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ML Engineering Lead

companyNormal Computing Corporation
locationSan Francisco, CA, USA
PublishedPublished: 6/14/2022
Full Time

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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