Staff Software Engineer

Cloudera•Austin, TX

6d•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 bring the best of public cloud — cost efficiency, scalability, elasticity, and agility — to wherever data lives: public clouds, private data centers, and the edge. Powered by Kubernetes, our hybrid architecture separates compute and storage to maximize flexibility and optimize infrastructure usage. This isn’t just cloud management — it’s about building a consistent, secure, and compliant cloud experience that gives organizations full access to all their data, anywhere. With the acquisition of Taikun, we’re simplifying Kubernetes and cloud management even further, creating a unified, scalable, future-ready platform. If you’re passionate about Kubernetes — not just using it, but building it at the core, managing workloads across hybrid clouds and datacenters, and obsessing over performance and DevOps — this is where you belong. We are seeking an experienced, independent, and driven Staff Backend Engineer to join our Anywhere Cloud team. In this role, you will lead the delivery of critical backend services, write high-performance, enterprise-ready Go code to drive customer adoption and expansion, and mentor engineers while managing key project priorities. You will also drive Anywhere Cloud’s roadmap by identifying bottlenecks, improving robustness, delivering well-tested code, collaborating across teams to ensure seamless platform performance, and supporting complex production issues when needed.

Requirements

6+ years of software engineering experience building large-scale distributed production systems
Expertise in at least one primary language (Go preferred) and ecosystem (eg: Rust) and cloud-native architectures (containers, service mesh, queues, eventing).
Proven expertise in advanced Kubernetes design and operation, including optimizing performance (e.g., node affinity, resource limits, horizontal pod autoscaling), service mesh implementation, and custom resource definition (CRD) development.

Nice To Haves

Experience designing reusable AI workflow primitives, SDKs, or internal platforms used by multiple product teams.
Built robust tracing/metrics/logging for AI systems; familiarity with quality dashboards and prompt diff tooling.
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.
Familiarity with the AI/ML ecosystem: You understand the fundamentals of LLMs, vector databases, RAG, and prompt engineering. Familiarity with tools such as MLflow, LangChain, or Hugging Face is a significant advantage.
Security & privacy mindset: Familiarity with data governance, PII handling, tenant isolation, and compliance considerations.

Responsibilities

Architect, build, and improve scalable backend systems and APIs.
Drive performance, reliability, and security across kubernetes based 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.