Staff Full Stack Software Engineer, Platform Engineering

Cloudera•Austin, TX

2d•Remote

About The Position

At Cloudera, we empower people to transform complex data into clear and actionable insights. With as much data under management as the hyperscalers, we're the preferred data partner for the top companies in almost every industry. Powered by the relentless innovation of the open source community, Cloudera advances digital transformation for the world’s largest enterprises. Ready to take cloud innovation to the next level? Join Cloudera’s Anywhere Cloud team and help deliver a true “build your own pipeline, bring your own engine” experience. enabling data and AI workloads to run anywhere, without friction or vendor lock-in. We take the best of the public cloud- cost efficiency, scalability, elasticity, and agility and extend it to wherever data lives: public clouds, private data centers, and even the edge. Powered by Kubernetes, our hybrid architecture separates compute and storage, giving customers maximum flexibility and optimized infrastructure usage. We are looking for a Staff Full Stack Software Engineer to lead the architecture and delivery of AI-powered workflows that are core to our product. You will define the technical strategy, set quality and reliability standards, and deliver end-to-end systems that transform ambiguous customer needs into robust, measurable, and privacy-safe AI experiences. You’ll partner closely with Product, Design, Data Science, and GTM to deliver high-impact features at scale.

Requirements

Bachelor’s degree in Computer Science or equivalent, and 6+ years of experience
Expertise in at least one primary language (Rust preferred) and ecosystem (e.g., Python, Go, or Java) and cloud-native architectures (containers, service mesh, queues, eventing).
Proven experience in integrating AI/ML models into user interfaces. This is more than just calling an API; you should have experience building features like AI-powered assistants, natural language interfaces (e.g., text-to-SQL), proactive suggestions, or intelligent data visualization.
Familiarity with the AI/ML ecosystem: You understand the fundamentals of LLMs, vector databases, RAG, and prompt engineering.
Security & privacy mindset: Familiarity with data governance, PII handling, tenant isolation, and compliance considerations.

Nice To Haves

Platform thinking: Experience designing reusable AI workflow primitives, SDKs, or internal platforms used by multiple product teams.
Model ops: Experience with model lifecycle management, feature/embedding stores, prompt/version management, and offline/online eval systems.
Search & data infra: Experience with vector databases (e.g., Pinecone, Weaviate, pgvector), retrieval strategies, and indexing pipelines.
Observability: Built robust tracing/metrics/logging for AI systems; familiarity with quality dashboards and prompt diff tooling.
Cost strategy: Experience with model selection, distillation, caching layers, router policies, and autoscaling to manage spend.
Experience with managing machine learning workloads on container orchestration platforms like Kubernetes, including setting up GPU resources, managing distributed training jobs, and deploying models at scale.

Responsibilities

Own the architecture: Design, evolve, and document the end-to-end AI workflow stack (prompting, retrieval, tools/function-calling, agents, orchestration, evaluation, observability, and safety) with clear interfaces, SLAs, and versioning.
Ship production systems: Build reliable, low-latency services that integrate foundation models (hosted and self-hosted), and traditional microservices.
Own end-to-end delivery of features from the user-facing aspect (UI) to the backend services.
Implement robust testing frameworks, including unit, regression, and end-to-end tests, to guarantee deterministic and predictable behavior from our AI-powered data platform.
Establish safety guardrails and human-in-the-loop processes to maintain accuracy and ensure the production of ethical, responsible, and non-toxic outputs.
Optimize for cost & performance: Instrument, analyze, and optimize unit economics (token usage, caching, batching, distillation) and performance (p95 latency, throughput, autoscaling).
Drive data excellence: Shape data contracts, feedback loops, labeling strategies, and feature stores to continuously improve model and workflow quality.
Mentor and multiply: Provide technical leadership across teams, unblock complex projects, raise code/design standards, and mentor senior engineers.
Partner across functions: Translate product intent into technical plans, influence roadmaps with data-driven insights, and communicate trade-offs to executives and stakeholders.