Senior Software Engineer - ML OPS Tooling

The Trade Desk is a global technology company with a mission to create a better, more open internet for everyone through principled, intelligent advertising. Handling over 1 trillion queries per day, our platform operates at an unprecedented scale. We have also built something even stronger and more valuable: an award-winning culture based on trust, ownership, empathy, and collaboration. We value the unique experiences and perspectives that each person brings to The Trade Desk, and we are committed to fostering inclusive spaces where everyone can bring their authentic selves to work every day.

Do you have a passion for solving hard problems at scale? Are you eager to join a dynamic, globally- connected team where your contributions will make a meaningful difference in building a better media ecosystem? Come and see why Fortune magazine consistently ranks The Trade Desk among the best small- to medium-sized workplaces globally.

What We Do:

The ML Ops team at The Trade Desk is dedicated to empowering our data scientists and data engineers by building the infrastructure and tools they need to innovate with AI and seamlessly transition machine learning models from research to production. Our mission is to accelerate this journey while upholding the highest standards of safety, scalability, and quality.

We're developing a robust platform that provides standardized tools and frameworks across the entire machine learning lifecycle, including:

• Enabling cross-team feature sharing
• Simplifying model deployment, monitoring, and experimentation
• Reducing friction in building and productionizing models end-to-end

Our goal is to give teams the autonomy to take their models from idea to impact-faster and more reliably than ever before.

About the Role:

Senior Software Engineers on the ML Ops team at The Trade Desk are end-to-end owners

You'll play a critical role in designing, building, and delivering the infrastructure and tools that empower data scientists and machine learning engineers to take models from research to production-safely, quickly, and at scale.

Our ML Ops team tackles a wide range of challenges, including:

• Building scalable systems to support feature engineering, model training, and inference
• Creating standardized frameworks for model deployment, monitoring, and versioning
• Developing internal platforms that reduce friction and increase velocity for ML teams
• Ensuring high reliability and performance across both cloud and physical data centers

The unique part of the role depending on your interests and expertise, you might lead initiatives involving container orchestration, CI/CD for ML workflows, model serving, data processing, observability tooling for models in production, or cross-team data pipelines for shared feature stores. But your impact won't stop at code.

As a senior engineer, you'll also shape our team through mentorship, cross-functional collaboration, and technical leadership. You'll raise the bar not only for infrastructure and systems-but also for the people around you.

Who We Are Looking For:

• A strong collaborator. You're a clear communicator who partners effectively with data scientists and engineers to identify and eliminate friction in the ML workflow.
• Efficiency-minded. You're passionate about building tools and systems that empower others-improving the productivity of data scientists and data engineers across the organization.
• Technically versatile. You have experience working with large-scale data systems-hands-on experience with Spark or distributed data pipelines is a plus.
• ML-aware. While you don't need to be a model builder, familiarity with ML modeling and deployment concepts is a bonus.
• A proven leader. You've led diverse teams of engineers and driven impactful projects end-to-end, showing initiative beyond your immediate scope.
• A technical driver. You own projects from planning through implementation. You write production-grade code, lead architecture discussions, and conduct thoughtful code reviews.
• A principled engineer. You understand the fundamentals and adapt tools and techniques from first principles-especially when off-the-shelf solutions don't scale.
• Comfortable across the stack. You've built always-on systems, services, and infrastructure-spanning backend systems, APIs, and data workflows.
• Product-oriented. You care about impact. You seek to understand the "why" behind your work and help shape the future of ML platforms that drive real business value.
• Humble and confident. You lead with curiosity, advocate for your ideas with evidence, and collaborate without ego. You're not afraid to be wrong-and quick to support your teammates.
• Diversity-minded. You welcome diverse perspectives and actively work to build an inclusive, thoughtful engineering culture.

Variety of technical opportunity is one of the best things about working at The Trade Desk as a software engineer which is why we do not expect you to know every technology we use when you start. What we care about is that you can learn quickly and find solutions to complex problems using the optimum tools for the job. What you know is less important than how well you learn and innovate. We don't need engineers who know all the answers; we need engineers who can invent the answers no one has thought of yet, to the questions yet to be asked.

The Trade Desk does not accept unsolicited resumes from search firm recruiters. Fees will not be paid in the event a candidate submitted by a recruiter without an agreement in place is hired; such resumes will be deemed the sole property of The Trade Desk. The Trade Desk is an equal opportunity employer. All aspects of employment will be based on merit, competence, performance, and business needs. We do not discriminate on the basis of race, color, religion, marital status, age, national origin, ancestry, physical or mental disability, medical condition, pregnancy, genetic information, gender, sexual orientation, gender identity or expression, veteran status, or any other status protected under federal, state, or local law.

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