Senior Engineering Manager – Validation Workflows
| Company | Woven |
|---|---|
| Location | Ann Arbor, MI, USA |
| Salary | $Not Provided – $Not Provided |
| Type | Full-Time |
| Degrees | |
| Experience Level | Senior |
Requirements
- 7+ years of professional software engineering experience, including 2+ years in an engineering leadership role.
- Proven track record of delivering production-ready backend systems, developer tools, or data processing pipelines.
- Experience designing scalable, maintainable systems using best practices in modularity, testing, observability, and CI/CD.
- Familiarity with modern cloud-based development practices and services (e.g., AWS Lambda, Step Functions, container orchestration, or similar).
- Practical experience with Python and related ecosystems, and familiarity with orchestration frameworks, data pipelines, or distributed compute platforms.
- Strong cross-functional collaboration skills and the ability to align engineering direction with product and organizational goals.
Responsibilities
- Lead and Grow the Team – Hire, mentor, and support a high-performing team focused on building backend frameworks that power validation workflows for ADAS development.
- Deliver Scalable Developer Frameworks – Build frameworks that enable the development of large-scale simulation and evaluation workflows across real-world and synthetic scenarios.
- Unify Related Cross-organizational Workflows – Identify workflows with common data needs and provide unified tooling that can be re-used in multiple user-facing solutions.
- Uplevel Engineering Practices – Drive improvements in modularity, observability, testability of critical developer workflows.
- Collaborate Across Functions – Work closely with ADAS developers, simulation engineers, systems engineering, and infrastructure teams to align on requirements and ensure solutions integrate smoothly into the broader development ecosystem.
Preferred Qualifications
- Experience working on or integrating with simulation systems or building tools for simulation-based testing.
- Background in data engineering or data science, especially working with large-scale log data or performance metrics.
- Familiarity with verification and validation (V&V) methodologies, including test strategy development, regression analysis, and requirements-driven testing.
- Experience building developer-facing frameworks that support data analysis.
- Experience in ADAS, autonomy, robotics, or other safety-critical software domains.
- Collaborative experience with globally distributed teams or external partners working on shared evaluation frameworks and KPIs.