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On Call Gaussian Splatting Jobs (NOW HIRING)

... SLAM (including Gaussian splatting and implicit representations where they earn their keep ... and on-call. • Excellent systems and architectural judgment; able to defend a position and ...

... SLAM (including Gaussian splatting and implicit representations where they earn their keep ... and on-call. • Excellent systems and architectural judgment; able to defend a position and ...

On Call Gaussian Splatting information

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How much do on call gaussian splatting jobs pay per hour?

As of Jul 13, 2026, the average hourly pay for on call gaussian splatting in the United States is $17.91, according to ZipRecruiter salary data. Most workers in this role earn between $15.38 and $19.23 per hour, depending on experience, location, and employer.
What are the most commonly searched types of Gaussian Splatting jobs? The most popular types of Gaussian Splatting jobs are:
Staff Autonomy Engineer

Staff Autonomy Engineer

Brain Corp

San Diego, CA • On-site

Full-time

Re-posted 8 days ago


Job description

Job Summary:
Brain Corp is a San Diego-based AI company creating transformative core technology for the robotics industry. As a Staff Autonomy Engineer, you will lead the technical direction for AI systems that power robots, manage large projects, and mentor other engineers while driving innovation in autonomy technology.
Responsibilities:
• Set technical direction for major areas of the autonomy stack — perception, SLAM, prediction, planning, or the ML systems underneath them — and own the multi-quarter roadmaps that follow.
• Lead large, ambiguous projects end-to-end: scope the problem, pick the approach, drive execution across multiple engineers, and ship to the fleet.
• Make and defend the build-vs-borrow, learn-vs-classical, and depth-vs-breadth decisions that shape what the team works on for the next year.
• Partner with engineering managers, product, and hardware on staffing, sequencing, and risk; serve as a senior technical voice in cross-functional planning.
• Represent the team externally where useful — publications, conferences, recruiting, customer technical conversations.
• Architect and contribute to systems spanning learned perception (transformer-based detectors, VLMs for scene understanding), neural and classical SLAM (including Gaussian splatting and implicit representations where they earn their keep), behavior prediction, and motion planning.
• Drive adoption of modern learning approaches — imitation learning, diffusion policies, vision-language-action models, RL where appropriate — and integrate them with the classical components that still do real work in production.
• Build and improve the data engines and evaluation infrastructure that turn fleet logs into training data, regression suites, and shipped wins.
• Lead performance and deployment work on embedded compute: model optimization, quantization, distillation, and runtime engineering on GPU/accelerator hardware.
• Stay current with the literature and translate the parts that matter into our codebase — and recognize the larger fraction that doesn't.
• Mentor Senior and mid-level engineers; raise the bar through design review, code review, and the technical standards you model.
• Play a leading role in hiring: shape interview loops, calibrate decisions, close strong candidates, and own onboarding for new hires on the team.
• Champion engineering practices that compound — testing, observability, documentation, simulation infrastructure, developer tooling — and invest in the parts of the org that enable everyone else to ship faster.
Qualifications:
Required:
• Master’s Degree. or Ph.D. in Computer Science, Robotics, Electrical Engineering, or a related field — or equivalent demonstrated experience.
• 7+ years of industry experience in embodied AI (robotics, self-driving, drones, or similar), with at least some of that time in a senior or lead capacity.
• Deep expertise in two or more of: machine learning, SLAM and state estimation, robotic perception, motion planning.
• Strong fluency in C++ and Python in a Linux environment.
• Demonstrated history of taking research ideas into production at meaningful scale.
• Deep, hands-on expertise with PyTorch (and/or JAX), modern training infrastructure, and contemporary architectures (transformers, diffusion models, VLA/foundation models).
• Extensive experience designing robotic systems with ROS 2 (or comparable middleware) and modern simulation environments such as Isaac Sim, MuJoCo, or Gazebo.
• Strong record of shipping ML-driven features to production hardware — including the post-launch reality of monitoring, debugging weird failure modes, rollback, and on-call.
• Excellent systems and architectural judgment; able to defend a position and equally able to update it when the evidence changes.
• Modern software engineering fundamentals: CI/CD, code review culture, observability, and iterative delivery.
• Excellent written and verbal communication — design docs that set direction, talks that bring people along, and code that other engineers want to read.
Preferred:
• Bonus: experience with on-device acceleration (TensorRT, ONNX, custom CUDA kernels), large-scale data infra, or leading distributed teams.
• Bonus: Track record of research impact (publications at venues like CoRL, RSS, ICRA, NeurIPS, CVPR, ICML — or open-source equivalents) *and* of converting research into product.
Company:
Brain Corp develops core technology for the robotics industry. Founded in 2009, the company is headquartered in San Diego, USA, with a team of 201-500 employees. The company is currently Growth Stage.