Machine Learning Engineer (Autonomous Vehicles)

Location: Santa Clara, CA; onsite 5 days a week

Salary: $170,000–$240,000 per year

Company

hirify.global develops autonomous vehicle technology and production machine learning systems for on-vehicle and cloud workflows.

What you will do

• Own the full machine learning lifecycle, including data strategy, preprocessing, training, evaluation, optimization, deployment, and monitoring.
• Develop and improve models for perception, prediction, planning, and scene understanding in autonomous driving systems.
• Design efficient neural networks using quantization, pruning, sparsification, compression, and other architecture optimization techniques.
• Integrate trained models into C++-based autonomy systems and optimize real-time inference for production vehicle hardware.
• Build high-throughput pipelines for training, evaluation, data processing, offline inference, dataset curation, and field-data feedback.
• Collaborate with perception, prediction, planning, infrastructure, systems, hardware, and autonomy teams to integrate reliable ML components into the vehicle platform.

Requirements

• MS or PhD in Computer Science, Machine Learning, Robotics, Electrical Engineering, Statistics, Optimization, or a related field.
• Strong Python and C++ programming skills, including experience with PyTorch or TensorFlow and high-performance production systems.
• Deep understanding of data curation, training, evaluation, ablation studies, deployment, inference optimization, and model diagnostics.
• Experience deploying and optimizing neural networks for real-time, embedded, robotics, autonomous driving, or other performance-constrained systems.
• Experience with software architecture, profiling, latency optimization, system-level debugging, and data-flow analysis.
• Experience with CUDA and TensorRT is highly desirable; cloud-based ML training and evaluation experience, preferably with Azure, is also valued.

Nice to have

• Experience with transformers, multimodal models, diffusion models, world models, or end-to-end driving models.
• Experience in autonomous driving, robotics, or other safety-critical real-time ML systems.
• Publications or technical contributions in efficient ML, autonomous driving, robotics, or related areas.
• Contributions to large-scale ML systems deployed in production.

Culture & Benefits

• Cross-functional collaboration across autonomy, systems, hardware, infrastructure, and perception teams.
• Work spans both on-vehicle and cloud-based machine learning workflows.
• Models are evaluated with simulation and real-world driving data and deployed on production vehicle hardware.