Inference ML API SDET
Senior SDET leading testing strategy, automation, and quality validation for Cerebras ML Inference API features, including accuracy, fairness, and performance at scale for AI models and hardware-backed services. Requires 5+ years experience building test frameworks and debugging large-scale distributed systems.
About the job
Responsibilities
- Architect and own end-to-end test strategies for new features, developing scalable tests, frameworks, and tooling to ensure quality.
- Lead contributions to industry-standard benchmarks and drive adoption of rigorous evaluation methodologies.
- Define and drive automation initiatives to significantly improve internal engineering efficiency and test coverage.
- Make strategic decisions around coverage trade-offs, resource requirements, and risk-based testing priorities.
- Serve as a technical anchor in a highly agile environment, adapting quickly to shifting priorities while maintaining quality standards.
- Mentor and guide junior SDETs on testing methodology, debugging practices, and automation development.
- Proactively identify systemic quality gaps and drive cross-functional initiatives to address them.
- Lead and facilitate effective technical communication across teams and time zones.
Requirements
- 5+ years of relevant industry experience in software integration, development, or quality engineering.
- Deep expertise in automation and programming using one or more languages such as Python, C++, or Go; ability to design and build reusable test frameworks from the ground up.
- Proven experience testing compute, machine learning, networking, or storage systems within large-scale enterprise environments.
- Strong track record of debugging complex issues across distributed, scaled-out deployments.
- Demonstrated ability to lead cross-functional quality initiatives involving product development, product management, customer operations, and field teams.
- Excellent verbal and written communication skills, with experience presenting technical findings to both engineering and leadership audiences.
- Strong organizational skills, ownership mindset, and ability to drive projects to completion independently.
- Experience leading and mentoring engineers across geographically dispersed teams and time zones.
Nice-to-Haves
- Hands-on experience with ML workloads including LLM and/or multimodal training or inference.
- Deep familiarity with hardware architecture, performance optimizations, compilers, and ML frameworks.
- Experience designing test strategies for distributed systems, cloud infrastructure, and security validation.
- Experience with microservices deployment, debugging, and orchestration at scale.
- Prior experience owning or significantly contributing to a team's quality engineering culture or test infrastructure.
Skills
Python, C++, Go, Machine Learning, LLMs, Distributed Systems, Microservices, Cloud Infrastructure, Debugging, Test Automation, Benchmarking
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