Sr. Embedded Machine Learning Engineer

Allen Control Systems•Austin, TX

About The Position

Allen Control Systems (ACS) is a cutting-edge defense startup developing an autonomous gun turret using advanced computer vision and control systems to detect, track, and neutralize enemy drones. We are hiring a Senior Embedded Machine Learning Engineer to manage the entire process of deploying trained machine learning models and their supporting code onto resource-constrained edge hardware. This role requires expertise in machine learning, embedded systems, and hardware engineering. The primary responsibilities include optimizing models for strict constraints on latency, memory, power, and thermal budget, and developing the C++ code for on-device model execution and pre/post-processing. This is a cross-functional role, collaborating with CVML, embedded/firmware, and product teams to ensure models are accurate, fast, small, and dependable in the field.

Requirements

Bachelor's or Master's Degree in Computer Science, Electrical Engineering, Computer Engineering or a related field, or equivalent practical experience.
10+ years of professional software or systems engineering experience.
At least 2 years focused on deploying ML models to embedded or edge devices.
Very strong proficiency in C/C++ (or Python, but C++ is most important).
Proficiency with CUDA.
Hands-on experience with PyTorch.
Experience with at least one edge runtime or inference format (TensorFlow Lite, ONNX Runtime, TensorRT, or similar).
Practical experience with model optimization techniques such as quantization (post-training and quantization-aware), pruning, or distillation.
Demonstrated ability to profile and optimize for latency, memory, and power on constrained hardware.
Working knowledge of embedded or edge platforms (e.g., NVIDIA Jetson, Google Coral, Qualcomm, ARM Cortex, or comparable NPUs and SoCs).
Working knowledge of Linux or an RTOS.
Solid grasp of computer architecture concepts relevant to inference, including memory hierarchy, fixed-point arithmetic, and accelerator offload.
Domain experience in computer vision or sensor processing on device.

Responsibilities

Model optimization using techniques like quantization, pruning, knowledge distillation, operator fusion, and graph optimization.
Model conversion and deployment using ONNX and TensorRT for edge runtimes.
Hardware bring-up and benchmarking on accelerators (GPUs, NPUs, DSPs, TPUs, FPGAs), measuring and optimizing latency, throughput, memory footprint, and power.
Designing, writing, and maintaining C++ code for model inference, including pre/post-processing, data/memory management, threading, and system interfaces, ensuring real-time constraints and memory budgets are met.
Building test harnesses to validate on-device accuracy and catch regressions.
Contributing to the model update pipeline, including packaging, versioning, and over-the-air updates.
Collaborating with research, firmware, and product teams to set performance targets and provide feedback on hardware constraints.
Providing technical leadership, setting best practices for edge deployment, and mentoring other engineers.
Development and optimization of computer vision algorithms for drone detection, tracking, and classification.
Designing and implementing ML models for resource-constrained environments.
Integrating computer vision systems into the turret's hardware architecture.
Conducting extensive testing and validation of computer vision algorithms.
Contributing to the hardening of the prototype turret and developing variants.