonomous vehicles. If you are an excellent, bright, hands-on person with a passion to make a difference come to lead the revolution!

What will your job look like:

Work on the semantics of road objects, using harvested tabular data as the primary input to our algorithms — turning large-scale, real-world observations into models that capture object meaning, attributes, and behavior on the road network.

Design, implement, and optimize machine learning and deep learning algorithms for these semantic tasks, from architecture and training objectives through evaluation and efficiency.

Develop and improve end-to-end model training pipelines, from data preparation and sampling of harvested tabular datasets through training and evaluation.

Investigate model behavior, identify failure modes, and drive targeted algorithmic improvements.

Develop effective strategies for data selection, dataset composition, sampling, augmentation, loss design, and training schedules.

Lead the investigation of complex algorithmic challenges, uncover patterns in data and model behavior, and translate insights into measurable improvements.

All you need is:

5+ years of experience in algorithm engineering using machine learning, deep learning, or neural networks.

Strong hands-on experience designing, training, evaluating, and optimizing.

Strong programming skills in Python.

Hands on experience with Spark, Pandas, Pytorch and AWS.

Experience with Polars and DuckDB- an advantage

Experience with some of the following: sampling strategies, data augmentation, hyperparameter optimization

Strong understanding of distributed systems, scalability, and performance optimization.

Ability to independently investigate complex problems, identify patterns in data and model behavior, run experiments, and translate findings into measurable algorithmic improvements.

Strong analytical and problem-solving skills, with a practical, hands-on, and ownership-driven mindset.