Perception Software Engineer at Aeva, Inc.

Mountain View, California, United States -

Full Time

Start Date

Immediate

Expiry Date

23 Feb, 26

Salary

209600.0

Posted On

25 Nov, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, C++, MLOps, TensorRT, CUDA, GPU Performance, Data Collection, ETL Pipelines, SQL, NoSQL, Robotics, Machine Learning, Field Data Workflows, Annotation, Labeling, Integration

Industry

Automotive

Description

About us: Aeva’s mission is to bring the next wave of perception to a broad range of applications from automated driving to industrial robotics, consumer electronics, consumer health, security, and beyond. Aeva is transforming autonomy with its groundbreaking sensing and perception technology that integrates all key LiDAR components onto a silicon photonics chip in a compact module. Aeva 4D LiDAR sensors uniquely detect instant velocity in addition to 3D position, allowing autonomous devices like vehicles and robots to make more intelligent and safe decisions. Role Overview: We’re looking for an engineer who can own data collection, scalable data systems, MLOps workflows and ML deployment. You’ll work cross-functionally with perception, operations and labelling teams to build fast, reliable, and production-grade ML pipelines. \n What you will be doing Deploy and optimize ML models across cloud, on-prem, and edge devices using TensorRT and CUDA. Profile, debug, and tune GPU performance; support CUDA kernels, TensorRT integration, and inference optimization. Build and maintain CI/CD and automated MLOps pipelines for training, evaluation, and deployment. Maintain data-collection vehicle integrity, ensuring reliable HW/SW integration of cameras, LiDAR, compute units, storage, and networking. Collaborate with the operations team to plan, support, and execute autonomous-driving data collection runs. Design and operate scalable data ingestion and ETL pipelines for large sensor datasets (camera, LiDAR, logs) while maintaining efficient structuring of high-volume perception data. Work with external annotation and labeling vendors, ensuring high-quality labels, smooth delivery, and label validation loops. What You Have 5+ years industry experience (or 3+ with a Master’s) in software, ML, or robotics systems. Strong Python and C++ skills, with experience in PyTorch/TensorFlow and CUDA. Proven experience deploying and optimizing GPU inference (TensorRT, ONNX Runtime). Experience with large sensor datasets and building scalable data/ETL pipelines. Hands-on experience collecting data for robotics or perception systems (camera/LiDAR rigs, logging tools, HW/SW integration, field data workflows). Comfortable working with SQL/NoSQL datastores as part of data workflows. Experience collaborating with external labeling/annotation vendors. A strong, “can do” mindset which a commitment to owning and successfully executing high priority, high visibility initiatives What’s in it for you: Be part of a fast-paced and dynamic team Very competitive compensation and meaningful stock grants Exceptional benefits: Medical, Dental, Vision, and more Unlimited PTO: We care about results, not punching timecards \n $154,900 - $209,600 a year Salary pay ranges are determined by role, level, and location. Within the range, the successful candidate’s starting base pay will be determined based on factors including job-related skills, experience, certifications, qualifications, relevant education or training, and market conditions. These ranges are subject to change in the future. Depending on the position offered, equity, bonus, and other forms of compensation may be provided as part of a total compensation package, in addition to comprehensive medical, dental, and vision coverage, pre-tax commuter and health care/dependent care accounts, 401k plan, life and disability benefits, flexible time off, paid parental leave, and 11 paid holidays annually. \n

Responsibilities

The engineer will deploy and optimize ML models across various platforms and maintain data-collection vehicle integrity. They will also collaborate with teams to execute autonomous-driving data collection runs and design scalable data ingestion pipelines.