3D Computer Vision Researcher (World Modeling for Autonomous Driving)

Tera AI; San Francisco (CA); On-site; USD 120,000 - 160,000; Full time

Job summary

A stealth VC-backed startup is seeking candidates with a broad technical foundation in software engineering and deep learning. Applicants should have experience in deep learning research, defined product management roles, and strong programming skills in Python. The role requires innovation in spatial AI with a focus on high-performance systems and collaboration in developing next-gen AI solutions. Ideal candidates possess PhDs or relevant experience and are eager to challenge themselves in a high-stakes environment.

Qualifications

• Broad technical foundation in software engineering and deep learning.
• Publications at top ML venues or experience training multimodal foundation models.
• Experience with defining, building, and launching new products.

Skills

Deep learning research

Product management

Programming in Python

Machine Learning

Data pipeline experience

Backend systems design

Cloud infrastructure knowledge

Monitoring tools experience

Version control systems

Domain-specific knowledge

Education

Ph.D. in relevant field

Tools

PyTorch

OpenCV

TensorFlow

Docker

Kubernetes

Job description

We are a well capitalized stealth VC-backed startup building a new type of spatial AI capable of universally solving autonomy. We innovate at the foundational layer of AI by training our own AI models.

Our team

Our team is composed of AI pioneers and leaders from Google X, Google Brain, and Space Agencies. Several of us are repeat founders, with deep commercialization insights across multiple enterprise segments. We enjoy long, deep ideations around entirely unexplored AI use cases in autonomy (cars, drones, robots).

Who you are

• Curious: you have an innate curiosity for all things intellectual, and it’s something you can’t turn off. You obsess over finer details others miss.
• High intensity: you thrive in a high-stakes environment, and are driven by an innate obsession, not by others.
• Fast learner: you gravitate toward learning new things, and often find yourself learning more quickly than everyone around you.
• Driven by discomfort: you enjoy leaving your comfort zone and challenging yourself
• Creative: track record of solving hard problems with solutions worthy of academic papers
• Educator: you take pride in your ability to communicate complex topics clearly and have excellent speaking and writing skills.
• Zero ego: you don’t just take feedback, but truly see it as a gift. You don’t wait until feedback is given, but solicit it with every opportunity.
• Founder mentality: you roll up your sleeves to help solve the most pressing problem on a given day, even if it has nothing to do with this job post.

Where you are

Technical skills

Broad technical foundation in software engineering and deep learning (through self-study, practical experience, or PhD)

• Deep learning research: publications at top ML venues, or practical experience training multimodal foundation models
• Trained LLMs, LVMs, or any foundation model
• Product: experience with defining, building, and launching new products
• Programming: deep knowledge of Python
• ML: knows the theory and applied side of deep learning, especially computer vision and foundational model architectures (PyTorch, OpenCV, Tensorflow)
• Robotics: experience here is a plus, but not required.
• Data: experience with distributed data pipelines (Beam/Spark/WebDataset)
• Backend: experience designing and building scalable and high availability server-side systems (Python, NodeJS, or Golang)
• Cloud: familiarity with DevOps and infrastructure (i.e. Docker, Kubernetes, GPU scheduling) on cloud (i.e. GCP, AWS)
• Monitoring: experience with logging systems and monitoring tools such as DataDog, Sentry, and/or CloudWatch
• Security: familiarity with enforcing security standards as well as other data regulation compliances (i.e. GDPR, SOC2)
• Version Control: knows the ins and outs of Git and has extensive experience collaborating with teams on systems like GitHub, GitLab, etc.
• Domain-specific: spatial data and analytics, geographic information systems, spatial databases (PostGIS)