Senior Machine Learning Systems Engineer

Job Title: Senior Machine Learning Systems Engineer
Location: Remote, US
Department: Data Science 
Type: Full-Time, Exempt
Experience: Senior (5+ Years)
Salary Range: $170,000 - $210,000 base
Core Hours: 9 AM - 1 PM PST / 12 - 4 PM EST

About tvScientific

tvScientific is the first and only CTV advertising platform purpose-built for performance marketers. We leverage massive data and cutting-edge science to automate and optimize TV advertising to drive business outcomes. Our solution combines media buying, optimization, measurement, and attribution in one, efficient platform. Our platform is built by industry leaders with a long history in programmatic advertising, digital media, and ad verification who have now purpose-built a CTV performance platform advertisers can trust to grow their business.

Role Summary

We're looking for a Senior Machine Learning Systems Engineer to help push the boundaries of what's possible in programmatic TV. This role sits at the intersection of high-performance systems engineering and applied machine learning—and is core to how we scale intelligent bidding and campaign delivery across millions of TV screens. You’ll work closely with our distributed team to optimize, build, and deploy ML-powered systems using Zig and Python, helping to make TV advertising as efficient and measurable as digital.

This is a unique opportunity to have outsized impact in a fast-growing Series B startup backed by top investors. If you thrive in fast-moving environments, love solving hard systems problems, and want to shape the infrastructure behind a next-gen CTV platform—this one's for you.

What You'll Do

• Write and maintain production-grade systems in Zig, optimizing for performance and scalability

• Build tooling and bridges between Zig and Python to enable ML development and deployment workflows

• Optimize and scale ML inference pipelines, ensuring low latency and high throughput across massive volumes of ad traffic

• Collaborate cross-functionally with ML scientists, data engineers, and platform teams to bring new models and features to life

• Help define engineering best practices in a growing team and contribute to the long-term architecture of our platform

You’ll Be Successful in This Role if You Have/Are

• Strong experience writing high-performance systems code, ideally in Zig or similar low-level languages

• Deep understanding of computer systems, compilers, and performance tuning

• Proven ability to thrive in startups or high-growth environments—comfortable with ambiguity, fast iteration, and rapid scaling

• Excellent written and verbal communication skills, especially when documenting and discussing technical tradeoffs

You May Also Have

• Experience in Adtech, CTV, or real-time bidding systems

• Background in low-latency systems (<10ms)

• Experience with Python and ML development pipelines

• Familiarity with Scala, Apache Spark, Apache Beam, AWS Athena, or other big data tools

• Comfort working with Kubernetes (EKS) for deployment and scaling

• Prior experience teaching or mentoring other engineers

Culture and Benefits

At tvScientific we believe people do their best work when they feel challenged and engaged by their day to day responsibilities, when they’re surrounded by smart, hard working people, and when they have a healthy work life balance. Our company culture and benefits package reflects these beliefs.

• Full health, dental, and vision insurance - up to 95% funded by the company for employees.
• Employee stock option program.
• Company-sponsored retirement plan with a matching contribution program.
• 12 annual paid holidays (including 2 flexible days).
• Generous PTO policy (get your work done and take the time you need).
• A remote-first environment that allows employees flexibility to work from most places in the US.

tvScientific is committed to building an inclusive environment for people of all backgrounds and everyone is encouraged to apply. tvScientific is an Equal Opportunity Employer and does not discriminate on the basis of race, color, gender, sexual orientation, gender identity or expression, religion, disability, national origin, protected veteran status, age, or any other status protected by applicable national, federal, state, or local law.