Join Skild AI as a Research Robotics/Computer Vision Engineer to develop intelligent robotic systems for real-world navigation.
Posted by employer 3 months ago
First seen on Joblaze 1 week ago
Last verified on the company career page 1 day ago
Skills & Technologies
Requirements
- Experience
- 1+ years
- Education
- Master's degree
Not disclosed in this posting: visa sponsorship.
Joblaze summary
In this role, the Research Robotics/Computer Vision Engineer at Skild AI focuses on developing advanced robotic systems that leverage 3D computer vision and autonomous navigation. Key skills include expertise in SLAM systems, scene reconstruction, and machine learning techniques, particularly in optimizing perception models. This position is ideal for candidates with a master's degree in a relevant field and at least one year of experience, who are eager to tackle complex challenges in a dynamic, innovative environment.
Joblaze insights
- Listed 9 days ago on Joblaze — tracked directly from Skild AI's career page.
- Skild AI has 63 other open roles including Manufacturing Engineering Manager, Senior Electrical Engineer , Operations Program Manager – Data Infrastructure - India.
- 2727 active AWS roles on Joblaze right now.
- 798 active Machine Learning roles on Joblaze right now.
- 480 active PyTorch roles on Joblaze right now.
- Salary band is above the typical range for AI/ML roles (median ~$188,000).
- Pays more than 94% of the 1354 salaried AWS roles we track (median $170,000). See AWS salary trends
Quick facts
- Is the Research Robotics/Computer Vision Engineer role remote?
- No — this is an on-site role in San Mateo.
- What's the salary range?
- Skild AI lists $250,000–$300,000 for this role.
- How much experience is required?
- At least 1 year of relevant experience for this Research Robotics/Computer Vision Engineer role.
- Where is the role based?
- Skild AI is hiring for this position in San Mateo.
- What's the tech stack?
- Joblaze extracted these technologies from the posting: 3D SLAM, AWS, Computer Vision, Data Science, Generative Computer Vision, Google Cloud.
- What seniority level is this role?
- Skild AI targets mid-level candidates for this position.
- Is this full-time or contract?
- Full-time for this Research Robotics/Computer Vision Engineer role at Skild AI.
From the original posting
Company Overview
At Skild AI, we are building the world's first general purpose robotic intelligence that is robust and adapts to unseen scenarios without failing. We believe massive scale through data-driven machine learning is the key to unlocking these capabilities for the widespread deployment of robots within society. Our team consists of individuals with varying levels of experience and backgrounds, from new graduates to domain experts. Relevant industry experience is important, but ultimately less so than your demonstrated abilities and attitude. We are looking for passionate individuals who are eager to explore uncharted waters and contribute to our innovative projects.
Position Overview
Skild AI, Inc. seeks a Research Robotics/Computer Vision Engineer in San Mateo, CA responsible for developing perceptive, intelligent, and adaptable robotic systems capable of learning and performing tasks with a focus on 3D computer vision and autonomous navigation. This includes designing perception pipelines, optimizing SLAM systems, and creating learning-based algorithms for robust robotic control in real-world environments. Specific duties include: (i) implementing perception on robots to enable safe exploration and navigation in real world environments in collaboration with the locomotion team; (ii) reconstructing an entire scene in 3D using monocular images, estimating camera poses, optimizing and streamlining 3D SLAM; (iii) developing a set of software tools for localization of a robot using only visual inputs; (iv) building robust software to enable life-long mapping on a robot via optimally merged pose-graphs; (v) visual servoing wrt objects detected/ tracked to control robot motion; (vi) researching novel techniques to detect and cater to glare during robotic mapping and navigation; (vii) building infrastructure and pipeline and collecting data to enable streaming of hand movements for training robot manipulation tasks such as pick and place; and (viii) maintaining a camera and 2D lidar based navigation stack, including fixing bugs, adding new customer feature requests, and ensuring successful deployments.
Responsibilities
- (i) implementing perception on robots to enable safe exploration and navigation in real world environments in collaboration with the locomotion team
- (ii) reconstructing an entire scene in 3D using monocular images, estimating camera poses, optimizing and streamlining 3D SLAM
- (iii) developing a set of software tools for localization of a robot using only visual inputs
- (iv) building robust software to enable life-long mapping on a robot via optimally merged pose-graphs
- (v) visual servoing wrt objects detected/ tracked to control robot motion
- (vi) researching novel techniques to detect and cater to glare during robotic mapping and navigation
- (vii) building infrastructure and pipeline and collecting data to enable streaming of hand movements for training robot manipulation tasks such as pick and place
- (viii) maintaining a camera and 2D lidar based navigation stack, including fixing bugs, adding new customer feature requests, and ensuring successful deployments.
Minimum Requirements
- Must have a master’s degree (or foreign equivalent) in Computer Vision, Robotics, or a directly related discipline and one (1) year of experience in Machine Learning or Data Science.
- Must have any experience with or knowledge of each of the following: (i) reconstructing 3D scenes using monocular videos, meshes, pointclouds, Neural Radiance Fields, and Gaussian Splats; (ii) reconstructing rigid and articulated hand-held objects from videos, including inferring the time-varying hand configurations and relative poses of the objects; (iii) using generative computer vision, including diffusion models to guide reconstruction, or addressing occlusion and limited viewpoint variations in videos via data driven priors; (iv) optimizing attention-based models for perception used in autonomous navigation systems; (v) using Neural Architectural Search (NAS) to find better perception backbone architectures with higher accuracies and lower latencies; and (vi) cloud-based training in AWS, Google cloud, or Vetex AI and optimized data loading for cloud based distributed training for deep learning workloads (e.g. Pytorch dataloader, or sharding) with hardware-in-loop.
- Experience can be concurrent.
Apply online at skild.ai/career.