Prognostics Reliability Engineer
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Job Location:
Foster, CA - USA
Monthly Salary:
Not Disclosed
Posted on:
5 days ago
Vacancies:
1 Vacancy
Job Summary
Reliability is foundational to scaling an autonomous mobility service. As the Prognostics Technical Lead you will define and lead Zooxs technical approach for predicting failures before they occur enabling smarter maintenance reducing unplanned downtime and improving fleet availability.
In this role you will sit at the intersection of field reliability data diagnostics vehicle engineering and service operations. You will lead the development of prognostics strategies for critical vehicle systems translate real-world failure behavior into deployable health monitoring approaches and help the organization decide where prognostics is the right lever versus diagnostics preventive maintenance or design change.
You will be expected to operate as both a technical leader and systems thinker: shaping the roadmap guiding model and monitor development aligning cross-functional partners and ensuring prognostics work is grounded in actual fleet impact. This role is ideal for someone who can move fluidly between failure physics field data estimation methods algorithm development and operational implementation.
In this role you will:
- Lead Zooxs technical strategy for prognostics across vehicle systems with a focus on reducing in-service failures and improving fleet availability
- Identify and prioritize the failure modes where prognostics can create meaningful operational value based on failure behavior detectability warning horizon and serviceability
- Develop and manage prognostics concepts methodologies and technical requirements for monitoring degradation predicting remaining useful life and detecting pre-failure behavior in fielded systems
- Partner with reliability design engineering service firmware/software and data teams to define the signals features infrastructure and product changes needed to enable effective prognostics
- Work with Design Reliability and Field Reliability to translate field performance repair history usage patterns and failure analysis into monitor strategies and deployable health indicators
- Guide the development validation and tuning of prognostic models and health monitoring algorithms using field and test data
- Establish technical frameworks for evaluating prognostic performance including sensitivity false positive burden lead time robustness and operational usefulness
- Drive tradeoff decisions between prognostics diagnostics inspection intervals and design improvement based on risk cost and implementation practicality
- Help build the data and analysis architecture needed to support prognostics at scale including data quality requirements feature generation monitor traceability and performance feedback loops
- Partner with service operations to ensure prognostics outputs translate into actionable maintenance decisions clear workflows and measurable business value
- Provide technical leadership and mentorship across the prognostics workstream raising the bar on methods rigor and cross-functional execution
- Communicate recommendations risks and roadmap priorities clearly to engineering leadership and cross-functional stakeholders
Qualifications
- Bachelors Masters or PhD in Mechanical Engineering Electrical Engineering Aerospace Engineering Systems Engineering Statistics Applied Mathematics Computer Science or a related field
- 8 years of experience in prognostics health monitoring reliability engineering condition-based maintenance or closely related domains
- Strong understanding of failure modes degradation behavior reliability fundamentals and the practical challenges of predicting failure in complex systems
- Experience developing or deploying prognostic anomaly detection or health monitoring methods for real-world hardware systems
- Experience working with field data sensor data maintenance data and failure analysis to drive engineering decisions
- Strong quantitative and analytical skills including experience with statistical modeling degradation analysis or machine learning approaches relevant to health monitoring
- Proficiency in Python or similar technical computing tools for analysis prototyping and model development
- Demonstrated ability to lead technically across functions and influence teams without direct authority
- Strong written and verbal communication skills with the ability to explain complex technical topics in an actionable way
Bonus Qualifications
- Experience in automotive EV robotics aerospace industrial equipment or other safety- and uptime-critical systems
- Experience working with vehicle telemetry embedded sensing CAN or log data diagnostics or onboard/offboard health monitoring architectures
- Experience building or guiding production-grade analytical workflows model pipelines or monitoring systems
- Experience with remaining useful life estimation fault detection and isolation or condition-based maintenance frameworks
- Experience evaluating model performance in production environments including false alert burden monitoring drift and field feedback loops
- Familiarity with service operations maintenance workflows and the real-world constraints of deploying predictive maintenance systems
- Experience operating in fast-moving product environments where reliability software hardware and operations must work together closely
- Familiarity with cloud-based data platforms and the practical challenges of deploying models beyond offline analysis
- Experience applying physics-informed statistical or machine learning approaches to degradation modeling and health monitoring
- Experience partnering closely with service or maintenance organizations to operationalize predictive maintenance workflows
- Strong intuition for when prognostics is the right solution versus when diagnostics preventive maintenance or design change is more effective
Base Salary Range
There are three major components to compensation for this position: salary Amazon Restricted Stock Units (RSUs) and Zoox Stock Appreciation Rights. A sign-on bonus may be offered as part of the compensation package. The listed range applies only to the base salary. Compensation will vary based on geographic location and level. Leveling as well as positioning within a level is determined by a range of factors including but not limited to a candidates relevant years of experience domain knowledge and interview performance. The salary range listed in this posting is representative of the range of levels Zoox is considering for this position.
Zoox also offers a comprehensive package of benefits including paid time off (e.g. sick leave vacation bereavement) unpaid time off Zoox Stock Appreciation Rights Amazon RSUs health insurance long-term care insurance long-term and short-term disability insurance and life insurance.
Required Experience:
IC
About Company
We’re reinventing personal transportation—making the future safer, cleaner, and more enjoyable for everyone. This is on-demand autonomous ride-hailing.
