Software Engineer - Machine Learning

We’re Underdog.

The fastest-growing sports gaming company – ever.

We build innovative games, products, and experiences for American sports fans.

We’re here to shake up the fastest growing industry with bold ideas, custom-built tech, and the drive to win.

Founded in 2020, our team has built four of today’s most widely played fantasy games and launched the Underdog Sportsbook – built entirely in-house with our own technology. That means we control our product, move fast, and create experiences you won’t find anywhere else. 

In just over two years, we’ve reached over a $1.2 billion valuation, with investors like BlackRock, Spark Capital, SV Angel, Mark Cuban, Kevin Durant, and Adam Schefter. And we’re just getting started.

At Underdog, we believe that sports are for everyone. Join us.

About the role and why it’s unique:

• As a ML Engineer on the Data Platform team, you’ll be developing and deploying advanced machine learning models and algorithms on a cloud environment
• Implement end-to-end machine learning pipelines, starting from data collection, feature engineering, model training, evaluation, to deployment
• Build frameworks to measure model performance and accuracy in production environments, leveraging techniques such as parameter tuning and model optimization
• Implement and maintain monitoring, alerting, and logging mechanisms to ensure the health and accuracy of Underdog’s ML systems
• Utilize your understanding of machine learning algorithms, including supervised and unsupervised learning, deep learning, reinforcement learning, and ensemble methods, to build production systems
• Work closely with engineering and product teams to ensure seamless integration of machine learning services into Underdog’s data platform
• Collaborate with the data science and quant teams to deploy ML models into production systems
• Mentor junior engineers, lead technical initiatives, and drive results in a fast-paced, dynamic environment
• Lead code reviews, provide constructive feedback, and evangelize best practices to maintain code and data quality
• Research and keep up to date on emerging ML technologies and trends and focus on iteratively implementing them into Underdog’s engineering systems

Who you are:

• At least 4 years of experience building scalable ML model training and inference systems on a cloud environment (e.g. AWS, GCP, Azure)
• Highly focused on delivering results for internal and external stakeholders in a fast-paced, entrepreneurial environment
• Excellent leadership and communication skills with ability to influence and collaborate with stakeholders
• Prior experience with machine learning libraries and frameworks such as TensorFlow, PyTorch, and/or scikit-learn
• Familiarity with containerization and orchestration technologies such as Docker, Kubernetes, or ECS
• Experience with data streaming frameworks such as Apache Kafka, Apache Flink, or Kinesis
• Advanced proficiency with C++ and Python
• Advanced proficiency with SQL
• Experience with DevOps practices such as CI/CD pipelines, and infrastructure-as-code tools (e.g. Terraform, CDK)

Even better if you have:

• Strong interest in sports
• Prior experience in the sports betting industry
• Experience in building simulation or inference systems

Our targeted compensation rate for this position is between $135,000 and $150,000, depending on experience, plus equity. Think your skills are exceptional and warrant higher pay? Apply anyway! If we agree, we're willing to negotiate.

What we can offer you:

• Unlimited PTO (we're extremely flexible with the exception of the first few weeks before & into the NFL season)
• 16 weeks of fully paid parental leave
• A $500 home office allowance
• A connected virtual first culture with a highly engaged distributed workforce
• 5% 401k match, FSA, company paid health, dental, vision plan options for employees and dependents

#LI-REMOTE

This position may require sports betting licensure based on certain state regulations.

Underdog is an equal opportunity employer and doesn't discriminate on the basis of creed, race, sexual orientation, gender, age, disability status, or any other defining characteristic.