Senior Machine Learning Engineer, AI Performance (Autonomous Driving)

TL;DR

Senior Machine Learning Engineer, AI Performance (Autonomous Driving): Delivering production-ready PyTorch models for automated driving systems with an accent on model performance, deployment readiness, and runtime efficiency. Focus on optimizing models for on-vehicle execution, solving latency and memory constraints, and designing reliable training-to-deployment release workflows.

Location: London, United Kingdom

Company

hirify.global develops embodied AI software and foundation models that enable vehicles to perceive, understand, and navigate complex environments for automated driving.

What you will do

• Own end-to-end delivery of model releases from requirements and training through evaluation, iteration, and deployment readiness.
• Train and iterate on deep learning models in PyTorch using hypothesis-driven experimentation, ablations, and clear evaluation criteria.
• Analyze regressions and performance issues, identify root causes, and implement improvements.
• Apply model optimization techniques such as quantization and distillation while evaluating practical trade-offs.
• Collaborate with ML and performance engineering teams to define bottlenecks, hand off models, and prioritize optimization work.
• Align stakeholders on delivery timelines, trade-offs, and release readiness criteria.

Requirements

• Experience improving production systems under tight latency, memory, bandwidth, power, thermal, or cost constraints.
• Strong hands-on experience training and iterating on deep learning models in PyTorch.
• Proficiency with at least one relevant toolchain, such as TensorRT, CUDA, Qualcomm QNN, Triton, or OpenCL.
• Ability to reason from high-level model behavior to low-level kernel and runtime execution.
• Understanding of model optimization concepts, including quantization and distillation.
• Strong engineering, analytical, communication, and collaboration skills.

Nice to have

• Experience with edge, embedded, real-time, or otherwise efficiency-constrained ML systems.
• Exposure to ML workflows spanning training, evaluation, and deployment handoff.
• Experience deploying and benchmarking ML models on embedded or edge devices.

Culture & Benefits

• Fast-paced environment focused on solving complex problems in embodied AI and automated driving.
• Inclusive, diverse, and respectful workplace that values different perspectives.
• Collaborative work across ML, performance engineering, and product delivery teams.
• Reasonable adjustments and accommodations are available throughout the interview process.

Hiring process

• Inclusive interview process with accommodations available upon request.
• Candidate experience includes evaluation of engineering, machine learning, and collaboration capabilities.