Staff Reliability Engineer (System-Level Modeling — SOFC Systems)

Bloom Energy; Bengaluru; On-site; INR 1,200,000 - 1,600,000; Full time

Job summary

Bloom Energy is seeking a Staff Reliability Engineer in Bengaluru to lead system-level reliability and availability modeling for solid oxide fuel cell distributed power generation systems. This role emphasizes modeling reliability using RBD and Monte Carlo simulations.

Responsibilities include developing system-level reliability models, mentoring team members, and collaborating with cross-functional stakeholders to ensure high product uptime. The ideal candidate has a degree in Engineering and 4-6 years of relevant experience.

Qualifications

• 4-6 years of experience in reliability engineering, focusing on system-level reliability/availability modeling.
• Hands-on expertise with RBD and availability modeling.
• Strong statistical foundation for reliability data interpretation.

Responsibilities

• Own and maintain system-level reliability models for SOFC products.
• Develop and validate RBD models using ReliaSoft BlockSim.
• Mentor engineers and analysts on reliability modeling methods.

Skills

Reliability modeling

Statistical analysis

Communication skills

Markov modeling

Monte Carlo simulations

Education

Bachelor’s or Master’s degree in Reliability, Mechanical, Electrical, Systems, Industrial Engineering

Tools

ReliaSoft BlockSim

Python

MATLAB

Job description

Job Overview

We are seeking a Staff Reliability Engineer to lead system-level reliability and availability modeling for Bloom Energy’s solid oxide fuel cell (SOFC) distributed power generation systems. This role focuses on building reliability block diagram (RBD) models, Markov/state-transition models, and Monte Carlo simulations to translate field and test data into actionable insights that improve uptime, service strategy, and product design decisions.

Key Responsibilities

• Own and maintain system-level reliability/availability models for SOFC products, subsystems, and service architectures (redundancy, bypass, standby system, maintenance strategies, operating profiles).
• Develop and validate RBD models using ReliaSoft BlockSim; establish modeling standards, libraries, and peer-review practices.
• Build Markov/state-transition models and run Monte Carlo simulations to quantify availability, MTBF/MTTR, and uncertainty/sensitivity.
• Lead data-to-model parameterization from field, test, and supplier inputs (failure/repair distributions, censoring, restoration factors, common-cause assumptions) and calibrate to fleet performance.
• Deliver trade studies and recommendations to influence design and service decisions; communicate results clearly to crossfunctional stakeholders.
• Mentor engineers/analysts on reliability modeling methods and support global execution across India and the U.S.

Required Qualifications

• Bachelor’s or Master’s degree in Reliability, Mechanical, Electrical, Systems, Industrial Engineering (or related).
• 4-6 years of experience in reliability engineering, including ownership of system-level reliability/availability modeling for complex, repairable systems.
• Proficiency in RBD and availability modeling, including hands‑on expertise with ReliaSoft BlockSim.
• Strong experience with Markov/state‑transition modeling and Monte Carlo analysis.
• Strong statistical foundation for reliability data interpretation (failure/repair distributions, censored data, uncertainty communication).
• Strong interpersonal and communication skills to collaborate with product engineering, service, operations, and reliability stakeholders across time zones.

Preferred Skills

• Experience with fuel cells, power generation, or high-temperature electro‑thermal systems.
• Experience using FRACAS/warranty/field data to calibrate models and validate projections against fleet performance.
• Reliability data analysis experience (e.g., Weibull, Crow‑AMSAA) and ability to automate analysis using Python/MATLAB (version control preferred).

Why You’ll Love This Role

• High impact: your models directly influence design choices, service strategy, and customer uptime for future and deployed fleets.
• End-to-end ownership: build the system view by connecting architecture, field performance, and maintainability into decision‑ready insights.
• Deep technical scope: apply RBD/BlockSim, Markov/state‑transition modeling, and Monte Carlo simulation to complex, repairable systems.