Software Engineer, Machine Learning Infrastructure

Develop and maintain robust, scalable infrastructure platforms that support the needs of machine learning engineers across multiple business units. Design, build, and maintain data processing and moderation pipelines that handle large data volumes and integrate with trust and safety workflows. Deploy and manage production ML systems using internal deployment tools and optimize compute and storage resources to ensure reliability, scalability, and cost efficiency. Design, develop, and maintain application programming interfaces (APIs), including REST, gRPC, and GraphQL, to support internal ML platform services and system integrations. Oversee deployment, monitoring, and performance of ML systems using observability tools to ensure compliance with technical specifications and service-level objectives. Develop and implement model evaluation, validation, and quality assurance processes, including A/B testing frameworks and automated evaluation systems, to ensure model accuracy, reliability, and performance. Design, develop, and maintain scalable ML platform systems and data infrastructure using distributed data technologies, including Apache Spark, Kafka, Flink, and Databricks, to support global data processing and analytics needs. Analyze ML infrastructure requirements across business units and design technical solutions within defined scalability, performance, and cost constraints. Support technical design and implementation of ML lifecycle infrastructure, including model training, serving, monitoring, feature stores, and evaluation systems, with an emphasis on platform engineering and self-service capabilities. Mentor and provide technical guidance to junior engineers on ML systems, backend systems, scalable data pipelines, production reliability, and deployment best practices. Participate in hiring activities by conducting technical interviews and providing input on candidate evaluations. Develop and maintain technical documentation, including system designs, operational guides, and internal knowledge bases. Design and optimize recommendation systems and moderation data pipelines, applying best practices for data versioning, feature management, and model evaluation. Implement and optimization of backend and ML services to ensure reproducibility, reliability, and operational stability. Design and optimize large-scale data pipelines and database systems to support efficient data access patterns for ML workflows. Collaborate with cross-functional teams, including software engineers, data engineers, and ML engineers, to support the development and deployment of ML-enabled product features. Design and maintain infrastructure supporting large language model (LLM) workloads. Analyze and resolve complex distributed systems issues affecting performance, scalability, reliability, and availability of high-traffic ML applications. Research and evaluate emerging ML infrastructure technologies and conduct proof-of-concept implementations to support architectural and technology decisions. Stay current with advances in ML infrastructure, distributed systems, and data engineering, and apply industry best practices to ongoing platform development. Telecommuting may be permitted. When not telecommuting must report to 8800 Sunset Blvd. West Hollywood, CA 90069. Up to 10% domestic travel for team meetings and on-site trainings. Salary: $190K - $246K per year.

MINIMUM REQUIREMENTS: Bachelor’s degree or its U.S. equivalent in Computer Science, Computer Engineering, or a related field, plus 5 years of professional experience as a Machine Learning Engineer, Site Reliability Engineer, or any occupation/position/job title performing ML infrastructure or backend software engineering.

In lieu of a Bachelor’s degree plus 5 years of experience, the employer will accept a Master’s degree or U.S. equivalent in Computer Science, Computer Engineering ,or related field, plus 3 years of professional experience as a Machine Learning Engineer, Site Reliability Engineer, or any occupation/position/job title performing ML infrastructure or backend software engineering.

Must also have experience in the following: 3 years of professional experience designing and implementing large-scale distributed ML platform systems, using big data technologies including Apache Spark, Apache Kafka, Apache Flink, or Databricks. 3 years of professional experience using multiple modern programming languages, including Python, Scala, Java, or Go, to develop ML platform systems, backend services, data

processing jobs, and automation tools supporting the ML lifecycle. 2 years of professional experience working with modern cloud platforms (including AWS, Azure, or GCP) and utilizing infrastructure-as-code practices, containerization tools (Docker on managed orchestration platforms including Amazon EKS or Amazon ECS), and monitoring systems based on Prometheus metrics and Grafana dashboards, including experience operating services backed by a timeseries metrics store including Grafana Mimir. 2 years of professional experience designing and building infrastructure for recommendation systems, moderation pipelines, or large language model (LLM) serving and deployment systems, including experience with modern ML serving frameworks including Ray Serve or Triton, and with LLM-serving. 2 years of professional experience in large-scale database design and optimization, and data pipeline performance tuning to support efficient data access patterns for ML workflows, including working with analytical storage systems including Delta Lake or data warehouses, including Redis, ValKey or DynamoDB. 1 year of professional experience leading technical initiatives across multiple engineering teams, including establishing platform ownership models, providing hands-on technical guidance, and driving adoption of shared ML infrastructure components including standardized GitOps pipelines, and modern model-serving platforms. 1 years of professional experience designing and implementing CI/CD automation pipelines and GitOps practices for ML infrastructure, using tools including Terraform, Terragrunt, Helm, and internal GitOps systems (including Scaffold) together with continuous integration systems (including Jenkins or Buildkite) to manage deployment strategies including canary releases, bluegreen deployments, and zerodowntime migrations of backend services.