{"id":2301473,"url":"https://alion.io/job/mobileye-senior-algorithm-engineer-model-training-optimization","title":"Senior Algorithm Engineer - Model Training & Optimization","company":{"id":174,"name":"Mobileye","domain":"mobileye.com","url":"https://alion.io/company/mobileye","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":{"grade":"B","score":75,"open_postings":57,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-13T05:45:00Z"}},"role":"Industrial Engineering","role_family":"Industrial Engineering","seniority":"senior","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Ramat Gan, Israel"],"countries":["IL"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":81000,"max_usd":184000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":6708},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AWS","optional":false},{"name":"Data Augmentation","optional":false},{"name":"DuckDB","optional":false},{"name":"Machine Learning","optional":false},{"name":"Pandas","optional":false},{"name":"Polars","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Spark","optional":false}],"status":"live","first_seen_at":"2026-05-27T10:15:41Z","employer_posted_date":"2026-05-27","last_verified_at":"2026-10-14T01:14:23Z","board_verified":true,"closed_at":null,"days_open":139,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":138},"description":"We are looking for a Senior ML Algorithm Engineer to lead the development and optimization of machine learning models for challenging real-world problems.\n\nIn 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.\n\nThis 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\nWhat will your job look like:\nWork 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.\nDesign, implement, and optimize machine learning and deep learning algorithms for these semantic tasks, from architecture and training objectives through evaluation and efficiency.\nDevelop and improve end-to-end model training pipelines, from data preparation and sampling of harvested tabular datasets through training and evaluation.\nInvestigate model behavior, identify failure modes, and drive targeted algorithmic improvements.\nDevelop effective strategies for data selection, dataset composition, sampling, augmentation, loss design, and training schedules.\nLead the investigation of complex algorithmic challenges, uncover patterns in data and model behavior, and translate insights into measurable improvements.\nAll you need is:\n5+ years of experience in algorithm engineering using machine learning, deep learning, or neural networks.\nStrong hands-on experience designing, training, evaluating, and optimizing.\nStrong programming skills in Python.\nHands on experience with Spark, Pandas, Pytorch and AWS.\nExperience with Polars and DuckDB- an advantage\nExperience with some of the following: sampling strategies, data augmentation, hyperparameter optimization\nStrong understanding of distributed systems, scalability, and performance optimization.\nAbility to independently investigate complex problems, identify patterns in data and model behavior, run experiments, and translate findings into measurable algorithmic improvements.\n\nStrong analytical 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