Full-Time

AI Control Theory & Optimization Scientist

Posted on 4/21/2025

Passive Logic

Passive Logic

No salary listed

Murray, UT, USA

In Person

Category
AI & Machine Learning (2)
,
Required Skills
Python
Data Science
Tensorflow
Neural Networks
Pytorch
iOS/Swift
C/C++
Reinforcement Learning
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.
  • Create 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.
  • Optimize model workflows: 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.
Desired Qualifications
  • Automatic differentiation expertise: Experience with automatic differentiation and differentiable programming.
  • Software design skills: Experience with software design, design patterns, and software architecture.
  • Systems modeling and algorithms: Experience with systems modeling and algorithm development.
  • Graph neural network expertise: Experience in building and training graph neural networks.
  • Swift development skills: Practical experience with the Swift programming language.
  • Computational methods proficiency: Experience in vector, SIMD, and tensor computational methods.
  • Startup experience: Background in fast-paced startup environments.
  • Autonomous systems knowledge: Hands-on experience designing, simulating, or deploying autonomous systems.

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INACTIVE