Staff Machine Learning Engineer (Vision Models)

TL;DR

Staff Machine Learning Engineer (Vision Models): Build, train, and fine-tune scene understanding models for offline measurement of the hirify.global Driver with an accent on accuracy, generalisation, and rigorous evaluation. Focus on defining ground truth and correctness criteria across a complex driving taxonomy, turning results into automated benchmarks and evidence that validation pipelines and safety cases can use at scale.

Company

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

What you will do

• Develop offline scene understanding models by adapting on-vehicle architectures and hirify.global Foundation Models for measurement use.
• Drive accuracy and generalisation across vehicle platforms, geographies, and driving conditions by diagnosing failure modes and closing blind spots.
• Exploit offline advantages (higher compute, larger model capacity, bidirectional temporal context, multi-task/joint representation learning).
• Benchmark models, set quality bars, and use metrics and error analysis to steer the next iteration.
• Ensure benchmarked results are statistically defensible and suitable for validation pipelines and broader safety cases.
• Collaborate cross-functionally and mentor others while aligning priorities with on-vehicle modelling, evaluation, data curation, and simulation teams.

Requirements

• 5+ years in ML engineering, including training and shipping deep learning models in production.
• Hands-on experience training modern computer vision models (including transformer-based and multimodal/VLM architectures) for detection, segmentation, classification, or scene understanding on camera and/or lidar data.
• Experience adapting or fine-tuning large pretrained/foundation models and training shared representations across multiple tasks/objectives (multi-stage or joint training) with real trade-offs across data and losses.
• Proficiency in Python and ML frameworks (especially PyTorch) and comfort with large-scale training and software engineering practices.
• Staff-level technical leadership: set direction, raise the bar, and lead cross-functional work without formal line management.
• Ability to measure your own models by defining and interpreting metrics that demonstrate genuine improvement.

Culture & Benefits

• Hybrid working policy combining time in Sunnyvale offices and workshops with time working from home.
• Competitive equity package in addition to base salary.
• Inclusive interview experience with accommodations available.
• Inclusive culture focused on diversity, fairness, and respect.

Hiring process

• Interview process with an inclusive interview experience; request accommodations if needed.
• Compensation is based on skills, qualifications, and experience.

Location: Sunnyvale, California, USA

Salary: $370,040–$407,330 (plus equity)