Staff Full Stack Software Engineer, Platform Engineering

Position Overview

At Cloudera, we empower people to transform complex data into clear and actionable insights. As part of our Anywhere Cloud team, the Staff Full Stack Software Engineer leads the architecture and delivery of AI-powered workflows that are central to our product. You’ll define technical strategies, build robust production systems, and partner with cross-functional teams to translate customer needs into measurable, secure, and privacy-safe AI experiences.

Key Responsibilities

• Own the architecture: Design, evolve, and document the end-to-end AI workflow stack with clear interfaces, SLAs, and versioning.
• Ship production systems: Build reliable, low-latency services integrating both foundation models and traditional microservices.
• Oversee end-to-end delivery: From the user-facing interface (UI) to backend services.
• Implement robust testing frameworks, establishing safety guardrails and human-in-the-loop processes for ethical and responsible outputs.
• Optimize for cost and performance: Analyze unit economics and performance metrics (latency, throughput, autoscaling) to enhance system efficiency.
• Drive data excellence by shaping data contracts, feedback loops, and feature stores.
• Mentor teams and elevate technical standards while unblocking complex projects.
• Partner across functions to translate product intent into technical plans and communicate trade-offs to stakeholders.

Required Qualifications

• Bachelor's degree in Computer Science or equivalent, with 6+ years of experience.
• Expertise in at least one primary language (Rust preferred) and proficiency in cloud-native architectures (containers, service mesh, queues, eventing).
• Proven experience in integrating AI/ML models into user interfaces beyond simple API calls.
• Solid understanding of the AI/ML ecosystem, including LLMs, vector databases, RAG, and prompt engineering.
• Strong security and privacy mindset, with familiarity in data governance, PII handling, and compliance considerations.

Preferred Qualifications

• Experience designing reusable AI workflow primitives, SDKs, or internal platforms.
• Familiarity with model lifecycle management, feature/embedding stores, and prompt/version management.
• Exposure to search & data infrastructure including vector databases and indexing pipelines.
• Proven ability to build robust observability systems with tracing, metrics, and logging.
• Experience with cost strategies involving model selection, distillation, caching layers, and autoscaling.
• Hands-on experience managing machine learning workloads on Kubernetes, including GPU resource setup and distributed training.

Benefits & Perks

• Generous PTO Policy
• Flexible WFH Policy to support work-life balance
• Mental & Physical Wellness programs
• Phone and Internet Reimbursement program
• Access to Continued Career Development
• Comprehensive Benefits and Competitive Packages
• Employee Resource Groups