ML Engineer

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. Accelerated individual development is expected.

You will join a community of software engineers, chip designers, mathematicians, physicists, and other polymaths who are working to innovate critical aspects of hardware engineering.

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. We look for resumes that show a history of drive, research vision, and production impact.

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

Equal Employment Opportunity Statement

Normal Computing is an Equal Opportunity Employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, or any other legally protected status.

Accessibility Accommodations

Normal Computing is committed to providing reasonable accommodations to individuals with disabilities. If you need assistance or an accommodation due to a disability, please let us know at accomodations@normalcomputing.ai.

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