Research Engineer
- Role
- AI / ML
- Experience
- Senior
- Employment
- Full-time
Open to CA only. Set where you work from to check your eligibility.
No BS summary
Research Engineer to work on novel AI problems, applying and extending the Helm proprietary algorithmic toolkit for unsupervised learning and perception at scale. Requires strong Python, working knowledge of C/C++, and neural networks experience with TensorFlow and/or PyTorch. Master's or Ph.D. preferred.
Core skills
Required skills
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. 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.
What you'll do
- 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
- Work collaboratively to improve our models and iterate on novel research directions
What they require
- 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
Benefits
- Competitive health insurance options
- 401K plan management
- Free lunch and fully-stocked kitchen in our South Bay office
- Monthly wellness stipend
- Office set up allowance
- Company retreats
Helm.ai builds AI software for autonomous driving and robotics. Its Deep Teaching methodology is data and capital efficient, using unsupervised learning software to train neural networks without human annotation or simulation, and it works with large automotive manufacturers.