You will:

You will work collaboratively to improve our models and iterate on novel research directions, sometimes in just days. We're looking for talented engineers who'd enjoy applying their skills to deeply complex and novel AI problems. Here, you will:

Apply and extend the Helm proprietary algorithmic toolkit for unsupervised learning and perception problems at scale
Carefully execute development and maintenance of tools used for deep learning experiments designed to provide new functionality for customers or address relevant corner cases in the system as a whole
Work closely with software and autonomous vehicle engineers to deploy algorithms on internal and customer vehicle platforms

You have:

A sense of practical optimism: not all experiments are successful, but the ones that are more than make up for it!
Comfort operating in a fast-paced environment to deliver customer projects
Introspection, thoughtfulness, and detail-orientation
Experience working with neural networks, Tensorflow and/or PyTorch
Fluency in Python and working knowledge of C/C++ programing
A strong interest in unsupervised learning, computer vision, and/or the autonomous vehicle industry
Master’s or Ph.D. in a related field and/or 5+ years of experience in a related field

The pay range for this position is estimated to fall in the base range of approximately $150,000 and $250,000. Base compensation for this position will vary based on location, qualifications, and relevant experience. The offered base salary may be above or below this range and compensation for the position may include additional compensation in the form of equity or a bonus/commission.

We offer:

Competitive health insurance options
401K plan management
Free lunch and fully-stocked kitchen in our South Bay office
Additional perks: monthly wellness stipend, office set up allowance, company retreats, and more to come as we scale
The opportunity to work on one of the most interesting, impactful problems of the decade

Helm.ai is proud to be an equal opportunity employer building a diverse and inclusive workforce. All applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status.

Any unsolicited resumes/candidate profiles submitted through our website or to personal email accounts of employees of Helm.ai are considered the property of Helm.ai and are not subject to payment of agency fees.

About the company

Helm.ai provides AI software for autonomous driving by licensing its unsupervised learning-based training technology to other companies, including carmakers and teams in aviation, robotics, manufacturing, and retail. Its product works by using unsupervised learning to train driving AI on unlabeled data, offering a cheaper and scalable alternative to traditional data-labeling approaches. Customers license the technology and may pay upfront licensing fees plus ongoing royalties based on usage. The company differentiates itself by focusing on unsupervised learning to reduce data labeling costs and speed up development, and by targeting multiple industries beyond just cars. Helm.ai’s goal is to become a major provider of scalable, cost-efficient AI training for autonomous systems and related fields, helping speed the adoption of autonomous technology in a trillion-dollar market.

Company Size

51-200

Company Stage

Late Stage VC

Total Funding

$145M

Headquarters

Redwood City, California

Founded

2016

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Simplify's Take

What believers are saying

Honda deepened its stake October 15, 2025, backing next-generation AD/ADAS commercialization.
Helm.ai’s July 2025 joint development targets consumer vehicles appearing by 2027.
Nine open roles on Ashby in August 2026 signal active hiring despite a small footprint.

What critics are saying

Honda dominates customer concentration; one OEM setback would slash Helm.ai’s licensing leverage.
Tesla, GM, and Mobileye already ship ADAS, compressing Helm.ai’s differentiation before 2027.
A failed Honda program would strand Helm.ai without scale, revenue, or a credible exit.

What makes Helm.ai unique

Helm.ai’s unsupervised learning cuts labeling dependence, unlike 2026 data-hungry autonomy stacks.
GenSim-3 and VidGen-3 reached native Full HD across six-camera surround-view systems, May 2026.
Honda’s 2025 investment and July 2025 joint development validate Helm.ai’s production relevance.

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Benefits

Health Insurance
401(k) Retirement Plan
401(k) Company Match
Flexible Work Hours

Growth & Insights and Company News

Headcount

6 month growth

1 year growth

2 year growth

Business Wire

May 27th, 2026

Helm.ai launches Full HD generative simulation for autonomous vehicles with 5X higher pixel density than industry benchmarks

Helm.ai has launched GenSim-3 and VidGen-3, foundation models that achieve native Full HD (1920x1080) resolution across six-camera, 360-degree surround view systems. The models render 12 megapixels per timestep, delivering five times higher pixel density than current industry benchmarks for generative world models. The breakthrough addresses the autonomous vehicle industry's "Data Wall" by providing production-ready synthetic training data that matches modern camera specifications. Standard generative models typically operate at sub-HD resolutions of approximately 0.4 megapixels per camera. GenSim-3 enables scene transfer for real-world video restylisation, whilst VidGen-3 generates fully synthetic driving sequences. The company achieved Full HD resolution using several hundred GPUs, significantly fewer than competitors requiring thousands for lower-resolution outputs. Founded in 2016, Helm.ai develops AI software for advanced driver assistance systems, autonomous driving and robotics.

Business Wire

Feb 25th, 2026

Helm.ai achieves vision-only urban autonomy, scaling from Level 2+ to Level 4 with 1,000 hours of data

Helm.ai has announced a major expansion of its Helm.ai Driver software, a vision-only autonomous driving stack that scales from Level 2+ systems through Level 4 urban autonomy without requiring lidar sensors or high-definition maps. The company released a demonstration video showing the system navigating Redwood City, California, handling turns, traffic lights and dynamic interactions. The system uses Helm.ai's proprietary Factored Embodied AI architecture, which splits autonomy into separate Perception and Policy layers for improved interpretability and safety certification. Using its Deep Teaching technology and semantic simulation, the company achieved urban capability with only 1,000 hours of real-world driving data, compared to millions of miles typically required. Helm.ai recently demonstrated zero-shot deployment in Torrance, California, enabling the system to operate in new environments without prior training or manual tuning.

WTWH Media LLC

Dec 11th, 2025

Helm.ai releases new architectural framework for autonomous vehicles

Helm.ai releases new architectural framework for autonomous vehicles. Typically, in the autonomous driving industry, developers create massive black-box, end-to-end models for autonomy that require petabytes of data to learn driving physics from scratch. Helm.ai today unveiled its Factored Embodied AI architectural framework, which it says offers a different approach. With the framework, the company released a benchmark demonstration of its vision-only AI Driver steering the streets of Torrance, CA, with zero-shot success without ever having seen those specific streets before. This included handling lane keeping, lane changes, and turns at urban intersections. Helm.ai said it achieved this autonomous steering capability by training the AI using simulation and only 1,000 hours of real-world driving data. "The autonomous driving industry is hitting a point of diminishing returns. As models get better, the data required to improve them becomes exponentially rarer and more expensive to collect," said Vladislav Voroninski, CEO and Founder of Helm.ai. "We are breaking this 'Data Wall' by factoring the driving task. Instead of trying to learn physics from raw, noisy pixels, our Geometric Reasoning Engine extracts the clean 3D structure of the world first. This allows us to train the vehicle's decision-making logic in simulation with unprecedented efficiency, mimicking how a human teenager learns to drive in weeks rather than years." Helm.ai said the architecture enables automakers to deploy ADAS through L4 capabilities using their existing development fleets, bypassing the prohibitive data barrier to entry. "We are moving from the era of brute force data collection to the era of Data Efficiency," added Voroninski. "Whether on a highway in LA or a haul road in a mine, the laws of geometry remain constant. Our architecture solves this universal geometry once, allowing us to deploy autonomy everywhere." Helm.ai said its new architecture can handle roads and more. The company said its new architecture offers several key technological advancements. First, it bridges the simulator gap. Helm.ai's architecture trains in "semantic space." This is a simplified view of the world that focuses on geometry and logic rather than graphics. By simulating the structure of the road rather than just the pixels, Helm.ai can train on infinite simulated data that works immediately in the real world. Next, leveraging this geometric simulation, Helm.ai's planner achieved robust, zero-shot urban autonomous steering using only 1,000 hours of real-world fine-tuning data, offering a capital-efficient path to fully autonomous driving. Additionally, to tackle acceleration, braking, and complex interactions, Helm.ai is leveraging its world model capabilities to predict the intent of pedestrians and other vehicles. Finally, to validate the robustness of its perception layer, Helm.ai deployed its automotive software into an Open-Pit Mine. With extreme data efficiency, the system correctly identified drivable surfaces and obstacles. This, Helm.ai said, proves the architecture can adapt to any robotics environment, not just roads. Helm.ai is working with Honda on mass-producing consumer avs. Founded in 2016, Helm.ai develops AI software for L2/L3 ADAS, L4 autonomous driving, and robotics automation. In August, the company partnered with Honda Motor Co., Ltd. The companies plan to work together to develop Honda's self-driving capabilities, including its Navigate on Autopilot (NOA) platform. The partnership centers on ADAS for production consumer cars, using Helm.ai's full-stack real-time AI software and large-scale autolabeling and generative simulation foundation models for development and validation. In October, Honda made an additional investment in Helm.ai. Honda isn't the only major automaker trying to put autonomous driving capabilities into consumer vehicles. In October, General Motors Co. announced plans to bring "eyes-off" driving to market. The company will be using technology originally developed at Cruise, a now-shut-down robotaxi developer. Tesla has long been a frontrunner when it comes to personal vehicle technology. Its "full self-driving" (FSD) software first came to the streets in 2020. While the company's technology has matured since then, it still requires a human driver to pay attention to the road and be ready to take over at all times.

Surperformance

Oct 15th, 2025

Helm.AI secures funding from Honda

Helm.AI Inc. announced it received funding on October 15, 2025, with participation from returning investor Honda Motor Co., Ltd. The company issued convertible preferred stock as part of the transaction.

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