You will be a technical authority in building scalable, high-performance backend and data systems that power our AI research engine – working closely with AI scientists, platform engineers, and product leaders. Beyond implementation, you will define technical direction, set engineering standards, and influence how we build and scale data-driven AI systems across the organization.
What Youll Do
Architecture & Technical Ownership
Own the architecture of core backend and data systems that support large-scale, AI-driven computation and inference.
Design and evolve high-performance analytical processing layers handling complex data workloads at scale.
Make and drive long-term architectural decisions across backend, data, and AI integration domains.
Define standards and best practices for performance, scalability, reliability, and data correctness.
Backend & Data Engineering
Design, implement, and optimize data-intensive backend services using Python.
Build and evolve analytical compute pipelines using technologies such as Polars, DuckDB, AirFlow, Iceberg, Spark and other ingestion and processing frameworks.
Collaborate closely with AI scientists to productionize algorithms and embed advanced intelligence into the platform.
Platform & Infrastructure
Architect and maintain containerized systems using Docker and Kubernetes.
Ensure systems are observable, debuggable, and operable in production environments.
Drive performance tuning and cost-efficiency across compute-heavy workloads.
Technical Leadership
Act as a technical authority and thought leader within the engineering organization.
Mentor senior engineers through design reviews, architectural guidance, and hands-on collaboration.
Serve as a force multiplier by unblocking complex technical challenges and raising the overall engineering bar.
What Were Looking For
A passion for building cutting-edge AI and data platforms that solve real, high-impact business problems.
Significant experience (8+ years) designing, building, and operating large-scale backend and data-intensive systems in production.
Deep Hands-on expertise in Python and strong understanding of analytical data processing and distributed systems.
Proven track record of owning and evolving complex system architectures over time.
Strong experience with containerization and orchestration technologies (Docker, Kubernetes).
Experience with Infrastructure-as-Code and modern delivery pipelines (e.g., Terraform, ArgoCD).
Demonstrated technical leadership through architectural ownership, mentorship, and influence across teams.
Comfort working at the intersection of backend engineering, data engineering, and AI systems.
