Machine Learning Engineer

Socket.dev; Santa Clara (CA); On-site; USD 170,000 - 240,000; Full time

Job summary

Socket.dev is hiring a high-impact Machine Learning Engineer to build, optimize, and deploy production ML models for their autonomous vehicle stack in Santa Clara, CA. You will collaborate with perception, prediction, planning, and systems teams to ensure models are efficient, scalable, and production-ready for both on-vehicle and cloud workflows.

The role is onsite five days a week in Santa Clara, focusing on end-to-end model development, real-time deployment, and scalable ML infrastructure.

Qualifications

• Strong Python with ML frameworks (PyTorch/TensorFlow).
• Experience deploying ML in production on real-time systems.
• Experience with ML workflows from data to deployment is expected.

Responsibilities

• End-to-end ML lifecycle ownership from data to monitoring.
• Develop and optimize autonomous driving models for perception, prediction, planning.
• Design efficient networks to meet real-time, embedded constraints.
• Integrate models into C++-based autonomy systems and optimize inference.

Skills

Python

PyTorch

TensorFlow

C++

ML deployment

Profiling

Education

MS or PhD in CS/ML/Robotics/EE/Statistics

Tools

CUDA

TensorRT

Job description

About the role

We are seeking a high-impact, technically deep Machine Learning Engineer to develop, optimize, and deploy production ML models across our autonomous vehicle (AV) stack. This role is ideal for engineers who enjoy building models end-to-end - from data and training through optimization and real-time deployment on autonomous vehicles.

You will work closely with perception, prediction, planning, infrastructure, systems, and hardware teams to ensure models are efficient, scalable, reliable, and production-ready for both on-vehicle and cloud workflows.

This role is onsite 5 days a week at our Santa Clara, CA office!

What you'll do

• End-to-End Model Development: Own the full ML lifecycle, including data strategy, preprocessing, training, evaluation, optimization, deployment, and monitoring.
• Autonomous Driving Models: Develop and improve models supporting perception, prediction, planning, and scene understanding.
• Efficient Neural Network Design: Optimize models using techniques such as quantization, pruning, sparsification, compression, and efficient architecture design to meet strict latency, compute, memory, and power constraints.
• Real-Time Deployment: Integrate trained models into C++-based autonomy systems and optimize inference for production vehicle hardware.
• Model Optimization: Profile and optimize neural networks using CUDA, TensorRT, and related technologies.
• Simulation and Evaluation: Analyze model performance using simulation and real-world driving data, identify failure modes, and drive improvements.
• Scalable ML Infrastructure: Build high-throughput pipelines for training, evaluation, data processing, and large-scale offline inference.
• Data Workflows and Tooling: Develop reliable pipelines for dataset curation, annotation, preprocessing, visualization, diagnostics, benchmarking, and continuous feedback from field data.
• Cross-Functional Integration: Partner with autonomy, systems, hardware, and infrastructure teams to ensure ML components integrate reliably into the broader vehicle platform.

What we're looking for

• Education: MS or PhD in Computer Science, Machine Learning, Robotics, Electrical Engineering, Statistics, Optimization, or a related field.
• Experience: Open to all experience levels. Leveling will be determined based on experience and technical depth.
• Programming & Frameworks:
• Strong Python skills and experience with frameworks such as PyTorch or TensorFlow.
• Strong C++ skills and experience integrating ML models into high-performance production systems.
• Core ML & Systems Expertise:
• Deep understanding of ML workflows, including data curation, training, evaluation, ablation studies, deployment, and inference optimization.
• Experience deploying and optimizing neural networks for real-time, embedded, robotics, autonomous driving, or other performance-constrained systems.
• Experience with model optimization techniques such as quantization, pruning, compression, and efficient architectures.
• Experience with software architecture, profiling, latency optimization, system-level debugging, and data flow analysis.
• Infrastructure & Compute Tools:
• Experience with CUDA and TensorRT is highly desirable.
• Experience with cloud-based ML training and evaluation pipelines, preferably Azure.

Bonus Qualifications:

• Experience with transformers, multimodal models, diffusion models, world models, or end-to-end driving models is a plus.
• Experience in autonomous driving, robotics, or other safety-critical real-time ML systems is strongly preferred.
• Publications or demonstrated technical contributions in efficient ML, autonomous driving, robotics, or related areas are a plus.
• Prior contributions to large-scale ML systems deployed in production.

Salary Range

$170,000 - $240,000