Ml Engineer (Ai)

TL;DR

Ml Engineer (AI): Developing and deploying machine learning models that power camera-based perception for autonomous trucks with an accent on building robust and scalable camera-based models. Focus on developing high-performance vision models and the infrastructure needed to train, evaluate, and deploy them at scale.

Location: Remote (US, Canada)

Company

hirify.global is developing machine learning models that power camera-based perception for autonomous trucks.

What you will do

• Develop and train deep learning models for camera-based perception.
• Implement production-quality machine learning code to support model training, evaluation, and inference for camera perception systems.
• Analyze model performance across diverse driving scenarios, identify failure modes, and improve robustness and generalization.
• Contribute to the development and optimization of large-scale training pipelines.
• Collaborate with cross-functional teams across perception, simulation, and validation to evaluate model performance and support integration into the autonomy stack.
• Improve experimentation workflows and tooling to accelerate model iteration, reproducibility, and evaluation.

Requirements

• Bachelor’s degree in Computer Science, Robotics, Electrical Engineering, Machine Learning, or a related technical field with 4+ years of industry experience, or a Master’s degree with 2+ years of experience.
• Experience developing machine learning or deep learning models for computer vision or perception systems.
• Strong programming skills in Python and PyTorch, with experience writing production-quality ML code.
• Experience training and evaluating machine learning models using large datasets and scalable compute environments.
• Understanding of modern deep learning architectures used in perception systems, such as CNNs, transformers, or multi-task learning models.
• Ability to collaborate with cross-functional teams to integrate ML models into larger software systems.

Nice to have

• Experience working in autonomous driving, robotics, or simulation-based training environments.
• Experience with multi-task learning or perception architectures that combine multiple visual tasks.
• Experience working with large-scale data pipelines, distributed training systems (e.g., Ray), or experiment management frameworks.
• Familiarity with camera calibration, geometric reasoning, or 3D perception from images.
• Experience deploying ML models into production or real-world robotics systems.