• Design, build, and operate machine-learning models. • Work across the full ML lifecycle, including data exploration, feature engineering, and model building. • Emphasize pair programming and test-driven development.

General Information RaceOn is seeking a Senior Machine Learning Engineer as well as a Junior Machine Learning Engineer to architect and build production ML systems that create a competitive advantage on race day. The tasks are slightly different but candidates will be managed via the same application and more details will be available in the interview. A generalization of tasks is listed below as well as profile requirements. Any applications not matching minimum profile requirements will be ignored. Your profile - 2+ years building production ML systems - MSc in Machine Learning, Data Science, Computer Science, or related field (or equivalent experience) - Strong Python and experience with ML libraries (scikit-learn and/or PyTorch/TensorFlow) - Experience with data handling and querying (SQL) - Understanding of model evaluation, deployment concepts, and version control (Git) - Ability to work in complex engineering environments and communicate with non-ML stakeholders - Advantageous would be: time-series forecasting, optimization, real-time systems, dashboards, sports/motorsport analytics, AWS experience. Work Location USA | Remote possible (role-dependent) | Limited Travel required Why us? Your mission - Develop, validate, and deploy ML models for performance and operational use cases (e.g., predictive analytics, decision support, performance measurement) - Build data pipelines and analysis workflows for structured and time-series data - Implement monitoring and iteration practices for deployed models (MLOps basics) - Collaborate with engineering and performance stakeholders to translate requirements into deliverables - Contribute to ML infrastructure and codebase quality (reviews, documentation, reusable components) - Travel occasionally for live validation and stakeholder feedback (role dependent; approx. 5–6 race weekends/year for some assignments)

• Translate business goals into measurable ML goals (KPIs, acceptance thresholds) • Lead the translation of ambiguous product needs into clear ML metrics and success criteria • Own the full lifecycle from prototyping to deployment and monitoring • Develop and maintain observability dashboards and alerts tied to ML metrics and feature drift • Champion cross-functional collaboration & governance • Architect data strategy, championing reproducibility, traceability, and quality across the ML stack • Lead ML solution design and own production deployments • Drive MLOps practices

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description This role is focused on architecting and scaling the ranking systems that power search, recommendations, and personalization across a high-traffic e-commerce platform. - Design multi-task, multi-objective models that optimize for long-term value, relevance, and user engagement, while leveraging LLMs to enhance features and recall. - Partner closely with engineers, product managers, and data teams to lead the development of production-grade ML systems. - Ensure low-latency serving and mentor other ML engineers. - Combine cutting-edge research with practical implementation, influencing user experience, revenue, and retention. - Contribute to both technical strategy and operational excellence in large-scale machine learning systems. - Architect and implement the ranking backbone that unifies search, personalization, ads, and merchandising into a single adaptive platform. - Design and develop multi-task learning models (e.g., shared encoders, MMOE/PLE task heads) to jointly learn relevance, propensity, margin, and churn risk. - Build value-aware, long-horizon objective functions and uplift/causal models to optimize incremental revenue, retention, and user engagement. - Own low-latency inference pipelines including re-ranking, diversity and quality constraints, and safe exploration strategies. - Advance evaluation practices through online experiments, counterfactual analyses, and attribution pipelines to measure long-term impact. - Collaborate with cross-functional teams including product, ads, infrastructure, and design to translate business goals into ML policies and measurable ROI. - Mentor and guide ML engineers, fostering expertise in ranking, causal inference, and scalable serving systems. Qualifications - 5+ years of experience applying ML at scale, with at least 3 years in technical leadership roles improving ranking or recommendation systems. - Proven experience with multi-objective or constrained optimization to balance relevance, revenue, margin, and user experience. - Strong coding skills in Python and data fluency using SQL/Pandas; experience with XGBoost and deep learning frameworks such as TensorFlow or PyTorch. - Solid understanding of low-latency serving architectures, feature stores, caching, vector/lexical retrieval, and re-ranking systems. - Expertise in multi-task learning, calibration, counterfactual evaluation, uplift/causal modeling, or contextual bandits is preferred. - Hands-on experience leveraging LLMs for feature enrichment, long-tail recall, or reasoning-rich context in ML pipelines. - Excellent analytical, problem-solving, and cross-functional communication skills. - Experience with remote-first collaboration and asynchronous alignment across teams and time zones. Benefits - Competitive base salary, with ranges depending on U.S. location: $173,000–$219,000. - Equity grants for new hires and annual refresh grants. - Comprehensive medical, dental, and vision coverage. - Flexible PTO and remote-first work culture. - Opportunities for mentorship, professional development, and research contributions. - Access to cutting-edge ML infrastructure and projects impacting millions of users. - Inclusive and collaborative environment with a focus on innovation and learning.