Staff Machine Learning Engineer (Computer Vision)

Pravāh • San Francisco, CA

21h

About The Position

Pravah is building foundational intelligence for the electric grid by applying modern machine learning to complex physical infrastructure problems spanning grid operations, weather, and geospatial systems. Their work integrates computer vision, physical systems, and large-scale ML, with deployments across utilities in the United States and India. They leverage multimodal data, including satellite imagery, LiDAR, and street-level data, to construct high-fidelity representations of grid assets and their surroundings. Pravah is backed by Khosla Ventures, Pear VC, and Conviction. The company is hiring a Staff Machine Learning Engineer (Computer Vision) to lead the development of core perception and mapping systems for electric grid infrastructure. This role demands high ownership and involves navigating ambiguity, focusing on building systems that operate on large-scale, heterogeneous visual data. The successful candidate will define technical direction, make key architectural decisions, and deploy models into production. Additionally, the role involves exploring state-of-the-art generative and vision architectures (e.g., ViTs, diffusion, flow matching) for adjacent domains like weather and spatiotemporal modeling, with opportunities to contribute to frontier work suitable for publication.

Requirements

• 6+ years of experience building and deploying machine learning systems, with a focus on computer vision
• Strong expertise in modern deep learning approaches, including: Object detection and segmentation, Vision transformers and/or generative modeling approaches, Multimodal learning
• Proven track record of taking systems from research to production at scale
• Strong engineering fundamentals and proficiency in Python and ML frameworks (e.g., PyTorch)
• Comfortable operating in ambiguous environments, reasoning from first principles, and driving technical direction with high ownership
• Demonstrated ability to produce high-quality technical work, whether through systems, research, or publications

Nice To Haves

• Experience working with large-scale, real-world datasets (e.g., geospatial, satellite, LiDAR)

Responsibilities

• Build and deploy models for object detection, segmentation, and instance-level understanding of grid assets and surroundings
• Work across satellite, aerial, street view, and LiDAR data to create unified representations of physical infrastructure
• Develop systems for depth estimation and spatial reasoning in complex real-world environments
• Adapt and fine-tune vision transformers and related architectures for domain-specific tasks
• Build multimodal systems that combine visual, spatial, and structured data
• Design representations that generalize across geographies, data sources, and operating conditions
• Bring state-of-the-art CV methods into production, bridging research and real-world deployment
• Explore the use of modern generative and vision architectures in weather and geospatial modeling applications
• Identify, prototype, and validate new approaches for modeling physical systems from visual data

Benefits

• Ownership of core perception and mapping systems deployed in real-world grid operations
• Opportunity to work on hard, open-ended problems at the intersection of AI and physical infrastructure
• Ability to shape technical direction and contribute to frontier ML work
• Close collaboration with a deeply technical founding team

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed