Helm.ai
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About
Helm.ai develops AI-first software and simulation solutions for ADAS to Level 4 autonomous driving. We provide comprehensive AI solutions for real-time deployment and offline training and validation, built on our proprietary Deep Teaching™ and generative AI models. Our offerings include production-ready perception software (Helm.ai Vision) for vision-first L3 autonomy without lidar, and real-time neural networks for path planning (Helm.ai Driver). For offline foundation models, we offer GenSim-2 and VidGen-2 for modifying and producing realistic driving data and synthetic videos, and WorldGen-1 for multi-sensor data simulation across the full AV stack. Our Deep Teaching™ method enables adaptable, high-performance foundation models at scale, automatically adapting to new geographies and driving conditions. We partner with global automakers and Tier 1 suppliers on mass market production programs, aiming to launch the world's safest and most advanced L2-L4 autonomous driving systems.
Platform
Features
• multi-sensor generative ai simulation (worldgen-1)
• vision-only path planning neural network (helm.ai driver)
• vision-first perception software (helm.ai vision)
• generative ai for data modification and synthetic videos
• deep teaching™ (proprietary unsupervised learning)
• offline training and validation ai models
• real-time deployment ai models
• ai-first software for adas to l4 autonomous driving
Job Opportunities
Machine Learning Engineer
Helm.ai is an AI-first software and simulation provider for ADAS to Level 4 autonomous driving, offering comprehensive AI solutions for real-time deployment and offline training.
Benefits:
Competitive health insurance options
401K plan management
monthly wellness stipend
office set up allowance
company retreats
Experience Requirements:
Proficiency in Python
Proven ability to thrive in fast-paced environment
Other Requirements:
A sense of practical optimism
Ability to communicate complex technical concepts to colleagues and a variety of audience
Introspection, thoughtfulness, and detail-orientation
Responsibilities:
Characterize neural network quality, failure modes, and edge cases based on research data
Maintain awareness of current trends in relevant areas of research and technology
Coordinate with researchers and accurately convey status of experiments
Manage a large number of concurrent experiments and make accurate time estimates with respect to deadlines
Review experimental results and suggest theoretical or process improvements for future iterations
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