Research | Staff | Full time | On-site | Bangalore
A founding research role building algorithms for vehicle health monitoring and predictive maintenance through reverse engineering of vehicle signals, diagnostics, and telematics data.
Own and build algorithms for vehicle health and preventive maintenance by reverse engineering vehicle data, signals, diagnostics, and telematics across Fleano's connected vehicle intelligence platform.
This is a Founding Staff role in an early-stage company. Candidates are expected to think and operate like owners — this role offers outsized impact, architectural ownership, and long-term upside.
This role is expected to be equity-only for the first 12-15 months, with cash component compensation thereafter as Fleano moves through MVP, pilots, and early commercial traction.
Analyze and interpret vehicle communication protocols including CAN, J1939, OBD-II, and UDS.
Work with raw vehicle data to identify undocumented signals, decode patterns and behaviors, and build signal mapping strategies.
Convert low-level signals into structured, usable parameters and define derived metrics and relationships between signals.
Design and implement algorithms for fuel consumption estimation, driver behavior analysis, fault detection, anomaly identification, and vehicle usage and performance patterns.
Build robust systems that handle noisy or incomplete data, multi-source inputs, and edge-case scenarios.
Validate outputs using real-world vehicle data and continuously refine models based on field feedback.
8–12+ years in automotive systems algorithm development covering predictive maintenance, driver behavior, or fuel systems.
Strong hands-on experience with CAN bus analysis and vehicle diagnostics including UDS, J1939, and OBD.
Experience working with real vehicle data, not just simulations or dashboards.
PhD in Automotive Systems.
Experience with telematics platforms or fleet management systems.
Background from automotive OEMs, Tier-1 suppliers, or connected vehicle companies.
