Senior Machine Learning Engineer, Vector Bidding Science

Unity Technologies SF; Mountain View (CA); On-site; USD 149,000 - 245,000; Full time

Benefits offered by this job

Health insurance

Commute subsidy

Employee stock ownership

Generous vacation and personal days

Parental leave support

Job summary

Unity Technologies SF is seeking a Senior Machine Learning Engineer for the Vector Bidding Science team in Mountain View, CA. This onsite role will help set technical direction and build real-time bidding systems that combine AI with optimization techniques.

You will validate model performance via offline evaluations and online A/B testing, translating results into algorithmic improvements informed by large-scale marketplace behavior.

Qualifications

• Hands-on experience applying state-of-the-art ML, RL, and control theory to bidding/pricing problems.
• Strong Python software engineering skills including DL frameworks like PyTorch.
• Experience with online experimentation (A/B tests) and large-scale data analysis.

Responsibilities

• Design, implement, and optimize core bidding algorithms and auction mechanisms.
• Architect and scale bid landscape forecasting capabilities.
• Analyze marketplace dynamics to surface insights and drive algorithmic improvements.
• Lead offline evaluations and online experiments to validate models and impact.
• Collaborate with product, infrastructure, and engineering teams.

Skills

Machine learning

Reinforcement learning

Control theory

Python

PyTorch

A/B testing

Large-scale data analysis

Software engineering

Tools

Spark

Ray

BigQuery

Flink

Job description

Unity Technologies SF is seeking a Senior Machine Learning Engineer for the Vector Bidding Science team in Mountain View, CA. In this onsite role, you will help set the technical direction and build scalable, real-time bidding systems that combine AI with optimization techniques.

Your work centers on bidding and pacing algorithms intended to improve advertisers’ outcomes. You will also validate model performance through both offline evaluations and online A/B testing, translating results into algorithmic improvements informed by large-scale marketplace behavior.

What you’ll do

• Design, implement, and optimize core bidding algorithms and auction mechanisms
• Architect and scale bid landscape forecasting capabilities
• Analyze large-scale marketplace dynamics to surface insights and drive algorithmic improvements
• Lead offline evaluations and online A/B experiments to validate model performance and produce measurable business impact
• Collaborate cross-functionally with product, infrastructure, and engineering teams

What you bring

• Hands-on experience applying state-of-the-art machine learning, reinforcement learning, and control theory to complex, real-world bidding or pricing problems
• Strong software engineering skills in Python, with experience in deep learning frameworks, preferably PyTorch
• Solid understanding of metric design, online experimentation frameworks (A/B testing), and large-scale data analysis
• Proven ability to lead projects end-to-end and deliver measurable business impact in an ambiguous technical environment

Technologies

• Python
• PyTorch
• A/B testing
• Spark
• Ray
• BigQuery
• Flink

Additional experience that may help

• Experience with large datasets and distributed computing frameworks such as Spark, Ray, BigQuery, or Flink
• Experience building real-time ad systems, including DSP bidding logic, survival analysis for market price estimation, or game-theoretic modeling
• Experience using AI tools such as Claude Code, GitHub Copilot, and Cursor to accelerate development

Compensation and benefits

Salary range: USD 148,700 - 244,600 per year. Anticipated base salary may vary by zone: Zone A ($188,200 - $244,600), Zone B ($167,200 - $217,300), Zone C ($148,700 - $193,300).

Beyond base salary, this role may be eligible for equity awards and participation in company incentive plans (such as annual discretionary bonuses or sales commissions). Final offer amount depends on geographic location and candidate experience, professional background, and skill set.

• 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