This role pays more than
81%
of similar roles. Most pay
$160,799–$233,800
— the shaded band above.
At the midpoint, this role pays about
$236k
versus about
$197k
for comparable roles.
Based on 240 similar postings.
Employer
About Nvidia
Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing
Nvidia currently has
892 open roles
on FindRole.
Listed pay typically runs
$184,000–$287,500
across 870 roles with salary data.
Senior Software Control Integration Engineer - Autonomous Vehicles
As a Senior Software Control Integration Engineer, you will join the autonomous vehicle team to develop, maintain, and integrate advanced auto control algorithms and software. You will build solutions for vehicle trajectory tracking, model predictive control, and state estimation while contributing to vehicle dynamics, urban driving, and emergency maneuvers. Your daily work involves writing new software modules from scratch, designing system architectures, and building performance benchmarks for vehicle control. You will also develop tools for calibration updates and maintain automated test suites to ensure software quality. The role requires expertise in C/C++, Matlab, Simulink, and Python to implement high-level algorithms into real-time embedded systems. Key technical domains include classical, modern, nonlinear, and robust control theories, as well as Kalman filters and Luenberger observers for state estimation within the autonomous vehicle software stack.
Develop and integrate autonomous vehicle control algorithms including trajectory tracking, model predictive control, and state estimation.
Design and write new software modules from scratch to drive the autonomous vehicle software stack.
Provide feedback on system architecture, failure modes, and redundancy for product-level design.
Create offline and online benchmarks to evaluate and improve vehicle control performance.
Build tools to tune vehicle dynamics and manage calibration updates across various vehicle types.
Troubleshoot control and actuation issues while integrating functionality across different hardware platforms.
Develop and maintain automated test suites to detect integration problems and ensure software quality.
What we're looking for
BS, MS, or higher degree in Mechanical Engineering, Electrical Engineering, Computer Science, or equivalent experience.
8+ years of work experience in relevant fields.
Hands-on application background in control theories including at least three of: classical, modern, nonlinear, MPC, optimal, robust, or sliding mode control.
Understanding of state estimation techniques such as Luenberger observer and Kalman filter.
Ability to develop vehicle models and perform parameter identification and benchmarking.
Experience in high-level algorithm design and prototyping in Matlab/Simulink/Python with product implementation in C/C++.
Experience in architectural design and software development with C/C++ for real-time embedded control systems like vehicle ECU systems.
Understanding of test and verification methodologies for automotive software, including unit and system level tests.
Experience integrating, tuning, and validating prototype or production control software with application, driver, and vehicle network layers. (preferred)
Hands-on experience in autonomous vehicle or advanced driver assist system development. (preferred)
Background in automotive safety concepts, failure mode analysis, and tools like FMEA. (preferred)
Experience developing and launching vehicle safety critical control system products. (preferred)
Understanding of deep learning and neural network concepts. (preferred)
12+ years engineering experience at an automotive OEM, tier-1 supplier, or autonomous driving startup. (preferred)
C++
Python
CUDA
TensorRT
PyTorch
Linux
QNX
Machine Learning
Model Inference
Quantization
Real-time Operating Systems
Autonomous Driving
ADAS
Robotics
performance-analysis
Software Architecture
TensorRT
CUDA
C++
Python
PyTorch
ONNX
Triton
Deep Learning Inference
Model Optimization
Quantization
Pruning
Linux
ISO 26262
QNX
Real-time Systems
DriveOS
Hypervisors
Reinforcement Learning
Python
C++
Deep Learning
GPU Clusters
Kubernetes
SLURM
High-Performance Computing
Data Pipelines
Simulation
Closed-Loop Simulation
PPO
GRPO
NuRec
Cosmos World Model
Autonomous Driving
AI Foundation Models
Robotics
CUDA
System Architecture
Large-scale ML Systems
Prediction
Decision
Planning
Control
Performance Tuning
Embodied AI
High-Performance Computing