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Neural Rendering information

What are the key skills and qualifications needed to thrive as a Neural Rendering Engineer, and why are they important?

To thrive as a Neural Rendering Engineer, you need a strong background in computer graphics, deep learning, and mathematics, generally with a degree in computer science, electrical engineering, or a related field. Experience with frameworks like PyTorch or TensorFlow, GPU programming (CUDA), and familiarity with 3D rendering engines is highly valuable. Strong problem-solving skills, creativity, and effective teamwork set exceptional candidates apart in this role. These competencies are crucial for developing innovative rendering solutions that bridge artificial intelligence and visual computing, enabling breakthroughs in graphics technology.

What are some common challenges faced by professionals working in Neural Rendering, and how can they be addressed?

Professionals in Neural Rendering often encounter challenges related to computational resource demands and the integration of novel algorithms into existing graphics pipelines. Handling large datasets and optimizing neural network architectures for real-time performance can also be complex. Collaboration with cross-functional teams—such as graphics engineers, researchers, and product managers—is essential to ensure solutions are both technically feasible and aligned with project goals. Staying updated with the latest research and leveraging open-source frameworks can help address these challenges effectively.

What is neural rendering?

Neural rendering is a cutting-edge technique in computer graphics and artificial intelligence that uses neural networks to generate, manipulate, or enhance images and videos, often producing photorealistic or novel visual content. Unlike traditional rendering methods, which rely heavily on physical modeling and computational geometry, neural rendering leverages deep learning algorithms to synthesize visual data from inputs like 3D models, images, or text descriptions. This technology is used in applications such as virtual reality, gaming, special effects, and creating digital avatars. Neural rendering can significantly reduce the computational cost and time needed for high-quality image synthesis, making it a transformative tool in visual computing industries.

What is the difference between Neural Rendering vs 3D Graphics Programmer?

AspectNeural Rendering3D Graphics Programmer
Required SkillsMachine learning, neural networks, deep learning frameworksGraphics APIs, shader programming, 3D modeling
Work EnvironmentResearch labs, AI-focused companies, tech startupsGame studios, visual effects companies, simulation firms
Industry UsageEmerging in AI-driven visualization and renderingEstablished in gaming, film, and simulation industries

Neural Rendering focuses on using neural networks and AI techniques to generate or enhance visual content, often requiring expertise in machine learning. In contrast, 3D Graphics Programmers develop traditional graphics algorithms, shaders, and models for real-time rendering. While both roles involve visual content creation, Neural Rendering is more research-oriented and AI-driven, whereas 3D Graphics Programming emphasizes technical implementation within graphics pipelines.

What are popular job titles related to Neural Rendering jobs in California? For Neural Rendering jobs in California, the most frequently searched job titles are:
What job categories do people searching Neural Rendering jobs in California look for? The top searched job categories for Neural Rendering jobs in California are:
What cities in California are hiring for Neural Rendering jobs? Cities in California with the most Neural Rendering job openings:
Neural Rendering jobs near you
Infographic showing various Neural Rendering job openings in California as of May 2026, with employment types broken down into 100% Full Time. Highlights an 50% In-person, and 50% Remote job distribution.

ML Engineer, Foundation Models

Humble Robotics

San Francisco, CA

Full-time

Posted 27 days ago

Job description

About Humble Robotics

Working at Humble Robotics means taking on the biggest change in ground transportation in decades. We’re building an autonomous, zero-emissions hauler that dramatically lowers the cost of freight with groundbreaking vision-based AI, designed for today’s global logistics network.

We’re a fast-moving, close-knit team of AV industry veterans and innovative thinkers. We don’t believe culture can be engineered – but when it falls into place, it’s a once-in-a-lifetime adventure.

Progress has never felt so present.

Position Overview

We’re looking for an ML engineer to design, train, and ship the vision-language-action (VLA) foundation model at the core of Humble’s autonomous driving stack. You’ll work across the full arc—from architecture decisions and large-scale training to closed-loop evaluation in simulation and deployment on our trucks. This is a rare chance to build a production VLA for autonomous freight from the ground up, with the freedom and responsibility that comes with a small team tackling a massive problem.

 
Key Responsibilities

  • Design and iterate on our VLA model architecture—including the VLM backbone, action decoder, and multimodal fusion pipeline

  • Build and optimize large-scale training infrastructure (distributed training, data pipelines, mixed-precision, efficient fine-tuning)

  • Develop simulation-based evaluation and closed-loop training workflows using photorealistic neural rendering

  • Curate and manage multimodal training datasets spanning real-world driving and synthetic scenarios

  • Translate state-of-the-art research (diffusion/flow-matching action heads, reasoning-augmented VLAs, world models) into production-grade systems

  • Collaborate directly with vehicle systems and controls engineers to integrate model outputs into a real-time autonomous driving stack

Minimum Qualifications

  • MS or PhD in Computer Science, Machine Learning, Robotics, or a related field—or equivalent industry experience

  • Strong proficiency in PyTorch, distributed training, and GPU-accelerated workflows

  • Solid foundation in transformer architectures, attention mechanisms, and modern generative modeling (diffusion, flow matching)

  • Eligible to work in the United States

Preferred Qualifications

  • Experience building or contributing to end-to-end autonomous driving systems

  • Track record of publications at top ML/robotics venues (NeurIPS, ICLR, ICRA, CoRL) or significant open-source contributions

  • Familiarity with sim-to-real transfer, photorealistic simulation, or neural rendering for driving scenes

  • Experience with reinforcement learning, imitation learning, or learning from demonstration in embodied settings

  • Comfort operating as an early team member—high ownership, low ego, fast iteration

Compensation

This role is eligible for base salary + benefits + equity compensation. Salary ranges are determined by role, level, and location. Within the range, individual pay is determined by additional factors, including qualifications, skills, experience, and location. 

 
Additional Information

As part of the interview process, we may use Artificial Intelligence (AI) tools to compare your qualifications and experience to the job description. A human reviews all AI output and makes a final hiring decision. Humble Robotics does not rely on the output to make any employment decisions. Some applicants may have a legal right to opt-out of the use of AI as part of our interview process. Contact legal@humblerobotics.ai to exercise this right or if you have further questions on the use of AI tools in our hiring process.

Humble Robotics is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, national origin, gender, age, religion, disability, sexual orientation, veteran status, marital status or any other characteristics protected by law. Humble Robotics will consider qualified applicants with arrest and conviction records in a manner consistent with local ordinances.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.