Job Description
The SpringCube team curated the following job opportunity to help you in your job search. Explore the position below to find your next career move.
Company Overview
A leading Connected TV advertising technology organization provides a performance-focused platform designed to help advertisers measure and optimize television advertising. The platform combines media buying, optimization, measurement, and attribution to help advertisers understand the business impact of their CTV campaigns across major streaming services and FAST channels.
The organization is seeking a Sr. Software Engineer, Machine Learning to build the machine learning and artificial intelligence systems that power a large-scale Connected TV advertising platform. The role focuses on real-time bidding, campaign optimization, incrementality measurement, and the development of AI-powered solutions across the advertising lifecycle.
The successful candidate will be a strong production-focused engineer with expertise in Python, machine learning, statistics, and modern AI technologies. This position requires the ability to own complex problems end-to-end, collaborate effectively within a distributed engineering team, and apply sound technical judgment in a fast-moving environment.
Key Responsibilities
- Write production-quality Python code supporting real-time bidding, machine learning model training, and campaign optimization.
- Train, deploy, and monitor machine learning models responsible for making millions of advertising bid decisions per second.
- Build and improve incrementality measurement systems to help advertisers understand the causal impact of their CTV advertising investments.
- Design and implement machine learning products across the advertising lifecycle, including audience targeting, bid optimization, pacing, and attribution.
- Apply large-scale machine learning techniques to improve advertising performance and measurement.
- Use large language models and Generative AI to develop internal tools that accelerate the development, testing, and deployment of machine learning systems.
- Serve as a technical leader and mentor within a distributed engineering organization.
- Collaborate with engineering and product teams to scope, design, implement, test, and deploy machine learning solutions.
- Take ownership of complex technical problems from initial definition through production deployment.
- Contribute to engineering decisions through clear written communication and thoughtful technical analysis.
Required Qualifications
- Strong production Python development skills with experience building software for production environments.
- Strong understanding of statistics and machine learning fundamentals.
- Ability to reason about experiment design, model evaluation, and the appropriate use of simple versus complex modeling approaches.
- Familiarity with modern AI tools and the ability to exercise sound judgment regarding where they can provide meaningful value.
- Familiarity with advertising technology, Connected TV, real-time bidding, or programmatic advertising.
- Strong written communication skills suitable for working effectively within a distributed engineering team.
- Comfort working with ambiguity and independently owning problems from scoping through delivery.
- Bachelor’s degree in Computer Science, Mathematics, Engineering, a related field, or equivalent professional experience.
- 4+ years of relevant industry experience.
Preferred Qualifications
- Experience using AI coding assistants such as Cursor, Copilot, Codex, or similar tools for development, debugging, testing, and refactoring.
- Familiarity with LLM-powered productivity tools for documentation search, experiment analysis, SQL and data exploration, and engineering workflow acceleration.
- Experience with causal inference techniques such as uplift modeling, synthetic controls, difference-in-differences, or incrementality testing.
- Experience working with big data technologies such as Scala and Spark.
- Systems programming experience with Zig, C, C++, Rust, or similar languages.
- Production experience with reinforcement learning or bandit algorithms.
- Experience building agentic AI systems or LLM-powered workflows.
- MLOps experience involving model deployment, monitoring, and pipeline orchestration on AWS.
- Experience working with large-scale advertising platforms or real-time decision-making systems.
Disclaimer
SpringCube curates tech job listings from various company websites to support tech professionals globally.
- No Endorsement: Job ads on SpringCube do not imply endorsement of their authenticity or quality.
- No Client Relationship: This company is not a client of SpringCube unless stated.
- To Apply: Click the Apply button to be redirected to the hiring company’s application page for this job.
- No Liability: SpringCube is not liable for inaccuracies.
