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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # ML Infrastructure engineer - Early Career - **Company:** Unity Technologies - **Location:** United States - **Salary:** $143,200.0 - $186,100.0 - **Contract:** Permanent contract - **Skills:** Training Data, Airflow, Big Data, Distributed Systems, Python (Programming Language), Machine Learning, Tensorflow, Azure Machine Learning, Data Processing, Pytorch, Apache Spark, Data Lakes, Information Technology, Machine Learning Operations, Stream Processing, Data Pipelines - **Published:** July 14, 2026 - **Apply:** https://www.juju.com/job/00000000ggltov ## About the Role + Bachelor's degree in Computer Science, Machine Learning, Systems, or a related field + Strong foundation in machine learning systems, distributed systems, or large-scale data processing (through research or projects) + Experience with Python and working with data-intensive workloads + Familiarity with ML frameworks (e.g., PyTorch, TensorFlow) and/or distributed systems (e.g., Ray, Spark) + Experience (academic or applied) with data pipelines, model training workflows, or large datasets + Strong problem-solving skills and ability to translate research ideas into practical systems + Interest in building scalable, reliable infrastructure for machine learning + Nice to Have + Experience with workflow orchestration systems (Airflow, Flyte, etc.) + Exposure to large-scale data platforms (data lakes, warehouses, streaming systems) + Publications or research in ML systems, distributed systems, or related areas ## Description You'll help build and evolve the infrastructure that powers training data generation, ML workflows, and distributed model training. Working closely with experienced engineers and researchers, you'll contribute to systems that ensure our ML pipelines are reliable, scalable, and efficient. This role offers the opportunity to bridge research and production-translating advanced ideas into systems that operate at scale. What you'll be doing + Build and maintain data pipelines that generate training datasets for machine learning models and experimentation + Contribute to infrastructure that supports distributed training workflows (e.g., PyTorch, Ray) + Work with workflow orchestration tools (e.g., Airflow, Flyte, or similar) to support multi-stage ML pipelines + Improve reproducibility and reliability through dataset validation, monitoring, and testing + Partner with ML engineers to support experimentation and model iteration + Help optimize performance and efficiency across data processing and training systems + Contribute to the evolution of our offline ML platform architecture as it scales, _This range reflects the anticipated base salary for this position. Beyond base salary, this role may be eligible for equity awards and participation in our company incentive plans (such as annual discretionary bonuses or sales commissions). The final offer amount will depend on several factors, including geographic location and the candidate's relevant experience, professional background, and skill set._, _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) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding) - [Introduction to Azure Machine Learning](https://www.wearedevelopers.com/videos/368-introduction-to-azure-machine-learning) - [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) - [PySpark - Combining Machine Learning & Big Data](https://www.wearedevelopers.com/videos/44-pyspark-combining-machine-learning-big-data) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [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) - [Got AI ideas but no money? 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