Computer Vision Engineer
Camera-based detection, segmentation, and tracking pipelines for vehicle perception.
Automotive ER&D · Autonomous Driving Practice
Talent for Autonomous Driving Systems
Autonomy programs need engineers who move across computer vision, sensor fusion, AI/ML perception, and planning — not specialists boxed into one legacy discipline. Propellence finds the bridge talent traditional automotive recruiting misses.
10+ yrs
Automotive ER&D domain focus
5
Regions: India, Germany, USA, Japan, APAC
Cross-Domain
AI/ML + embedded search, one team
Computer VisionCAMERA · VISION PIPELINE
Sensor FusionLIDAR · RADAR · CAMERA
PerceptionDETECTION · TRACKING
Planning & ControlPATH · MOTION PLANNING
Safety & ValidationSOTIF · SIMULATION
Autonomous-driving hiring is a convergence problem: six disciplines, one stack.
Roles We Fill
Cross-domain autonomy roles, precisely defined
We search by what the autonomy stack actually needs — often a hybrid profile beyond a standard job description.
Sensor Fusion Engineer
LiDAR, radar, and camera fusion into a calibrated environmental model.
Perception Systems Engineer
Detection, classification, and tracking that makes raw sensor data actionable.
ML Engineer, Autonomy
Training, validation, and real-time deployment of perception and prediction models.
Motion Planning Engineer
Path planning, trajectory optimization, and vehicle-control algorithms.
Head of Autonomous Driving
Cross-domain roadmap and build-vs-buy decisions across the autonomy stack.
Why Propellence
Built for a search problem that crosses domains
The right candidate often sits at the intersection of AI/ML and safety-critical embedded systems.
Bridge Talent
We identify engineers who translate ML research into real-time embedded systems.
One Search, Six Domains
Vision, fusion, perception, planning, AI/ML, and safety validation evaluated together.
Confidential, Passive Search
Direct, discreet access to engineers building next-generation autonomy stacks.
Global Reach
Coverage across India, Germany, the USA, Japan, and APAC autonomy hubs.
FAQ
Autonomous driving talent acquisition, explained
The full stack from sensor data through computer vision, fusion, perception, AI/ML, planning, and control.
The scarcest candidates understand both ML research and real-time, safety-critical embedded constraints.
Yes — from critical perception or planning hires to coordinated multi-hire builds across the full stack.
Scaling an autonomous driving team?
Tell us which domains are missing — we’ll bring candidates who bridge them.