Staff Engineer, Test Automation (R5792)
San Diego · USD 150,000-230,000 / year · 5+ years experience · Onsite · Posted Sep 26, 2026
At a glance
- Experience
- 5+ years experience
- Workplace
- Onsite
- Location
- San Diego
- Salary
- USD 150,000-230,000 / year
Responsibilities
- Own the technical strategy, architecture, and roadmap for automated testing, verification, and MLOps quality across the Hivemind ecosystem.
- Design and maintain scalable test frameworks for autonomy software, backend services, APIs, operator-facing applications, and distributed hardware environments.
- Lead functional, integration, regression, system, performance, reliability, and end-to-end testing across simulation, edge-compute, software-in-the-loop, and hardware-in-the-loop environments.
- Build automated ML validation pipelines covering data quality, training reproducibility, model accuracy, robustness, regression, latency, resource utilization, and system integration.
- Establish CI/CD and continuous training workflows that provide versioning and traceability for datasets, models, configurations, evaluation results, and deployment artifacts.
- Develop scenario-based validation for autonomy models, including edge cases, degraded sensing or communications, distribution shifts, and representative mission conditions.
- Create observability, analytics, and failure-triage capabilities for software behavior, model and data drift, inference health, test results, and production performance.
- Build Python automation that improves test execution, parallelization, reporting, environment setup, experiment comparison, and developer productivity.
- Create test harnesses, simulators, stubs, mocks, and synthetic data capabilities that improve system testability and coverage.
- Collaborate with software, autonomy, machine learning, data, simulation, and systems engineers to define verification strategies and improve designs before implementation.
Requirements
- Typically 8+ years of relevant experience in software engineering, test infrastructure, developer tooling, MLOps, systems integration, or systems verification, or an equivalent combination of experience and demonstrated impact.
- 5+ years of experience building scalable automation frameworks or developer tooling in Python.
- Demonstrated success designing test, CI/CD, or MLOps infrastructure used across multiple engineering teams.
- Experience validating machine learning systems across the data, training, evaluation, packaging, deployment, and monitoring lifecycle.
- Understanding of ML quality risks such as data leakage, training-serving skew, nondeterminism, distribution shift, drift, model regression, and statistical acceptance criteria.
- Experience defining model-performance baselines, automated evaluation suites, release thresholds, and candidate-to-production comparison workflows.
- Experience testing GPU-accelerated infrastructure and workloads, including GPU scheduling, allocation, utilization, and resource contention in Kubernetes environments.
- Experience with performance benchmarking, profiling, and observability for GPU workloads, including identifying compute, memory, storage, networking, and data-loading bottlenecks.
- Experience validating multi-tenant Kubernetes environments, including RBAC, resource quotas, workload isolation, and scheduling behavior.
- Experience qualifying integrated hardware and software systems, including automated validation of compute, GPU, storage, networking, drivers, firmware, and deployed software configurations.
Benefits
- Bonus
- Benefits
- Equity
- temporary benefits package (applicable after 60 days of employment)
Skills
- Python
- Kubernetes
- Go
- TypeScript
- C
- C++
- MLflow
- Kubeflow
- Weights & Biases
- SageMaker
- Vertex AI
- CUDA
Show all 15 skills
- PyTorch
- Conan
- Poetry
