As a ML Engineer, you will:
Lead ML delivery: transforming research output (code, models, ideas) into robust, scalable, low-latency microservices in production
Help architect e2e solutions to real customer pains ranging from ingestion, integration, ETLs, DB design up to low-latency services
Design, build, and maintain automated workflows for ML models, including auto-trains, benchmarking, testing, performance gating, and production deployment.
Tackle complex backend challenges: optimizing API response times, managing database connectivity and concurrency at scale, balancing accuracys drive for complex questions with the business needs of fast responsiveness by making hard technical trade-offs between customer gains and business costs.
Design and optimize data pipelines and ETL processes, connecting our Snowflake data warehouse to our training environments.
Work within our existing ML infrastructure (Kubeflow, MLflow, KServe) to ensure smooth model lifecycles and performance monitoring.
Collaborate closely with ML Scientists, guiding them on software engineering best practices without slowing down their research.
Monitor and optimize production models for performance, cost efficiency, availability, and observability.
6+ years of backend software engineering experience designing, building, and maintaining large-scale, high-throughput production systems
Strong coding skills, Ability to write clean, maintainable code, OOP familiarity, package design, microservices etc.
Note: Work is in python, but strong engineers with deep Java/C# backgrounds who have some Python experience and are willing to transition fully are highly encouraged to apply.
Solid Database design & SQL skills, Deep understanding of SQL, experience working with relational and/or bigdata (columnar) databases, ORMs, and efficient query design.
API & Performant Design Proven experience – building robust systems, you understand how to handle concurrency, ETL tradeoffs, building fault-tolerant best effort data flows
