Senior Software Engineer - Custom Solutions

What we do

At The Trade Desk, we design and build custom solutions that extend our platform's capabilities, with a focus on quality, reusability, and customer success. From client‑facing applications and backend services to data science models, workflow automation, reports, and large‑scale data workflows, we deliver end‑to‑end systems that drive advanced campaign optimization, actionable analytics, and robust data operations at global scale. We are a small, high‑impact team that strategically enables advertisers to unlock advanced capabilities, solving unique partner needs and shaping technical patterns that benefit the wider platform.

Whether it's enabling advanced bidding strategies, streamlining data pipelines, or creating bespoke reporting experiences, we build with quality and scale in mind. We value a culture of collaboration, innovation, and learning. Here, engineers invent and simplify as they extend platform capabilities, striking the right balance between reusability and customization while partnering closely with clients and cross‑functional teams.

This role is ideal for someone passionate about building innovative, data‑driven solutions in the programmatic advertising space. You thrive in a high‑scale, data‑intensive environment where client needs are diverse, and you will play a key role in designing reusable patterns and standardized components that accelerate future solution delivery.

We’re looking for a Senior Software Engineer who combines deep software engineering expertise with strong statistical and data‑analysis skills to lead the development of data‑driven systems and solutions. This role blends software engineering excellence, machine learning development, and production‑grade MLOps—ideal for someone who enjoys bridging the gap between model experimentation and deployment ’ll work closely with data scientists, ML engineers, and product teams to develop ML pipelines, optimize data workflows, automate deployment processes, and ensure that machine learning models deliver real‑world value reliably and efficiently.

• Drive architecture and design for end‑to‑end systems within Custom Solutions, influencing platform patterns and best practices across teams
• Develop custom APIs and backend services that integrate seamlessly with the core platform
• Optimize data storage, retrieval, and API performance for global‑scale programmatic workloads
• Apply best practices in software engineering across the stack for scalability, maintainability, and resilience
• Create and maintain data processing workflows and automation solutions to support large‑scale advertiser operations
• Collaborate with data scientists to integrate productionized models and algorithms into production‑ready systems for advanced bidding and analytics
• Design and optimize large‑scale data pipelines supporting ML workflows
• Establish and enforce Engineering and MLOps best practices across our teams
• Implement monitoring, logging, and observability across frontend and backend systems to maintain reliability and performance
• Identify opportunities to standardize patterns to accelerate future solution delivery

Our culture is much deeper than just having fun together (though we do that well, too). We take pride in our engineers being trust‑builders, generous givers, scrappy problem‑solvers, and gritty pursuers of excellence. You do not have to meet all the requirements below, but we believe that people who meet most of them will have a higher likelihood of succeeding in this role:

• Deep experience designing and building large‑scale, distributed systems across client‑facing applications, backend services, and data workflows
• Proven ability to own architecture and technical direction for complex, end‑to‑end systems
• Strong knowledge of data processing frameworks and workflows, with experience scaling systems for data‑intensive workloads
• Strong experience developing, testing, and optimizing predictive models and statistical algorithms
• Expertise in big data processing and analytics frameworks—and tools such as Apache Spark, Kafka, Airflow, Hadoop, Snowflake, and distributed data storage systems (S3, Big Query, Redshift, or Delta…