
Chief AI System Reliability Engineer
5 days ago
About Us:
We empower individuals who run physical operations with tools to make their work safer, more productive, and profitable.
For the first time ever, safety, operations, and finance teams can manage drivers, vehicles, equipment, and fleet spend in a single system.
The platform combines industry-leading AI with complete visibility and control, significantly reducing manual workloads by automating tasks.
We serve over 100,000 customers across various industries, including transportation, logistics, construction, energy, field service, manufacturing, agriculture, food and beverage, retail, and the public sector.
Job Description:We seek an experienced Engineering Manager to lead a team focused on AI feature reliability, robustness, and performance.
This team will ensure our AI-powered features operate with high precision and consistency in production environments.
You will build and grow a team of engineers and analysts responsible for data analysis, debugging, root-causing production issues, and figuring out long-tail failures and corner cases across our AI stack.
Responsibilities:- Team Leadership: Build, manage, and mentor a high-performing engineering team focused on AI system reliability.
- Failure Mitigation: Lead efforts to detect, diagnose, and mitigate failures and degradation across AI features in production.
- Tool Development: Design tools, dashboards, and processes to surface anomalies, performance regressions, and edge cases.
- Cross-Functional Initiatives: Drive cross-functional initiatives to improve monitoring, observability, and data quality.
- Data Analysis: Oversee data analysis, annotation processes, and AI system workflows to improve model performance.
- Technical Documentation: Develop and maintain technical documentation for AI workflows, ensuring clarity and efficiency.
- Performance Optimization: Monitor key AI performance metrics and implement strategies for optimization.
- Culture Development: Champion a culture of ownership, rigor, and continuous learning within the team.
- 2+ years of experience managing high-performing engineering teams, preferably in AI, ML infrastructure, or production systems.
- Strong understanding of AI feature development lifecycles, data pipelines, model debugging, and post-deployment validation.
- Proven experience with root cause analysis, reliability engineering, or debugging complex distributed systems.
- Strong analytical mindset and ability to dig deep into data to uncover insights.
- Experience working cross-functionally with ML researchers, product managers, customer success and support teams, and data engineers.
- Excellent communication and leadership skills; ability to drive clarity in ambiguous environments.
- Proficiency in Python and SQL for data processing and reporting.
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