The opportunity We are seeking a senior ML engineer to design and evolve the large-scale offline platform. This role focuses on building reliable infrastructure for generating training datasets, orchestrating ML workflows, and enabling efficient, distributed model training at scale. You will work closely with ML engineers and platform teams to ensure our pipelines can efficiently handle growing data volumes and increasingly complex training workloads. You will play a key role in shaping how model datasets are prepared as well as model training, validated, and delivered to distributed training systems, while ensuring the reliability, scalability, and performance of our offline ML platform. What you'll be doing - Design and operate large-scale data pipelines that generate training datasets used for machine learning training and experimentation - Develop infrastructure that supports distributed training workflows using technologies such as Pytorch, Ray Data, and Ray Train, etc. - Integrate ML pipelines with workflow orchestration systems (e.g., Flyte, Airflow, or similar) to enable reliable multi-stage training workflows - Improve reproducibility and observability of ML pipelines through dataset validation, monitoring, and automated testing - Optimize performance and resource utilization across distributed compute systems used for data processing and model training - Partner closely with ML engineers to enable efficient large-scale experimentation and model iteration - Lead architectural improvements to ensure our offline ML pipelines remain scalable, reliable, and cost-efficient What we're looking for - Strong experience building large-scale ML pipelines - Experience working with distributed computing frameworks such as Ray, - Spark, Flink and familiarity in the Ray ecosystem (Ray Data, Ray Train) for distributed data processing and model training - Experience building infrastructure for training data generation, dataset preparation, or ML feature pipelines - Deep experience designing and operating production-grade data pipelines - Strong programming skills in Python and experience working with large-scale distributed workloads - Experience with modern data infrastructure (data lakes, warehouses, orchestration systems, streaming platforms) - Strong systems thinking, with the ability to reason about performance, scalability, reliability, and cost tradeoffs in distributed systems - Proven ability to lead technical direction and influence architectural decisions across teams without formal authority Additional information - Relocation support is not available for this position - Work visa/immigration sponsorship is not available for this position Benefits At Unity, we want our team members to thrive. We offer a wide range of benefits designed to support well-being and work-life balance. Please note: Benefits eligibility, specific offerings, and coverage vary based on the country and employment status. While specific benefits vary, here are some of the ways we strive to take care of our eligible team members globally: Comprehensive health, life, and disability insurance | Commute subsidy | Employee stock ownership | Competitive retirement/pension plans | Generous vacation and personal days | Support for new parents through leave and family-care programs | Office food snacks | Mental Health and Wellbeing programs and support | Employee Resource Groups | Global Employee Assistance Program | Training and development programs | Volunteering and donation matching program Life at Unity Unity [NYSE: U] is the world’s leading game engine, powering play for more than 3 billion consumers each month. The top mobile games in the world, the most played PC indie titles, the most innovative console games, and virtually all of the top XR and Web Games are developed, deployed, and grown in Unity. Unity also enables teams across industries like automotive, manufacturing, and healthcare to design, simulate, and collaborate in 3D — closing the gap between ideas and reality. For more information, please visit www.unity.com. Unity is a proud equal opportunity employer. We are committed to fostering an inclusive, innovative environment and celebrate our employees across age, race, color, ancestry, national origin, religion, disability, sex, gender identity or expression, sexual orientation, or any other protected status in accordance with applicable law. Our differences are strengths that enable us to support the growing and evolving needs of our customers, partners, and collaborators. If you have a disability that means there are preparations or accommodations we can make to help ensure you have a comfortable and positive interview experience, please fill out this form to let us know. This position requires the incumbent to have a sufficient knowledge of English to have professional verbal and written exchanges in this language since the performance of the duties related to this position requires frequent and regular communication with colleagues and partners located worldwide and whose common language is English. Headhunters and recruitment agencies may not submit resumes/CVs through this website or directly to managers. Unity does not accept unsolicited headhunter and agency resumes. Unity will not pay fees to any third-party agency or company that does not have a signed agreement with Unity. Your privacy is important to us. Please take a moment to review our Prospect Privacy Policy and Applicant Privacy Policy. Should you have any concerns about your privacy, please contact us at DPO@unity.com. #SEN *Note: Certain locations require a good faith disclosure of the base salary range for the role. The actual salary for the successful candidate may differ based on location, experience, and other job-related factors. Gross pay salary $178,300—$267,500 USD

About the Role: You'll own the full lifecycle of machine learning at ServiceUp, from exploration and model development through production deployment and iteration. You'll collaborate closely with product, design, and engineering to identify where ML can create value and translate model outputs into features customers use. Our data stack today includes PostgreSQL, BigQuery, and Hex, but this is our first dedicated ML hire. We'll look to you to help shape the ML tooling and infrastructure. What You'll Do: - Own and expand the model portfolio. Drive the ML engineering process from idea to production. - Define success metrics and evaluation frameworks for each model. - Productionize ML models. Take experiments to production with real-time inference, monitoring, retraining pipelines, and phased rollouts with feature flags and guardrails. - Build and evaluate classifiers with rigorous offline and online methods. - Design feature engineering pipelines across repair orders, shop performance, fleet behavior, vehicle attributes, and cost benchmarks. - Own model evaluation including cross validation, XAI, confidence calibration, bias detection, A/B testing, and shadow mode comparison against human decisions. What You'll Bring: - 7+ years of applied ML experience with a track record of shipping models to production. - Strong statistics and ML fundamentals. Bias-variance tradeoffs, class imbalance, calibration, and knowing when a simpler model wins. - Deep experience with tabular data, feature engineering on structured data, and messy real-world datasets. - Production ML experience. Feature drift, data quality, monitoring, and the gap between offline metrics and real-world performance. - Proficiency with ML tooling and frameworks. Our stack is Python-based today, but we care more about shipping production-quality code than any specific language. - SQL fluency for data exploration, feature building, and model validation. - Excellent communication and collaboration skills in a remote environment. Nice to Have: - Domain experience in marketplaces, fleet operations, or repair/maintenance workflows. - Anomaly and fraud detection experience. - Practical LLM experience for explanation generation or data enrichment (not as the core model). Why You’ll Love Working Here: We live our values every day: Team First, Work Smart, Own It, Be Bold, Push Boundaries. If that sounds like you, you’ll fit right in What We Offer: - Competitive pay with equity - Medical, dental, and vision coverage - Flexible PTO and company holidays - Remote-friendly setup with home office support - Learning and development budget - Wellness stipend to support your health and well-being - Paid parental leave Benefits may vary by location and role. If you’re motivated by impact and aligned with our values, we want to hear from you. You don’t need to meet every single requirement — we care about your skills, drive, and how you work with a team. Service Up is an equal opportunity employer committed to a diverse, inclusive workplace where everyone can do their best work.

• Design, build, and operate scalable ML and data pipelines for spatial-temporal and sensor-driven datasets. • Operationalize data science algorithms into reliable, distributed ML workflows covering feature extraction, training, evaluation, inference, and model lifecycle management. • Implement and maintain containerized ML workloads in cloud-native environments. • Integrate model outputs into downstream serving systems and analytical platforms to support web-based applications and operational decision-making. • Develop and maintain CI/CD pipelines for ML and data services. • Collaborate closely with data scientists to operationalize experimental models into reproducible, observable, and scalable production systems. • Take ownership of MLOps practices within an applied research team, bringing structure, repeatability, and best practices to evolving environments.

• Diseñar, automatizar y escalar sistemas de Machine Learning aplicando prácticas avanzadas de MLOps • Participar activamente en las etapas clave del ciclo de ML: *data dependencies, ingestion, pre-processing, feature engineering, orchestration, pipeline, transformation e integration testing* • Implementar modelos de regresión, clasificación, segmentación, clustering y series de tiempo • Optimizar el rendimiento de los modelos mediante técnicas como cross-validation, regularización y análisis de curvas de error • Configurar APIs para habilitar el consumo de modelos en producción • Colaborar con equipos multidisciplinarios para integrar soluciones de ML en productos digitales • Promover buenas prácticas de ingeniería de software (PEP8, principios SOLID, modularidad, code review) • Asegurar la calidad del código mediante CI/CD/CT y versionamiento en GitHub • Utilizar herramientas cloud como GCS, BigQuery y Vertex AI (incluyendo pipelines de Kubeflow) • Presentar resultados y avances de forma clara y efectiva utilizando herramientas como Excel y PowerPoint