onsite
Machine Learning Engineer
Machine Learning Engineer
This Machine Learning Engineer role focuses on building and deploying real-time decisioning systems that leverage interaction feedback to learn and improve. Key responsibilities include owning the modeling logic, moving prototypes to production, and designing robust data pipelines, while continuously monitoring and improving system performance.
About the role
About the Role
As a Machine Learning Engineer, you will be responsible for designing, building, and releasing real-time decisioning systems that learn from interaction feedback at scale. This role involves owning the modeling logic, from user representation and signals to reward attribution that closes the loop and improves future interactions.
Responsibilities
- Design, build, and release real-time decisioning systems that learn from interaction feedback at scale.
- Own the modeling logic from how we represent users and signals to how reward attribution closes the loop and improves the next interaction.
- Move ideas from papers and notebook prototypes including online learning policies and counterfactual estimators to production code that runs behind real-time APIs.
- Design and build resilient streaming/batch pipelines that feed user state, reward signals, and offline replay.
- Run honest experiments, A/B test what you ship, design offline evaluation for what you cannot, and kill your own work when it doesn’t outperform against the baseline.
- Continuously monitor and improve the quality, latency, observability, and scalability of the systems.
- Collaborate across platform and product teams to turn research-grade ideas into production-grade products.
- Share context, mentor engineers, raise the technical bar of the team, and help set direction for how we do ML at scale.
Skills
Machine LearningReal time Decisioning SystemsOnline Learning PoliciesCounterfactual EstimatorsStreaming PipelinesBatch PipelinesA/B Testing