ML Applied Science Engineer

Summary

Join Braze's growing team as an RL researcher or practitioner to apply reinforced learning and solve real-world customer communication challenges. You will improve RL algorithms, develop diagnostic tools, conduct research on RL techniques, implement monitoring tools, collaborate with engineering teams, participate in customer implementations, and contribute to Braze's product strategy. The ideal candidate is an exceptional coder, problem solver, impact-driven, collaborative, structured, and a clear communicator with a Ph.D. in Computer Science, Machine Learning, or a related field (MS with relevant experience is also acceptable). You will work with various technologies including Python, Spark, BigQuery, FastAPI, Kubernetes, Airflow, and Terraform. Braze offers a collaborative and fun work environment with various benefits.

Requirements

• Exceptional coder: you have experience on writing clean, well-designed, versioned code; you care about good coding practices and terse, testable APIs
• Problem solver: you thrive on tackling complex, real-world challenges with novel ML approaches
• Impact-driven: you're motivated by seeing your research translate into tangible business outcomes
• Collaborative: You enjoy working closely with a team of driven individuals across multiple teams to get things done. You’re willing to both help and ask for help
• Structured and organized: you can structure a plan, align stakeholders, and see it through to execution
• Clear communicator: you are able to express yourself clearly and persuasively, both in writing and speech
• Ph.D. in Computer Science, Machine Learning, or a related field with a focus on Reinforcement Learning. MS with professional experience with RL is fine too
• Data Science/Back End: Python ML ecosystem, Spark, BigQuery, FastAPI
• Architecture/DevOps: Kubernetes, Airflow, Terraform, GCP

Responsibilities

• Improve RL algorithms to increase performance, sample efficiency, and robustness at scale
• Develop and apply advanced diagnostic tools, including off-policy evaluation methods
• Conduct research on state-of-the-art RL techniques and their applicability to marketing optimization
• Implement better monitoring and observability tooling
• Work closely with engineering teams to improve Brazes platform and develop APIs for Braze ML components
• Participate in customer implementations to gain insights into real-world use cases
• Contribute to Braze’s product strategy and roadmap

Benefits

• Competitive compensation that may include equity
• Retirement and Employee Stock Purchase Plans
• Flexible paid time off
• Comprehensive benefit plans covering medical, dental, vision, life, and disability
• Family services that include fertility benefits and equal paid parental leave
• Professional development supported by formal career pathing, learning platforms, and a yearly learning stipend