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. As a Sr. Machine Learning Engineer at tvScientific, you'll build the ML and AI systems behind our Connected TV ad-buying platform: real-time bidding, campaign optimization, and incrementality measurement at scale. We're an adtech company solving a hard problem: making CTV advertising actually measurable. Our platform helps advertisers buy ads across the CTV ecosystem: Hulu, Pluto TV, Disney+, HBO Max, and hundreds of FAST channels: and prove that those ads drove real business outcomes. What you'll do: - Write production Python that powers real-time bidding, model training, and campaign optimization - Train, deploy, and monitor ML models that decide which ads to show, when, and at what price: millions of bid decisions per second - Build and improve our incrementality measurement systems: helping advertisers understand the true causal lift of their CTV spend - Design and implement new ML products across the ad-buying lifecycle: audience targeting, bid optimization, pacing, and attribution - Use LLMs and generative AI to build internal tools that accelerate how we develop, test, and ship ML systems - Serve as a technical lead and mentor on a distributed engineering team What we're looking for: - Strong production Python skills: you write code that runs in prod, not just notebooks - Solid statistics and ML fundamentals: you can reason about experiment design, model evaluation, and when simpler approaches beat complex ones - Familiarity with modern AI tools and good judgment about where they add value - Adtech or CTV experience: familiarity with RTB, programmatic advertising, supply-path optimization - Clear written communication: we're a distributed team and writing is how decisions get made - Comfort with ambiguity: you'll own problems end-to-end in a fast-moving environment, from scoping to shipping - Nice-to-Haves: - Teaching experience - Causal inference: uplift modeling, synthetic controls, difference-in-differences, or incrementality testing - Big data experience with Scala and Spark - Systems programming experience in Zig or similar (C, C++, Rust) - Reinforcement learning or bandit algorithms in production - Experience building agentic AI systems or LLM-powered workflows - MLOps experience: model deployment, monitoring, and pipeline orchestration on AWS In-Office Requirement Statement: - We recognize that the ideal environment for work is situational and may differ across departments. What this looks like day-to-day can vary based on the needs of each organization or role. Relocation Statement: - This position is not eligible for relocation assistance. Visit our PinFlex page to learn more about our working model. #LI-SM4 #LI-REMOTE At Pinterest we believe the workplace should be equitable, inclusive, and inspiring for every employee. In an effort to provide greater transparency, we are sharing the base salary range for this position. The position is also eligible for equity. Final salary is based on a number of factors including location, travel, relevant prior experience, or particular skills and expertise. Information regarding the culture at Pinterest and benefits available for this position can be found here. US based applicants only $155,584—$320,320 USD

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. tvScientific is looking for a Senior MLOps Engineer! You'll be working with a distributed engineering team on our Connected TV ad-buying platform, as we scale our Machine Learning practice. We’ve cracked the code on optimizing TV ad campaigns. We’re scaling massively and we need your help to make that scale sustainable. An Idealab company, tvScientific was co-founded by executives with deep roots in programmatic advertising and digital media. tvScientific helps our clients buy ads across the CTV universe, from Hulu to PlutoTV to the ad-supported tier of Disney+ and (HBO) Max. Since our acquisition by Pinterest, we're expanding our work on CTV to lift search & social advertising performance. What you'll do: - Scale the decisionmaking process for tools for the tvScientific AI team, from our workflows to our training infrastructure to our Kubernetes deployments - Improve the developer experience for the data science team - Upgrade our observability tooling - Serve as a technical lead and mentor to the team - Make every deployment smooth as our infrastructure evolves. What we're looking for: - Deep understanding of Linux - Excellent writing skills - A systems-oriented mindset - Experience in high-performance software (RTB, HFT, etc.) - Software engineering experience + reliability (e.g. CI/CD) expertise - Strong observability instincts - Nice-To-Haves: - Reverse-engineering experience - Terraform, EKS, or MLOps experience - Python, Scala, or Zig experience - NixOS experience - Adtech or CTV experience - Experience deploying a distributed system across multiple clouds - Experience in hard real-time low-latency (<10 ms) environments In-Office Requirement Statement: - We recognize that the ideal environment for work is situational and may differ across departments. What this looks like day-to-day can vary based on the needs of each organization or role. Relocation Statement: - This position is not eligible for relocation assistance. Visit our PinFlex page to learn more about our working model. #LI-SM4 #LI-REMOTE At Pinterest we believe the workplace should be equitable, inclusive, and inspiring for every employee. In an effort to provide greater transparency, we are sharing the base salary range for this position. The position is also eligible for equity. Final salary is based on a number of factors including location, travel, relevant prior experience, or particular skills and expertise. Information regarding the culture at Pinterest and benefits available for this position can be found here. US based applicants only $155,584—$320,320 USD

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. We are seeking a Machine Learning Engineer to build out our simulation and AI capabilities. You'll design and implement systems that model the CTV advertising ecosystem — auction dynamics, bidding strategies, campaign outcomes, and counterfactual scenarios — and develop AI-driven tools that accelerate how we build, test, and deploy ML systems. What you’ll do: - Design and build simulation environments that model CTV auction mechanics, inventory supply, and advertiser competition - Develop counterfactual and what-if frameworks for evaluating bidding strategies, budget allocation, and pacing algorithms offline - Build AI agents that explore strategy spaces, generate hypotheses, and automate experimentation within simulated environments - Use LLMs and generative AI to accelerate internal ML workflows — synthetic data generation, code generation, automated analysis, and rapid prototyping - Use simulation to de-risk ML model deployments — validate new bidding and optimization strategies before they touch live traffic - Define the technical direction for simulation and AI infrastructure and mentor engineers on the team What we’re looking for: - Strong production Python skills and experience building simulation or modeling systems - Deep understanding of probabilistic modeling, stochastic processes, or agent-based simulation - Hands-on experience with modern AI tools: LLMs, code generation, agentic workflows — and good judgment about when they help vs. when they don't - Adtech experience: you understand auction theory, RTB mechanics, and the dynamics of programmatic advertising - Ability to translate business questions ("what happens if we change our bid strategy?") into rigorous simulation frameworks - Clear written communication: you'll be defining new technical directions and need to bring others along - Ownership: you scope, design, and ship systems end-to-end with minimal direction - Nice-to-Haves: - Causal inference — uplift modeling, synthetic controls, difference-in-differences, or incrementality testing - Experience with discrete event simulation, Monte Carlo methods, or digital twins - Reinforcement learning — using simulated environments for policy learning and evaluation - Experience building agentic AI systems or multi-agent simulations - Big data experience with Scala and Spark - Systems programming experience in Zig or similar (C, C++, Rust) - MLOps experience — model deployment, monitoring, and pipeline orchestration on AWS In-Office Requirement Statement: - We recognize that the ideal environment for work is situational and may differ across departments. What this looks like day-to-day can vary based on the needs of each organization or role. Relocation Statement: - This position is not eligible for relocation assistance. Visit our PinFlex page to learn more about our working model. #LI-SM4 #LI-REMOTE At Pinterest we believe the workplace should be equitable, inclusive, and inspiring for every employee. In an effort to provide greater transparency, we are sharing the base salary range for this position. The position is also eligible for equity. Final salary is based on a number of factors including location, travel, relevant prior experience, or particular skills and expertise. Information regarding the culture at Pinterest and benefits available for this position can be found here. US based applicants only $123,696—$254,667 USD

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description This role offers the opportunity to shape and scale the next generation of data infrastructure and machine learning systems that power customer-facing products and business insights. The Senior Data/ML Engineer will design, implement, and optimize core data pipelines, ML workflows, and analytics platforms to deliver high-quality, reliable, and actionable data. You will work closely with product, engineering, and analytics teams to operationalize ML models, automate workflows, and ensure data quality and governance at scale. The position combines hands-on technical expertise with strategic influence, making it ideal for engineers passionate about solving complex data challenges and building impactful ML-driven solutions. Collaboration across global teams and ownership of scalable, customer-focused data products are central to this role. - Design, build, and maintain core data models supporting analytics, machine learning, and generative AI workloads. - Implement automation and operationalize ML model workflows to improve efficiency and reduce manual processes. - Collaborate with engineering, product, and analytics teams to deliver seamless customer-facing data products and integrations. - Establish and maintain data quality, observability, and governance frameworks to ensure reliable data at scale. - Document data flows, integration contracts, and operational runbooks for efficient scaling and knowledge sharing. - Develop solutions leveraging modern ML technologies, including LLMs, embeddings, recommendations, and forecasting models. Qualifications - 5+ years of experience in data engineering, with hands-on exposure to machine learning, MLOps, or backend workflow automation. - Proficiency in SQL and Python, with experience using ML frameworks and libraries. - Deep expertise in modern data stack tools such as dbt, Snowflake, and Looker/Omni; experience with Kafka or Flink is a plus. - Strong understanding of semantic layer design, dimensional modeling, and data architecture best practices. - Knowledge of data governance, data quality, observability, and security/analytics best practices. - Experience building products with generative AI, LLMs, embeddings, or advanced ML techniques. - Excellent problem-solving, communication, and collaboration skills for working across global, cross-functional teams. Benefits - Competitive base salary: $151,000 – $205,000 depending on location, plus potential variable compensation. - Equity participation in the company. - 401(k) match, medical, dental, vision, and life insurance coverage. - Fully remote work with monthly home office stipend. - Flexible vacation and paid time off policy. - Family planning resources and specialized employee support programs. - Learning and development program for professional growth in market-focused areas.