Your Role in Our Mission:As an AI Engineer Lead, you will play a pivotal role in leading and managing projects from concept to production, mentoring team members, and influencing the strategic direction of our AI initiatives. Knowledge of semiconductor design and manufacturing is a plus.
Responsibilities:Lead AI projects from concept to production deployment
Solve challenging AI and software engineering problems while promoting best practices
Create showtime-ready benchmarks to continually measure quality and robustness of solutions relative to baselines
Develop and deploy state-of-the-art AI models for problems in hardware engineering with complex logical and uncertainty-bound constraints
Evaluate state-of-the-art Bayesian and non-Bayesian approaches to reliable deep learning and formal verification of AI systems
Set up experimentation tools and synthetic data infrastructure to support rapid experimentation and iteration, with a clear path to production deployment
Develop strategies to manage AI-specific challenges (latency, variance, errors)
Keep up with AI advancements, especially in language models and multi-modal AI, and synthetic data generation
What Makes You A Great Fit:4+ years of experience with deep learning frameworks like Pytorch, Tensorflow, Jax
Rich leadership experience over the "full stack" when it comes to designing, training, evaluating and deploying machine learning models, especially large generative models
Strong software engineering skills, especially in building complex, distributed systems around AI technologies
Expertise in prompt engineering, fine-tuning, and deploying large generative models in production environments
Skilled in handling and preprocessing large datasets for AI applications, including multimodal data
Strong understanding of AI evaluation metrics and benchmarking methodologies
Excellent communication skills, with the ability to explain complex AI concepts to technical and non-technical stakeholders
What Elevates Your Application:Experience deploying AI models in high-stakes or regulated environments
Hands-on experience with cloud platforms for large-scale AI deployment
Familiarity with probabilistic programming languages (e.g., TensorFlow Probability, Pyro) and probabilistic reasoning methods (e.g. Bayesian NNs or Monte Carlo Tree Search)
Specialized knowledge in advanced AI techniques such as few-shot learning, meta-learning, or AI alignment, and relevant frameworks like DSPy
Contributions to open-source AI projects or publications in top-tier AI conferences/journals
Deep curiosity for or experience in semiconductors and physics
A "defensive AI engineering" mindset, with experience handling the challenges of working with non-deterministic AI systems
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