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Unity
Machine Learning Engineer, Offline Infrastructure (Entry-Level / New Grad)
Unity
San Francisco•On-site•Salary•0-1 Yrs•Jun 30
Job description
Overview
The opportunity Unity Vector builds an offline ML platform that powers insight, experimentation, attribution, and AI-driven decision-making across the company.
Responsibilities
- 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
Qualifications
- 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)
- Familiarity with ML frameworks (e.g., PyTorch, TensorFlow) and/or distributed systems (e.g., Ray, Spark)
- Strong problem-solving skills and ability to translate research ideas into practical systems
- Interest in building scalable, reliable infrastructure for machine learning
- Exposure to large-scale data platforms (data lakes, warehouses, streaming systems)
- Publications or research in ML systems, distributed systems, or related areas
- Relocation support is not available for this position
- Work visa/immigration sponsorship is not available for this position
Benefits
- Note: 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.