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
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.
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Ability to independently investigate complex problems, identify patterns in data and model behavior, run experiments, and translate findings into measurable algorithmic improvements.
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Strong analytical and problem-solving skills, with a practical, hands-on, and ownership-driven mindset.