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Principal, Machine Learning Engineer

WHAT YOU’LL DO


Viant’s Machine Learning team is redefining the Ad Tech industry by building cutting-edge machine learning systems that automate the complex processes of digital advertising. Our mission is to develop real-time, autonomous AI-driven solutions capable of handling hundreds of millions of events daily, optimizing decision-making, and maximizing impact at scale.


As a Principal Machine Learning Engineer, you will play a pivotal role in designing and deploying large-scale, low-latency machine learning systems. You will drive innovation, lead technical initiatives, and develop state-of-the-art AI solutions that accelerate Viant’s growth and technological advancement. This role requires deep expertise in machine learning, system design, and a strong ability to collaborate across teams to deliver mission-critical AI solutions.


THE DAY-TO-DAY
  • Lead and Architect Scalable ML Solutions – Design, develop, and deploy real-time, high-throughput, low-latency ML services in production environments.
  • Advance MLOps Practices – Implement best-in-class MLOps strategies, automate model deployment, and optimize end-to-end machine learning workflows.
  • Innovate with Generative AI – Research, develop, and integrate cutting-edge generative AI techniques into production-grade systems.
  • Optimize Model Performance – Build high-performing ML models for large-scale decision-making while ensuring efficiency, reliability, and robustness.
  • Drive Technical Strategy – Work closely with Engineering, Data Science, and Product teams to define and implement Viant’s AI roadmap.
  • Mentor and Elevate the Team – Provide technical leadership, mentor junior engineers, and foster a high-impact, collaborative team culture.
  • Ensure Scalable Infrastructure – Work with distributed computing frameworks, cloud-based ML platforms, and modern AI tooling to enable large-scale training and inference.


MUST HAVE
  • Experience: 7+ years in Machine Learning Engineering or a similar role, with a track record of building and scaling ML solutions in production.
  • Generative AI Implementation: Practical experience in developing and deploying generative AI models, agentic systems for real-world applications.
  • Technical Expertise: Proficiency in Python or Go, with deep experience in designing and optimizing scalable ML architectures.
  • ML & Deep Learning Proficiency: Strong knowledge of modern deep learning techniques, including expertise in TensorFlow, PyTorch, LangChain, LangGraph or Llama Index.
  • Production-Grade ML Systems: Experience deploying large-scale ML models in high-throughput, low-latency environments.
  • MLOps Mastery: Hands-on experience with model versioning, CI/CD for ML, monitoring, and automated retraining pipelines.
  • Cloud & Distributed Computing: Proficiency with cloud platforms (AWS, GCP, or Azure) and distributed computing frameworks (e.g., Spark, Ray, or Dask).
  • Generative AI Implementation: Practical experience in developing and deploying generative AI models for real-world applications.
  • Cross-Functional Collaboration: Proven ability to work with product managers, data scientists, and software engineers to drive impactful AI solutions.


GREAT TO HAVE
  • Master’s or PhD in Computer Science, Machine Learning, or a related field.
  • Ad Tech industry experience or familiarity with digital advertising systems.
  • Expertise in implementing reinforcement learning or causal inference techniques for decision-making models.
  • Strong understanding of privacy-preserving ML techniques and compliance standards.


$220,000 - $260,000 a year
In accordance with California law, the range provided is Viant’s reasonable estimate of the compensation for this role. Final title and compensation for the position will be based on several factors including work experience and education.

Not the right position for you? Check out our other opportunities! 

Viant Careers


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About Viant


Viant Technology Inc. (NASDAQ: DSP) is a leader in AI-powered programmatic advertising, dedicated to driving innovation in digital marketing. Our omnichannel platform built for CTV allows marketers to plan, execute and measure their campaigns with unmatched precision and efficiency. With the launch of ViantAI, Viant is building the future of fully autonomous advertising solutions, empowering advertisers to achieve their boldest goals. Viant was recently awarded Best Demand Side Platform by MarTech Breakthrough, Great Place to Work® certification and received the Business Intelligence Group’s Innovation award for AI Advancements. Learn more at viantinc.com.


Viant is an equal opportunity employer and makes employment decisions on the basis of merit.  Viant prohibits unlawful discrimination against employees or applicants based on race (including traits historically associated with race, such as hair texture and protective hairstyles), religion, religious creed, color, national origin, ancestry, physical disability, mental disability, medical condition, genetic information, marital status, sex, reproductive health decision making, gender, gender identity, gender expression, age, military status, veteran status, uniformed service member status, sexual orientation, transgender identity, citizenship status, pregnancy, or any other consideration made unlawful by federal, state, or local laws.  Viant also prohibits unlawful discrimination based on the perception that anyone has any of those characteristics, or is associated with a person who has or is perceived as having any of those characteristics.


By clicking “Apply for this Job” and providing any information, I accept the Viant California Personnel Privacy Notice.

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What You Should Know About Principal, Machine Learning Engineer, Viant

Are you ready to take your Machine Learning career to the next level? At Viant, we're on a mission to redefine the Ad Tech industry, and we're looking for a talented Principal Machine Learning Engineer to join our innovative team. In this pivotal role, you'll design and deploy large-scale, low-latency machine learning systems that will help automate the complexities of digital advertising. Your expertise will be essential in driving innovation and leading technical initiatives that align with Viant's ambitious AI roadmap. You'll have the chance to work with cutting-edge technologies, including generative AI and advanced MLOps practices, allowing you to fully unleash your technical skills. At Viant, we value collaboration, and you’ll partner closely with cross-functional teams to ensure our AI solutions are impactful and efficient. Your leadership will not only help elevate our projects but also mentor junior engineers, fostering a culture of learning and innovation. If you're experienced in building scalable ML solutions and have a genuine passion for AI, you’ll find a wealth of opportunities to make your mark at Viant, all while enjoying a competitive salary of $220,000 - $260,000. Join us in our journey to empower advertisers and shape the future of autonomous advertising!

Frequently Asked Questions (FAQs) for Principal, Machine Learning Engineer Role at Viant
What are the primary responsibilities of a Principal Machine Learning Engineer at Viant?

As a Principal Machine Learning Engineer at Viant, you will lead the architecture of scalable machine learning solutions, develop real-time ML services, and advance MLOps practices. You'll also innovate with generative AI, optimize model performance, drive our technical strategy, and mentor junior engineers.

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What qualifications are required for the Principal Machine Learning Engineer position at Viant?

Candidates must have at least 7 years of experience in Machine Learning Engineering or a similar role. Proficiency in Python or Go, strong knowledge in deep learning techniques, and experience with deploying production-grade ML systems are critical for this position at Viant.

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What technical skills are valued for the Principal Machine Learning Engineer role at Viant?

Technical expertise in modern deep learning frameworks like TensorFlow and PyTorch, MLOps mastery, proficiency with cloud platforms like AWS, and distributed computing knowledge are highly valued for the Principal Machine Learning Engineer role at Viant.

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How does Viant support growth and innovation for its Principal Machine Learning Engineers?

Viant fosters an environment of continuous learning and innovation, enabling Principal Machine Learning Engineers to drive impactful AI solutions, mentor their peers, and contribute to a collaborative team culture while furthering their own technical prowess in cutting-edge fields like generative AI.

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What is the salary range for the Principal Machine Learning Engineer position at Viant?

The salary for the Principal Machine Learning Engineer position at Viant ranges from $220,000 to $260,000 per year, depending on experience and education, consistent with California law.

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Common Interview Questions for Principal, Machine Learning Engineer
How do you approach designing scalable machine learning solutions?

When designing scalable ML solutions, I focus on understanding the system requirements and the data flow first. I ensure that the architecture supports real-time processing and integrates seamlessly with existing IT infrastructure. Using modular design principles and cloud-based frameworks allows for flexibility and scalability.

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Can you explain your experience with MLOps and CI/CD for machine learning?

I have extensive experience with MLOps practices and CI/CD, automating the deployment and monitoring of machine learning models. This includes setting up version control for models, implementing automated testing pipelines, and utilizing orchestration tools to ensure robust end-to-end workflows.

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What generative AI models have you implemented in your previous projects?

In my previous roles, I have implemented various generative AI models, including GPT-3 for natural language processing tasks and Variational Autoencoders for data augmentation. I focus on integrating these models into production environments, ensuring they enhance existing systems effectively.

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How do you optimize model performance in a production environment?

Optimizing model performance involves careful analysis of model metrics and runtime behavior. I apply techniques like hyperparameter tuning, conducting A/B testing for different models, and employing monitoring tools to spot and fix inefficiencies promptly.

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Describe your experience with cross-functional collaboration on machine learning projects.

I believe collaboration is key to delivering successful ML projects. I've worked closely with product managers to clarify the project vision, data scientists for feature engineering, and software engineers for implementation. This teamwork ensures that all perspectives are considered and the final product is aligned with business goals.

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What strategies do you use to mentor junior engineers in machine learning?

I prioritize hands-on learning, providing challenging yet achievable tasks along with regular feedback sessions. I encourage junior engineers to take ownership of smaller projects and support them with resources and knowledge to foster their confidence and skill set.

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How do you stay updated with the latest advancements in machine learning?

To stay updated, I regularly read research papers, follow ML conferences, and participate in professional networks. Engaging in online courses and hands-on projects allows me to experiment with innovative techniques directly.

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What tools do you prefer for implementing MLOps?

I prefer tools like MLflow for tracking experiments and model versions, Docker for containerization, and Kubernetes for orchestration. These tools streamline the deployment processes and help manage complex workflows in production.

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Can you explain your understanding of privacy-preserving machine learning techniques?

Privacy-preserving techniques include differential privacy and federated learning. These methods allow organizations to leverage data while minimizing the risk of personal information exposure. I advocate for their use in projects involving sensitive data.

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What challenges have you faced in deploying machine learning models and how did you overcome them?

One challenge I faced was model drift after deployment. To combat this, I implemented a monitoring system to track model performance and set up automated retraining pipelines to keep the model aligned with new data distributions.

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Viant® is a technology company focused on driving growth for marketers.

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Full-time, on-site
DATE POSTED
February 12, 2025

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