SuperSonic by Unity is seeking a highly skilled Senior Machine Learning Engineer to join our dynamic Data Department. In this role, you'll work with petabyte-scale data to design, develop, and deploy state-of-the-art machine learning pipelines and systems. You'll build robust ML infrastructure using AWS services and orchestrate processes with Apache Airflow. If you're a passionate problem solver with a commitment to excellence and thrive in a collaborative environment, SuperSonic by Unity offers a unique opportunity to make a significant impact. Here, your expertise will drive innovation and help shape the future of machine learning, all while working on groundbreaking projects at a massive scale.
What you'll be doing
End-to-End Project Ownership: Take charge of feature design from concept through to deployment, ensuring robust, scalable solutions in a complex and dynamic environment.
Pipeline & Infrastructure Development: Architect and implement advanced machine learning pipelines that efficiently manage and process massive data volumes.
Collaboration & Teamwork: Embrace a team-first mindset"there is no I in team"actively supporting your peers while continuously learning and striving for excellence.
Problem Solving: Utilize your analytical skills to navigate technical challenges and deliver high-quality results on schedule, all while taking full ownership of your projects.
Requirements: What we're looking for
4+ years as a Machine Learning Engineer.
6+ years of programming experience in Python, Java, or Scala.
4+ years of experience with AWS and general DevOps practices.
Deep understanding of distributed systems, object-oriented programming, and design patterns.
Proficiency with distributed compute frameworks such as Spark, Dask, or Ray.
Hands-on expertise in designing, training, and deploying machine learning models.
Proven experience in building data pipelines using Apache Airflow.
Familiarity with MLOps practices and open source ML libraries (e.g., MLflow, CatBoost, LightGBM, XGBoost, scikit-learn, NumPy, Pandas).
Hands-on experience with microservices architecture, cloud technologies, and containerization tools like Docker and Kubernetes.
You might also have
Bachelor's or Master's degree in Computer Science or a related field is advantageous.
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