Machine Learning Engineer - Model Optimization

Stack

Responsibilities

• ▹Profile and analyze machine learning models to identify computational, memory and data-movement bottlenecks
• ▹Explore trade-offs between model output quality and computational cost, including latency, throughput and memory footprint
• ▹Develop methodologies for hardware-aware model optimization and neural network architecture search, using real hardware measurements as optimization objectives (targets include CPUs, GPUs and dedicated AI accelerators)
• ▹Apply model optimization techniques such as quantization, mixed-precision inference, distillation and other model compression techniques
• ▹Analyze the numerical differences introduced by these optimization techniques
• ▹Develop and maintain model export, benchmarking and deployment pipelines across frameworks and inference runtimes such as PyTorch, ONNX and TensorRT
• ▹Evaluate different deployment strategies and determine how models should be mapped onto heterogeneous processing units such as CPUs, GPUs and dedicated AI accelerators
• ▹Work closely with machine learning researchers to develop and evaluate hardware-aware model architectures

Requirements

• ▹Strong understanding of machine learning and deep neural network architectures with hands-on experience developing models using frameworks such as PyTorch
• ▹Proficiency programming in Python
• ▹Experience analyzing the computational characteristics of neural networks and understanding how model architecture affects inference performance
• ▹Familiarity with techniques such as model architecture search, model scaling, quantization, mixed-precision inference, knowledge distillation or other model compression methods
• ▹Experience with machine learning inference and deployment technologies such as ONNX, TensorRT or similar frameworks
• ▹Ability to reason across the layers of the ML deployment stack, from model architecture and computational graphs to inference runtimes and hardware execution
• ▹Familiarity with professional software development practices and tools, including Git, unit testing, debugging and profiling
• ▹Strong communication skills and the ability to work effectively across machine learning research, embedded software and product engineering teams
• ▹Fluent in English and French, both written and spoken
• ▹Resume submitted in English

Nice to have

• ▹Proficiency with modern C++
• ▹Familiarity with CUDA/OpenCL
• ▹Experience deploying machine learning models in embedded systems
• ▹Experience mentoring team members on software development and best practices

Soft skills

What we offer

• ▹Opportunity to make an impact at a young, venture-backed company in an emerging market
• ▹Competitive salary ranging from 75,000 to 90,000 euros annually depending on experience, plus equity
• ▹Hybrid work model: in office 3 days per week (Monday, Tuesday, Thursday), the rest work from wherever
• ▹Fully equipped, modern office in the heart of Paris
• ▹Commuter benefits (partial reimbursement for public transport, where applicable)
• ▹Subsidized meal vouchers (tickets restaurant)
• ▹Wellness Pass (ex Gymlib)

About the company

Zendar builds a radar-centric autonomy stack that makes any vehicle, from cars to robots, autonomous in any environment. The whole stack is built in house, from radar sensor hardware through signal processing and multi-modal perception models to path and trajectory planning. The hiring process is intentionally human: every resume is reviewed by a real person.