Senior ML Performance Engineer - Embedded Autonomy

Latitude AI; Palo Alto (CA); On-site; USD 179,000 - 269,000; Full time

Benefits offered by this job

Competitive compensation

Medical, dental, and vision insurance

401(k) with employer match

Equity compensation

Visa sponsorship

Job summary

Latitude AI is seeking a Senior Software Engineer - ML Performance to architect and implement scalable software for one of the largest-scale robotics projects. You will collaborate with teams spanning ML, robotics, cloud platforms, and compute systems to optimize AI performance on embedded platforms.

The role emphasizes end-to-end delivery, performance profiling, and tool development in a fast-paced, autonomous environment with a strong safety culture.

Qualifications

• Bachelor's degree in a related field with 4+ years of relevant experience (or advanced degrees with more experience).
• Proven experience with GPU/embedded architectures and performance-focused development.
• Strong Python and C++ programming skills; familiarity with PyTorch/ONNX stack.
• Experience delivering production-quality code in fast-paced environments.

Responsibilities

• Architect, design, and implement software solutions that scale from lab to customer for large robotics projects.
• Collaborate with teams to integrate and optimize algorithms and models.
• Profile and implement efficiency improvements from PyTorch to C++ within compute constraints.
• Develop infrastructure and tools to evaluate and optimize AI performance.
• Drive end-to-end projects with CI, testing, and code reviews in a safety-conscious setting.

Skills

Python

PyTorch

C++

GPU/embedded

Performance optimization

AI deployment

Communication

Education

Bachelor's in Computer Engineering/CS/EE/Robotics

Master's or PhD preferred

Tools

CUDA

Embedded systems

Git

Job description

Latitude AI is seeking a Senior Software Engineer - ML Performance to architect and implement scalable software for one of the largest-scale robotics projects. You will collaborate with teams spanning ML, robotics, cloud platforms, and compute systems to optimize AI performance on embedded platforms.

The role emphasizes end-to-end delivery, performance profiling, and tool development in a fast-paced, autonomous environment with a strong safety culture.