Senior Principal Software Engineer

Role Description

We’re looking for an exceptional Senior Principal AI Scientist in Generative AI who is ready to drive the future of mobility with us!

What You Will Work On:

• Optimize and deploy high-performance LLM inference pipelines
• Own inference runtimes across data center, edge, and embedded platforms
• Push model performance through quantization, kernel fusion, and cache optimization
• Drive latency and throughput improvements that directly impact production products
• Enable efficient, reliable deployment without external vendor dependency

Core Responsibilities

• Inference Engines & Runtime:
  • Build deep expertise and ownership of: vLLM, TensorRT-LLM, llama.cpp, QAIRT
  • Extend and tune inference engines using custom CUDA kernels
  • Adapt runtimes for constrained and embedded deployment environments
• Quantization & Numerical Optimization:
  • Implement and evaluate quantization strategies: INT8, INT4, FP4, FP8, mixed precision, AWQ, GPTQ
  • Balance accuracy, latency, memory footprint, and throughput
• KV Cache Optimization:
  • Optimize key–value cache performance through paging, prefix caching, cache-aware memory layout design
  • Reduce memory pressure while sustaining high throughput
• Latency & Throughput Optimization:
  • Design and tune batching strategies, continuous batching, speculative decoding
  • Optimize tail latency and tokens/sec under real production traffic patterns

What Success Looks Like

• Models deploy efficiently on edge and embedded devices, not just servers
• Tokens/sec significantly outperform baseline implementations
• End-to-end latency is minimized and predictable
• Inference cost per request is materially reduced
• The company is no longer dependent on partners for inference optimization

Qualifications

• Proven experience optimizing ML inference performance in production
• Deep understanding of GPU architecture and memory hierarchies
• Hands-on experience with CUDA and low-level performance tuning
• Experience deploying models beyond research environments

Requirements

• Inference engines: vLLM, TensorRT-LLM, llama.cpp, QAIRT
• CUDA kernel development and profiling
• Quantization techniques: INT8/INT4/FP4/FP8, AWQ, GPTQ
• KV cache optimization and memory layout design
• Latency optimization: batching, speculative decoding, continuous batching

Benefits

• Salary range: $141,400 USD - $226,300 USD
• Annual bonus opportunity
• Insurance coverage (medical, dental, vision, life, and disability)
• Paid time off
• Paid holidays
• Company contribution to the RRSP (Registered Retirement Savings Plan)
• Equity awards for certain positions and levels
• Remote and/or hybrid work available depending on the position