Machine Learning Engineer
Description For Machine Learning Engineer
Underdog, founded in 2020, is revolutionizing the sports gaming industry as the fastest-growing company in the space. They've built four of today's most popular fantasy games and launched their own Underdog Sportsbook with proprietary technology. Valued at nearly $500 million within just two years, they're backed by notable investors including Mark Cuban, Kevin Durant, BlackRock, and SV Angel.
As a Machine Learning Engineer on the Data Platform team, you'll be at the forefront of developing and deploying advanced ML models in a cloud environment. You'll be responsible for implementing end-to-end ML pipelines, from data collection to deployment, while building frameworks to measure model performance and accuracy in production.
The role demands expertise in various ML algorithms, including supervised/unsupervised learning, deep learning, and reinforcement learning. You'll work closely with engineering, product, data science, and quant teams to ensure seamless integration of ML services into Underdog's data platform.
Key responsibilities include mentoring junior engineers, leading technical initiatives, conducting code reviews, and staying current with emerging ML technologies. The ideal candidate brings 4+ years of experience building scalable ML systems, strong leadership skills, and proficiency in technologies like TensorFlow, PyTorch, Docker, Kubernetes, and various programming languages.
Join a company that believes sports are for everyone and offers competitive compensation, unlimited PTO, comprehensive benefits, and a connected virtual-first culture. This is an opportunity to shape the future of sports gaming with a rapidly growing industry leader.
Responsibilities For Machine Learning Engineer
- Develop and deploy advanced machine learning models and algorithms on cloud environment
- Implement end-to-end machine learning pipelines
- Build frameworks to measure model performance and accuracy
- Implement monitoring, alerting, and logging mechanisms
- Work with engineering and product teams for ML services integration
- Collaborate with data science and quant teams
- Mentor junior engineers and lead technical initiatives
- Lead code reviews and maintain code quality
- Research and implement emerging ML technologies
Requirements For Machine Learning Engineer
- At least 4 years of experience building scalable ML model training and inference systems on cloud environment
- Excellent leadership and communication skills
- Experience with machine learning libraries like TensorFlow, PyTorch, scikit-learn
- Familiarity with Docker, Kubernetes, or ECS
- Experience with data streaming frameworks (Apache Kafka, Apache Flink, or Kinesis)
- Advanced proficiency with C++ and Python
- Advanced proficiency with SQL
- Experience with DevOps practices and infrastructure-as-code tools
Benefits For Machine Learning Engineer
- Unlimited PTO
- 16 weeks paid parental leave
- $500 home office allowance
- Virtual first culture
- 5% 401k match
- Company paid health, dental, vision for employees and dependents
- FSA
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