AboutClient is one of the world's fastest-growing AI companies accelerating the advancement and deployment of powerful AI systems.Client helps customers in two ways: Working with the world’s leading AI labs to advance frontier model capabilities in thinking, reasoning, coding, agentic behavior, multimodality, multilinguality, STEM and frontier knowledge; and leveraging that work to build real-world AI systems that solve mission-critical priorities for companies.Powering this growth is Turing’s talent cloud—an AI-vetted pool of 4M+ software engineers, data scientists, and STEM experts who can train models and build AI applications. All of this is orchestrated by ALAN—our AI-powered platform for matching and managing talent, and generating high-quality human and synthetic data to improve model performance. ALAN also accelerates workflows for model and agent evals, supervised fine-tuning, reinforcement learning, reinforcement learning with human feedback, preference-pair generation, benchmarking, data capture for pre-training, post-training, and building AI applications.About the Team: The ML Ranking Platform team builds and runs the services that coordinate contextualized and personalized rankings. We build ML infrastructure, including a scatter-gather ranking coordination service and feature indexes. We work closely with a team of ML Engineers to develop and productionize new models and with a team of ML backend engineers that build inference and training services and feature stores. Ranking is a core and growing part of our platform, and connecting users with the content they’re looking for is key to the future. This is particularly salient right now, as our society is currently engaging in a deep discussion around the algorithms used to match users with content. Our team mantra is “Intention over Attention”.Job requirementsKey ResponsibilitiesSystem design. This requires a good understanding of how the systems work, with particular focus on latency and scalability.Development. RP works mainly with Go and some Python. Skills in testing, SOLID and design patterns are a must.Systems architecture. RP makes extensive use of Kubernetes, Kafka, Redis and Postgres, and many internal and third party APIs, with focus on resilience, monitoring and alerting, and automation.Qualifications for this role:Proficiency in Python and Go, object oriented programming, design patterns.Proficiency in testing.Ability to naturally write clear, unconvoluted, testable code.Experience with Kubernetes, Kafka, Redis (user level).Experience with AWS and/or Google Cloud (user level).Bonus pointsExperience with highly scalable systems.Experience with Terraform.Experience with ML systems or frontend (React) are a big bonus!
