Job Description

Your Role in Our Mission:

We are looking for Machine Learning Engineers to build systems for distilling diverse hardware engineering data and logic into complex human-centric automation. This is a demanding job, requiring both strong software engineering skills, creativity with probabilistic ML, and the ability to dive deep into domain-specific tribal understanding. Knowledge of semiconductor design and manufacturing is a plus.

You'll work closely with our research scientists, software engineers, and product teams to advance our full-stack products for hardware engineering. We welcome candidates of all experience levels, from mid-level and up.

Responsibilities:

• 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.
• Create showtime-ready benchmarks to continually measure quality and robustness of solutions relative to baselines.
• Architect systems around open source foundation models to process a variety of modalities and rich symbolic logic, including multi-modal hardware descriptive documents, schematics, customer service logs, and tabular data.
• Collaborate with cross-functional teams to integrate AI solutions into our products and services.

What Makes You A Great Fit:

• 4+ years of experience with deep learning frameworks like Pytorch, Tensorflow, Jax.
• Rich ownership of the “full stack” when it comes to designing, training, evaluating and deploying machine learning models, especially large language models.
• Experience with generative models for various modalities.
• Familiarity with cloud infrastructure and deploying ML models from ideation to production.
• Ability to handle and preprocess large datasets, including time-series and sensor data.
• Excellent problem-solving skills and a strategic mindset for identifying valuable solutions.
• Proactive and adaptable mindset, thriving in a dynamic environment, including a transparent and open communication style.

What Elevates Your Application:

• Familiarity with probabilistic programming languages (e.g., TensorFlow Probability, Pyro) and probabilistic reasoning methods (e.g. Bayesian NNs or Monte Carlo Tree Search).
• Familiarity with advanced prompt optimization frameworks like DSPy.
• Contributions to open-source projects or publications in AI-related 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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