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Machine Learning Engineering Intern

Join our innovative Machine Learning team at Viant and help revolutionize the Ad Tech industry! As a Machine Learning Intern, you will work on real-world projects, collaborate with talented engineers and data scientists, and gain hands-on experience developing and deploying machine learning systems. You’ll have the opportunity to contribute to solutions that process hundreds of millions of events daily, learn MLOps best practices, and explore emerging technologies like Generative AI.


To be considered for this opportunity, candidates must be available for the full duration of our internship program, running from June 16th to September 18th. This is an in-person internship based at our Irvine, CA office, with an on-site schedule Monday through Thursday and the option to work from home on Fridays. Standard working hours are 9:00 AM to 5:00 PM (PST).


THE DAY-TO-DAY
  • Collaborate with engineers and data scientists to solve challenging business problems using machine learning.
  • Assist in designing, developing, testing, and deploying scalable AI/ML services for real-time, high-throughput production workloads.
  • Learn and apply MLOps principles to build robust model lifecycle pipelines.
  • Experiment with Generative AI techniques and contribute to production-grade systems.
  • Write clean, production-ready, and scalable code in a collaborative environment.
  • Participate in code reviews and team discussions to enhance the performance and quality of ML solutions.


DESIRED SKILLS
  • Currently pursuing or recently completed a Bachelor’s or Master’s degree in Computer Science, Machine Learning, or a related field.
  • Strong programming skills in one or more languages, such as Python or Go.
  • Familiarity with machine learning frameworks like TensorFlow or PyTorch.
  • Passion for learning and implementing MLOps best practices.
  • Strong problem-solving skills and a collaborative mindset.
  • Experience with distributed computing frameworks (e.g., Spark, Ray, or Dask).
  • Exposure to cloud platforms like AWS, GCP, or Azure.
  • Knowledge of the Ad Tech industry or digital advertising systems.
  • Understanding of data privacy, security, and compliance in machine learning.


BENEFITS
  • Hands on experience with our engineering team
  • Exposure to an agile work environment with ability to participate in team meetings and daily stand ups
  • Networking events
  • Fully stocked kitchen
  • Casual Office Atmosphere


$30 - $30 an hour
In accordance with California law, the range provided is Viant’s reasonable estimate of the compensation for this role. 

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 Machine Learning Engineering Intern, Viant

Join our innovative Machine Learning team at Viant and help revolutionize the Ad Tech industry! As a Machine Learning Engineering Intern, you'll dive into real-world projects, collaborating with talented engineers and data scientists. This is your chance to gain hands-on experience in developing and deploying machine learning systems that can process hundreds of millions of events every day! You'll learn about MLOps best practices and explore exciting technologies like Generative AI during your time in Irvine, CA. The internship program runs from June 16th to September 18th and is an in-person opportunity at our office. You’ll work an on-site schedule from Monday through Thursday, with Fridays being a work-from-home option, all within the standard hours of 9:00 AM to 5:00 PM PST. On a daily basis, you'll collaborate closely with the team, contributing to the design, development, testing, and deployment of scalable AI/ML services for real-time, high-throughput tasks. You'll also participate in code reviews, enhancing the quality of ML solutions while learning from peers. Ideal candidates are currently pursuing or have recently completed a Bachelor's or Master’s degree in Computer Science, Machine Learning, or a related field, and possess strong programming skills in languages like Python or Go. If you're passionate about learning and eager to apply MLOps practices in a collaborative environment, come make an impact at Viant!

Frequently Asked Questions (FAQs) for Machine Learning Engineering Intern Role at Viant
What are the responsibilities of a Machine Learning Engineering Intern at Viant?

As a Machine Learning Engineering Intern at Viant, you'll be responsible for collaborating with engineers and data scientists to tackle complex business problems utilizing machine learning techniques. You will assist in designing, developing, testing, and deploying scalable AI/ML services, as well as apply MLOps principles to build robust model lifecycle pipelines. Engaging in team discussions and reviews will enhance the quality of your contributions and foster a collaborative environment.

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What qualifications are needed for the Machine Learning Engineering Intern position at Viant?

To be considered for the Machine Learning Engineering Intern position at Viant, candidates should be currently pursuing or recently completed a Bachelor’s or Master’s degree in Computer Science, Machine Learning, or a related field. Strong programming skills in languages such as Python or Go, familiarity with machine learning frameworks like TensorFlow or PyTorch, and a passion for learning MLOps best practices are essential. Experience with distributed computing frameworks and exposure to cloud platforms like AWS or GCP are also beneficial.

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What is the duration of the Machine Learning Engineering Internship at Viant?

The Machine Learning Engineering Internship at Viant runs for the full summer duration from June 16th to September 18th. This gives interns ample time to immerse themselves in real-world projects and gain valuable experience in the field of machine learning.

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What kind of projects will I work on as a Machine Learning Engineering Intern at Viant?

During your internship at Viant, you will work on impactful real-world projects that involve developing and deploying machine learning systems. You’ll contribute to problems that require processing hundreds of millions of events daily, experiment with cutting-edge Generative AI techniques, and assist in creating scalable AI/ML services that cater to high-throughput production workloads.

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Can I work remote during my Machine Learning Internship at Viant?

Yes, while the Machine Learning Engineering Internship at Viant is primarily in-person in our Irvine, CA office, there is flexibility for remote work on Fridays. This hybrid arrangement allows you to balance collaboration with your team on-site while enjoying the comfort of working from home one day a week.

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Common Interview Questions for Machine Learning Engineering Intern
Can you explain your experience with machine learning frameworks like TensorFlow or PyTorch?

To effectively answer this question, draw on specific projects where you've utilized TensorFlow or PyTorch. Discuss the types of models you built, any challenges you encountered, and how you resolved them. Make sure to highlight your understanding of how these frameworks fit into the machine learning lifecycle and the benefits they offer.

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What MLOps practices have you followed in your previous work or academic projects?

When discussing MLOps practices, emphasize your familiarity with version control for code, continuous integration/continuous deployment (CI/CD) pipelines, model monitoring, and lifecycle management. Provide specific examples of how you applied these practices, such as automating workflows or using tools like Docker or Jenkins in your projects.

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How do you approach data privacy and security in machine learning projects?

Demonstrate your understanding of data privacy by discussing relevant regulations like GDPR or CCPA and how they apply to machine learning. Provide examples of how you've implemented practices to protect sensitive data, such as anonymization techniques, secure storage solutions, and ensuring compliance during data collection and model training.

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Describe a machine learning challenge you've faced and how you overcame it.

Be prepared to share a specific challenge you encountered, such as an imbalanced dataset or a model that was not performing as expected. Discuss the steps you took to analyze the issue, the strategies you applied to address it, and the final outcome. Focus on the learning experience and how it has shaped your approach to problem-solving.

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What excites you most about working in the Ad Tech industry?

This is a great opportunity to convey your passion for the Ad Tech industry. Discuss your interest in how data and machine learning can drive marketing strategies, enhance user engagement, and optimize advertising performance. Relate this back to specific trends in the industry, such as personalized advertising or leveraging big data.

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Can you describe your coding style and practices?

Describe your coding style as clean, organized, and modular. Discuss how you prioritize readability and maintainability. Provide examples of your practices, such as code reviews or adhering to coding standards, and how you ensure quality in collaborative projects.

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In what ways do you collaborate with team members on machine learning projects?

Discuss the importance of collaboration and communication in successful machine learning projects. Provide examples of how you've worked together with cross-functional teams, utilized tools like Git or Slack for collaboration, and contributed to peer code reviews to improve overall project outcomes.

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How do you keep current with advancements in machine learning and AI?

Share your methods for staying updated in the rapidly evolving field of machine learning and AI, such as following leading research papers, attending webinars, participating in online courses, or engaging with communities on platforms like GitHub or Kaggle. Highlight specific resources you find invaluable.

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What role do you see yourself playing in a team project involving machine learning?

Articulate your desired role based on your strengths; whether it's coding, data analysis, or more of a leadership position. Discuss how you can contribute to the team's diversity of skills, your communicative approach to sharing ideas, and how you would support other team members in achieving project goals.

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If given a dataset, what steps would you take to prepare it for a machine learning model?

When outlining how to prepare a dataset, address key steps such as data cleaning (handling missing values, removing duplicates), exploratory data analysis (identifying patterns and distributions), feature selection/engineering, and splitting the dataset into training and testing sets. Discuss the importance of thorough preparation in creating a successful machine learning model.

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

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Internship, on-site
DATE POSTED
March 12, 2025

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