Machine Learning Engineer (Model Optimization)
Confirmed on the employer's own hiring board on Oct 13, 2026. First seen by Alion on Sep 6, 2026.
Join our team as a Machine Learning Engineer specializing in Model Optimization. In this role, you will optimize and deploy machine learning models on various embedded computing platforms. You will work closely with machine learning researchers to develop hardware-aware model architectures, identify computational bottlenecks, and explore architectural changes that improve latency, throughput, and memory usage while maintaining model quality. This is an exciting opportunity to tackle real-world challenges in bringing algorithms developed in the lab to vehicles operating in diverse physical environments.
Missions
- Optimizing and deploying machine learning models on heterogeneous embedded computing platforms.
- Collaborating with machine learning researchers to develop and evaluate hardware-aware model architectures.
- Developing methodologies for hardware-aware model optimization and neural network architecture search.
Profil recherché
- The ideal candidate enjoys understanding both neural network architectures and the hardware on which they execute, and is interested in techniques such as hardware-aware neural architecture search, model scaling, quantization, mixed-precision inference, and model compression- Proficiency programming in Python
- Fluent in English and French, both written and spoken
- Strong understanding of machine learning and deep neural network architectures with hands-on experience developing machine learning models using frameworks such as PyTorch
- Familiarity with techniques such as model architecture search, model scaling, quantization, mixed-precision inference, knowledge distillation, or other model compression methods
- Ability to reason across different layers of the ML deployment stack, from model architecture and computational graphs to inference runtimes and hardware execution
- Experience analyzing the computational characteristics of neural networks and understanding how model architecture affects inference performance
- Strong communication skills and the ability to work effectively across machine learning research, embedded software, and product engineering teams
- Familiarity with professional software development practices and tools, including Git, unit testing, debugging, and profiling
- Experience with machine learning inference and deployment technologies such as ONNX, TensorRT, or similar frameworks
- Familiarity with CUDA/OpenCL
- Experience mentoring team members on software development and best practices
- Experience deploying machine learning models in embedded systems
- Proficiency with modern C++