Senior/Staff Machine Learning Engineer, Data Infrastructure
About The Position
Unity Vector builds a Data platform that powers insight, experimentation, attribution, and AI-driven decision-making across the company. 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.
Requirements
- 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
Responsibilities
- 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
Benefits
- 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
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
