AI Control Theory & Optimization Scientist
| Company | Passive Logic |
|---|---|
| Location | Murray, UT, USA |
| Salary | $Not Provided – $Not Provided |
| Type | Full-Time |
| Degrees | Master’s, PhD |
| Experience Level | Senior, Expert or higher |
Requirements
- MS or PhD in Control Engineering, Computer Science, Mathematics, or related fields.
- Demonstrated expertise in AI development, scientific machine learning, reinforcement learning, multiagent systems, and optimization.
- Strong background in model predictive control and programming skills (e.g., Swift, C++, Python).
- Exceptional Communication Skills: Excellent interpersonal skills and a team-oriented mindset.
- Organized and Strategic: Strong analytical and problem-solving skills, particularly in mathematics and numerical methods.
- Collaborative Mindset: Open to feedback and committed to a continuous improvement process.
- Adaptability: Comfortable in a fast-paced startup environment, eager to learn, iterate, and innovate.
- Problem solving: You own this role. When issues arise, be the empowered force that solves them, rolling-up.
Responsibilities
- Develop predictive models leveraging deep learning, reinforcement learning, and transfer learning techniques.
- Develop autonomous agents for generative training of deep learning predicates.
- Develop algorithms such as stochastic gradient descent, coordinate descent, distributed optimization, Bayesian methods, and evolutionary algorithms.
- Utilize big data computation and storage models to create prototypes and data sets.
- Conduct model training, evaluation, integration, testing, and optimization to deliver high-performing solutions.
- Act as a subject-matter expert in TensorFlow, PyTorch, Halide, and other AI tools.
Preferred Qualifications
- Experience with automatic differentiation and differentiable programming.
- Experience with software design, design patterns, and software architecture.
- Experience with systems modeling and algorithm development.
- Experience in building and training graph neural networks.
- Practical experience with the Swift programming language.
- Experience in vector, SIMD, and tensor computational methods.
- Background in fast-paced startup environments.
- Hands-on experience designing, simulating, or deploying autonomous systems.