Machine Learning Engineer-Internship

Unity Technologies SF•Mountain View, CA

12d

About The Position

Unity Vector builds an offline machine-learning platform that supports insight, experimentation, attribution, and AI-driven decision-making across the company. The platform works across batch and streaming data and enables analytics, product intelligence, machine-learning pipelines, and business operations. As an intern on the Offline Infrastructure team, you will help build and evolve infrastructure for training-data generation, machine-learning workflows, and distributed model training. Working with experienced engineers and researchers, you will contribute to reliable, scalable, and efficient ML pipelines, bridging research and production by translating ideas into systems that operate at scale.

Requirements

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, gained through research or projects.
Experience with Python and data-intensive workloads.
Familiarity with ML frameworks such as PyTorch or TensorFlow and/or distributed systems such as Ray or Spark.
Academic or applied experience 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.
Sufficient knowledge of English for professional verbal and written communication with colleagues and partners worldwide.

Nice To Haves

Experience with workflow orchestration systems such as Airflow or Flyte.
Exposure to large-scale data platforms, including data lakes, warehouses, or streaming systems.
Publications or research in ML systems, distributed systems, or related areas.

Responsibilities

Build and maintain data pipelines that generate training datasets for machine-learning models and experimentation.
Contribute to infrastructure supporting distributed training workflows, such as PyTorch and Ray.
Work with workflow orchestration tools such as Airflow, Flyte, or similar tools 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 the offline ML platform architecture as it scales.

Benefits

Comprehensive health, life, and disability insurance.
Commute subsidy.
Employee stock ownership.
Competitive retirement or pension plans.
Generous vacation and personal days.
Leave and family-care programs supporting new parents.
Office food and snacks.
Mental health and wellbeing programs and support.
Employee Resource Groups.
Global Employee Assistance Program.
Training and development programs.
Volunteering and donation matching program.
The role may be eligible for equity awards and participation in company incentive plans, such as annual discretionary bonuses or sales commissions; eligibility and offerings vary by country and employment status.