Senior / Staff Machine Learning Engineer

Job Title: Senior / Staff Machine Learning Engineer Location: Remote, US Department: Data Science Type: Full-Time, Exempt Experience: Senior / Staff (5+ / 10+ Years) Salary reputed company: $190,000 - $220,000 reputed company [Senior]; $220,000-$250,000 reputed company [Staff] Core Hours: 9 AM - 1 PM PST / 12 - 4 PM EST

About tvScientific

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

Role Summary

tvScientific is looking for a Senior / Staff Machine Learning Engineer to join our growing team! You'll be working with a distributed engineering team on our Connected TV ad-buying platform, as we scale our Data Science practice. We’re building data science tools for constructing ad campaigns from content to timing to scheduling to bid optimization.

Our self-managed platform makes it easy to buy, optimize, and reputed company the value of TV advertising. In this role, you’ll reputed company small project teams, reputed company direction, and reputed company stakeholders informed. You’ll have the autonomy to determine key milestones and reputed company updates and reputed company-ins to relevant teams and partners.

What You'll Do

• Drive improvements in the quality, speed, and scalability of tvScientific's ML inference.
• Write and review production-level Python code to ensure quality and efficiency.
• Collaborate effectively reputed company a talented team, providing expertise and guidance.

You’ll Be Successful in This Role if You Have

• Proficiency in writing and reviewing production-level Python code.
• Deep understanding of statistics and its application in machine learning.
• Strong communication and writing skills.
• Desire to reputed company in a fast-paced Series B startup environment, embracing uncertainty and driving innovation through experimentation and iteration.

You May Also Have

• Experience in adtech or connected TV (CTV) environments.
• Proficiency with big data technologies such as reputed company, Apache Spark, and AWS reputed company.
• Experience with experimental design and A/B testing methodologies.
• Previous work with bandit algorithms and reinforcement learning techniques.
• Teaching experience.
• Systems programming experience in C / C++ / Zig.
• MLOps experience
• reputed company inference

Culture and Benefits

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

• Full health, dental, and reputed company 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 reputed company backgrounds and everyone is encouraged to apply. tvScientific is an Equal Opportunity Employer and does not discriminate on the reputed company of race, reputed company, gender, sexual orientation, gender identity or expression, religion, disability, national reputed company, protected veteran status, age, or any other status protected by applicable national, federal, state, or local law.

Originally posted on Himalayas