Senior Software Engineer, TVision

What You'll Do

Viant Technology (NASDAQ: DSP) is building the future of AI-powered, identity-driven advertising across CTV and the open internet and TVision plays a critical role in that ecosystem by delivering next-generation audience measurement through real-world, person-level viewing insights. Using a combination of computer vision, audio content recognition, network traffic analysis, and large-scale behavioral data collection, TVision measures what consumers watch, how they engage, and how advertising performs across modern streaming and television environments.

As a Senior Software Engineer, you will help build the distributed systems and data infrastructure that power TVision's measurement platform. You'll develop scalable backend services, cloud-native data pipelines, and processing frameworks that enable reliable device telemetry, content recognition, and large-scale analytics. Working closely with data science, analytics, and platform engineering teams, you'll help transform billions of seconds of raw viewing data into actionable insights that drive innovation across Viant's CTV and advertising technology platforms.

The Day-to-day

• Build and maintain web services that support configuration, monitoring, and data ingestion for TVision's in-home measurement devices.
• Build and maintain data pipelines to support the recognition and analysis of content captured by our devices.
• Work with data science and analytics staff to build clean, well-designed tools and libraries for our back-end data processes, making best practices in software engineering and database performance accessible to the entire team.
• Ensure the quality, security, and timeliness of our data deliveries by thorough testing, careful code review, and robust operational monitoring.

Must Have

• Bachelor's degree in Computer Science or a related quantitative field.
• 3+ years of experience building backend services and distributed data systems.
• Experience with Python and modern Python engineering best practices.

Great to Have

• Experience building batch and streaming data pipelines at scale.
• Experience with AWS infrastructure, DevOps tooling, and CI/CD workflows.
• Experience with cloud-hosted, containerized web service architectures and modern deployment patterns.
• Knowledge of relational databases, data lake technologies, and performance optimization best practices.
• Experience with modern big data processing frameworks such as Spark, Databricks, Flink, or similar technologies.
• Comfort collaborating with quantitative and data science teams to optimize computational workflows and data processing efficiency.