We are seeking an experienced Staff Backend Engineer to lead the engineering efforts behind our homegrown platform for serving and operating production-grade AI models and AI based algorithms.
This is a mission-critical role for someone passionate about building highly-scalable, GPU-aware, cloud-native systems that act as the connective tissue between algorithm research and product innovation. You will play a pivotal part in re-designing and evolving the platform, while supporting both research and application teams across the organization, and contributing to MLOps initiatives.
Key Responsibilities
Platform Ownership
Own the architecture, stability, scalability, and performance of the system.
Design and implement platform features that support both synchronous low-latency and asynchronous compute-heavy algorithm execution.
Enhance GPU management, scheduling, and resource allocation for optimal performance and cost-efficiency.
Ensure robust Kubernetes-based deployment and observability for a highly dynamic system.
Cross-Team Collaboration
Act as the technical bridge between Research and Application teams by translating requirements into scalable system designs.
Collaborate closely with algorithm developers to streamline model deployment processes.
Partner with backend engineers (primarily working in Ruby and Go) to integrate the research group algorithms into our company services.
Engineering Excellence
Advocate for high standards in code quality, observability, testing, and security.
Guide engineering integration efforts when consuming the different platform APIs.
Provide mentorship, support, and best practices to other engineers interacting with the platform.
Take part in general R&D efforts, supporting a broader production environment.
Platform Extension and MLOps
Contribute to the evolution of our platform to support a wider range of algorithmic workloads and model types.
Help shape tooling and infrastructure for model versioning, rollout, monitoring, and testing.
Collaborate with DevOps and Infrastructure teams to maintain operational excellence, system observability, and robust infrastructure support
This is a mission-critical role for someone passionate about building highly-scalable, GPU-aware, cloud-native systems that act as the connective tissue between algorithm research and product innovation. You will play a pivotal part in re-designing and evolving the platform, while supporting both research and application teams across the organization, and contributing to MLOps initiatives.
Key Responsibilities
Platform Ownership
Own the architecture, stability, scalability, and performance of the system.
Design and implement platform features that support both synchronous low-latency and asynchronous compute-heavy algorithm execution.
Enhance GPU management, scheduling, and resource allocation for optimal performance and cost-efficiency.
Ensure robust Kubernetes-based deployment and observability for a highly dynamic system.
Cross-Team Collaboration
Act as the technical bridge between Research and Application teams by translating requirements into scalable system designs.
Collaborate closely with algorithm developers to streamline model deployment processes.
Partner with backend engineers (primarily working in Ruby and Go) to integrate the research group algorithms into our company services.
Engineering Excellence
Advocate for high standards in code quality, observability, testing, and security.
Guide engineering integration efforts when consuming the different platform APIs.
Provide mentorship, support, and best practices to other engineers interacting with the platform.
Take part in general R&D efforts, supporting a broader production environment.
Platform Extension and MLOps
Contribute to the evolution of our platform to support a wider range of algorithmic workloads and model types.
Help shape tooling and infrastructure for model versioning, rollout, monitoring, and testing.
Collaborate with DevOps and Infrastructure teams to maintain operational excellence, system observability, and robust infrastructure support
Requirements:
8+ years of experience in software engineering, with 3+ years working on infrastructure/platforms involving ML/AI, GPU, or data-heavy systems.
Proficiency in Python and familiarity with backend languages such as Ruby and/or Go.
Strong understanding of Kubernetes internals and experience running GPU workloads in production environments.
In-depth knowledge of AWS services.
Experience architecting systems that support both real-time and asynchronous processing pipelines.
Familiarity with the ML lifecycle and MLOps practices, including CI/CD for models, monitoring, and rollback strategies.
Bonus Qualifications
Experience working in research-driven environments or alongside data scientists, algorithm research team and ML engineers.
Contributions to open-source projects related to model serving, Kubernetes operators, or ML platforms.
Experience supporting systems with diverse user groups across engineering and research disciplines.
8+ years of experience in software engineering, with 3+ years working on infrastructure/platforms involving ML/AI, GPU, or data-heavy systems.
Proficiency in Python and familiarity with backend languages such as Ruby and/or Go.
Strong understanding of Kubernetes internals and experience running GPU workloads in production environments.
In-depth knowledge of AWS services.
Experience architecting systems that support both real-time and asynchronous processing pipelines.
Familiarity with the ML lifecycle and MLOps practices, including CI/CD for models, monitoring, and rollback strategies.
Bonus Qualifications
Experience working in research-driven environments or alongside data scientists, algorithm research team and ML engineers.
Contributions to open-source projects related to model serving, Kubernetes operators, or ML platforms.
Experience supporting systems with diverse user groups across engineering and research disciplines.
This position is open to all candidates.
