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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior/Staff Machine Learning Engineer, Data Infrastructure - **Company:** Unity Technologies - **Location:** Bellevue, WA, United States - **Experience:** Expert - **Salary:** $200,400.0 - $260,500.0 - **Contract:** Permanent contract - **Skills:** Training Data, Airflow, Automation of Tests, Big Data, Computer Programming, Data Infrastructure, Distributed Computing Environment, Distributed Systems, Python (Programming Language), Machine Learning, Data Streaming, Data Processing, Apache Spark, Data Lakes, Apache Flink, Machine Learning Operations, Data Pipelines - **Published:** August 14, 2026 - **Apply:** https://dejobs.org/x/x/F5905D9DF18B4A0C92FD18B59EC81941/job/ ## About the Role * Experience working with distributed computing frameworks such as Flink, Spark, Ray for distributed data processing * Experience building infrastructure for training data generation, dataset preparation, or ML feature pipelines * Experience optimizing big data pipelines and infrastructure for cost efficiency * 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 ## Description Our systems operate at scale across batch and streaming data, supporting analytics, product intelligence, machine learning pipelines, and business operations. As data volume and complexity grow, our platform also supports large-scale model training, feature generation, and experimentation workflows that power production ML systems. To support this growth, we need strong technical ownership to ensure our ML pipelines remain reliable, scalable, and architecturally sound., We are seeking a senior data infra engineer to design and evolve the large-scale offline platform. This role focuses on building reliable infrastructure for generating data infrastructure, training datasets, and orchestrating data workflows. 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 to ensure the reliability, scalability, and performance of our data platform. What You'll Do * Develop infrastructure that supports both batch and stream big data processing using technologies such as Flink, Spark, Ray, etc. * Design and operate large-scale data pipelines that generate training datasets used for machine learning training and experimentation * Integrate data pipelines with workflow orchestration systems (e.g., Flyte, Airflow, or similar) to enable reliable multi-stage training workflows * Improve reproducibility and observability of data pipelines through dataset validation, monitoring, and automated testing * Optimize performance and resource utilization across distributed compute systems used for data processing * Partner closely with ML engineers to enable efficient large-scale experimentation and model iteration * Lead architectural improvements to ensure our offline data pipelines remain scalable, reliable, and cost-efficient, 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. This posting is intended to fill an existing vacancy, and we are committed to providing applicants with updates throughout the hiring process in accordance with applicable law. 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. ## Related Videos - [Alibaba Big Data and Machine Learning Technology](https://www.wearedevelopers.com/videos/37-alibaba-big-data-and-machine-learning-technology) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Empowering Retail Through Applied Machine Learning](https://www.wearedevelopers.com/videos/976-empowering-retail-through-applied-machine-learning) - [PySpark - Combining Machine Learning & Big Data](https://www.wearedevelopers.com/videos/44-pyspark-combining-machine-learning-big-data) - [The Data Mesh as the end of the Datalake as we know it](https://www.wearedevelopers.com/videos/156-the-data-mesh-as-the-end-of-the-datalake-as-we-know-it) - [AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment](https://www.wearedevelopers.com/videos/1152-ai-model-management-life-circles-ml-ops-for-generative-ai-models-from-research-to-deployment) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [Dev Digest 120 - Apple and peers](https://www.wearedevelopers.com/magazine/455-dev-digest-120-apple-and-peers) - [The Most Popular IT Jobs on the Market](https://www.wearedevelopers.com/magazine/376-the-most-popular-it-jobs-on-the-market) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production)