Senior Machine Learning Engineer, Vision Models

Location: Sunnyvale, California, USA; full-time hybrid role based in the Sunnyvale office

Salary: $311,850–$370,000 per year, plus a competitive equity package. Actual compensation depends on skills, qualifications, and experience.

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

• Build, train, and fine-tune computer vision and scene-understanding models for offline measurement of automated-driving performance.
• Adapt on-vehicle architectures and foundation models for offline use with larger compute budgets and bidirectional temporal context.
• Improve model accuracy and generalisation across vehicle platforms, geographies, and driving conditions.
• Benchmark models, define quality bars, analyse errors, and diagnose failure modes and blind spots.
• Develop statistically defensible evidence for large-scale validation pipelines and safety cases.
• Collaborate with modelling, evaluation, data curation, and simulation teams while mentoring less experienced engineers.

Requirements

• 4+ years of ML engineering experience, including training and shipping deep learning models in production.
• Hands-on experience with modern computer vision models, including transformer-based and multimodal or VLM architectures for detection, segmentation, classification, or scene understanding.
• Experience training models on camera and/or lidar sensor data and adapting or fine-tuning pretrained or foundation models.
• Experience with multi-task or joint training, shared representations, and trade-offs across data and loss functions.
• Proficiency in Python and ML frameworks, especially PyTorch, with strong software engineering practices and experience with large-scale training.
• Ability to define and interpret model metrics, lead modelling work independently, collaborate across teams, and mentor engineers.

Nice to have

• Experience in 3D scene understanding, geometric and semantic perception, or large-scale semantic enrichment of driving scenes.
• Experience with offboard or offline modelling, auto-labelling, model distillation, temporal models, or world models.
• Autonomous vehicle or robotics experience involving physical-system deployment and closed-loop validation.
• Experience with fleet-scale data and distributed training infrastructure.

Culture & Benefits

• Hybrid work combines time in offices and workshops with time working from home.
• Work with fleet-scale camera, lidar, and simulation data on mission-critical automated-driving systems.
• Collaborate in a diverse, inclusive, and respectful environment that values different perspectives.
• Competitive equity package included with compensation.
• Interview accommodations are available upon request.