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ML Engineer
| Company | Normal Computing |
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| Location | New York, NY, USA |
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| Salary | $150000 – $240000 |
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| Type | Full-Time |
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| Degrees | |
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| Experience Level | Mid Level, Senior |
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Requirements
- 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
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
Preferred Qualifications
- 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
