Machine Learning Engineer - Semantic Reasoning (Highway) (Autonomous Driving)

TL;DR

Machine Learning Engineer - Semantic Reasoning (Highway) (Autonomous Driving): Developing high-performance reasoning engines and spatial representations for autonomous vehicles on high-speed roads with an accent on multi-task transformers and Vision Language Action (VLA) models. Focus on optimizing deep learning models for real-time inference and resolving perception edge cases in urban and highway environments.

Location: Hybrid (Foster City, CA / Boston, MA)

Company

hirify.global is an autonomous vehicle company building robots that enable human-level decision-making for safe navigation in complex driving environments.

What you will do

• Design, train, and deploy deep learning models for semantic reasoning tailored for high-speed highway environments.
• Collaborate with Scene Intelligence, Semantic Grounding, and PCP Mapping teams to evolve the unified ML stack.
• Partner with motion planning teams to define semantic representation requirements and establish validation workflows.
• Optimize deep learning models for low-latency real-time inference within rigorous vehicle compute constraints.
• Investigate and resolve perception-related regressions and edge cases using simulation and live fleet data.
• Contribute to the long-term strategic architecture for Perception Semantic Reasoning for scalable fleet deployment.

Requirements

• MS (3–5 years) or PhD (0–2 years) in Computer Science, Robotics, Electrical Engineering, or a related field.
• Professional software engineering experience, ideally in autonomous driving, robotics, or computer vision.
• Deep understanding of 2D/3D computer vision, semantic segmentation, and deep learning architectures.
• Exceptional programming skills in modern C++ and Python.
• Hands-on experience with modern deep learning frameworks like JAX or PyTorch.
• Proven track record of deploying real-time ML models on resource-constrained embedded systems or on-bot hardware.

Nice to have

• Experience with highway autonomous driving scenarios and their specific mapping/perception challenges.
• Familiarity with BEV, Sparse Transformer architectures, and Vision-Language Models (VLMs).
• Strong publication record in top AI conferences or journals (e.g., CVPR, ICCV, ECCV, ICML, NeurIPS).

Culture & Benefits

• Opportunity to work on cutting-edge AI and robotics at the intersection of hardware and software.
• Commitment to building a diverse team with a variety of backgrounds and perspectives.
• Collaborative environment working across perception and motion planning teams.