Senior Embedded ML Engineer - Real-Time Edge AI

Allen Control Systems; Austin (TX); On-site; USD 140,000 - 190,000; Full time

Benefits offered by this job

Competitive salary

ACS Equity Package

Health, Dental, Vision Insurance

Paid Time Off

Job summary

Allen Control Systems is seeking a Senior Embedded Machine Learning Engineer to bring trained ML models to edge devices, balancing latency, memory, and power budgets while integrating C++ code for on-device inference. You will collaborate with CVML, firmware, and product teams to ensure fast, small, and reliable performance in the field, influencing the hardware and software stack.

This role requires deep expertise in ML for embedded hardware, real-time constraints, and hands-on tuning for edge

Qualifications

• Bachelor's or Master's in CS/EE or related field or equivalent practical experience.
• 10+ years of software or systems engineering, including 2+ years deploying ML to embedded/edge devices.
• Very strong proficiency in C/C++ (Python is optional).
• Proficiency with CUDA and PyTorch; experience with edge runtimes (TensorRT, ONNX Runtime).
• Practical experience with model optimization techniques (quantization, pruning, distillation).
• Ability to profile and optimize for latency, memory, and power on constrained hardware.

Responsibilities

• Optimize models using quantization, pruning, knowledge distillation, and graph optimization.
• Convert trained models into edge-runtime formats and deploy to target hardware.
• Profile inference latency, memory footprint, and power on accelerators; drive optimizations to meet targets.
• Design, write, and maintain C++ code for on-device inference, preprocessing, and memory management.
• Build test harnesses to verify on-device accuracy and catch regressions after optimization.

Skills

C/C++ proficiency

Python (optional)

Edge ML deployment

Model optimization

Linux/RTOS familiarity

Computer vision domain experience

Education

Bachelor's or Master's in CS/EE or related, or equivalent

Tools

CUDA

PyTorch

ONNX Runtime

TensorRT

Edge runtimes

Job description

Allen Control Systems is seeking a Senior Embedded Machine Learning Engineer to bring trained ML models to edge devices, balancing latency, memory, and power budgets while integrating C++ code for on-device inference. You will collaborate with CVML, firmware, and product teams to ensure fast, small, and reliable performance in the field, influencing the hardware and software stack.

This role requires deep expertise in ML for embedded hardware, real-time constraints, and hands-on tuning for edge