Sr. Embedded Machine Learning Engineer

Senior Embedded Machine Learning Engineer for Autonomous Anti-Drone Systems*

Company Overview:*

Allen Control Systems (ACS) is a cutting-edge defense startup founded by two former Navy electrical engineers with a proven track record in robotics and software. We are developing an autonomous gun turret using advanced computer vision and control systems to precisely detect, track, and neutralize enemy drones.

With an engineering-first culture, ACS values technical excellence and innovation. Backed by our founders' successful exits from two previous venture acquired for a combined $180M in 2022, we are committed to ensuring that the groundbreaking technologies we develop will have a real-world impact.

Position Summary:*

We are hiring a Senior Embedded Machine Learning Engineer to own the end-to-end process of taking trained machine learning models including any code supporting them and deploying them efficiently onto resource-constrained edge hardware. This person sits at the intersection of machine learning, embedded systems, and hardware engineering.

The role has two tightly linked primary responsibilities: integrating, converting, and optimizing models so they run within strict constraints on latency, memory, power, and thermal budget; and building and integrating the supporting C++ code that runs the models on device and performs any necessary pre or post processing. The role is highly cross-functional. You will partner with CVML who build the models, with embedded and firmware teams who own the device, and with product teams who define performance targets. Success means models that are not just accurate in the lab but fast, small, and dependable in the field.

What You'll Do:*

• Model optimization. Apply quantization, pruning, knowledge distillation, operator fusion, and graph optimization to shrink models and reduce inference cost while protecting accuracy.• Model conversion and deployment. Convert trained models into formats suitable for edge runtimes using ONNX and TensorRT and deploy them to target hardware.• Hardware bring-up and benchmarking. Profile inference on accelerators such as GPUs, NPUs, DSPs, TPUs, or FPGAs. Measure latency, throughput, memory footprint, and power, then drive the changes needed to hit targets.• C++ application integration. Design, write, and maintain the supporting C++ code that hosts inference on device. This includes the application and library code that loads and runs models, the pre- and post-processing pipelines, data and memory management, threading, and the interfaces to the rest of the embedded system. Ensure the combined model and C++ stack meets real-time constraints, fits within the device memory budget, and behaves reliably on the target platform, using Python where appropriate for tooling and validation.• Accuracy and quality validation. Build test harnesses that verify on-device accuracy against reference results and catch regressions introduced by optimization or quantization.• Model update pipeline. Contribute to the tooling and processes for packaging, versioning, and delivering model updates to deployed devices, including over-the-air update paths where applicable.• Cross-functional collaboration. Work closely with research, firmware, and product teams to set realistic performance targets early and to feed hardware constraints back into model design.• Technical leadership. Set best practices for edge deployment, review designs and code, and mentor other engineers on optimization and embedded ML techniques.