Full-Time

Probabilistic Machine Learning Engineer

Updated on 6/7/2024

Normal Computing

Normal Computing

1-10 employees

Generative AI platform for enterprise applications

AI & Machine Learning

Mid

New York, NY, USA

Required Skills
Tensorflow
Data Structures & Algorithms
Pytorch
Pandas
NumPy
Requirements
  • Applied experience with data modeling
  • Applied experience with machine learning, preferably modern deep learning architectures
  • Experience with machine learning training objectives beyond accuracy
  • Experience with at least one programming language
  • Experience using TensorFlow, PyTorch, Jax, NumPy, Pandas or similar ML/scientific libraries
  • Familiarity with probabilistic programming languages
Responsibilities
  • Develop and implement state-of-the-art probabilistic machine learning models and algorithms
  • Develop and implement robust data pipelines at a variety of scales
  • Explore Bayesian and non-Bayesian approaches to Reliable Deep Learning
  • Develop evaluation metrics and procedures to assess the performance of LLMs
  • Work closely with the engineering team to build seamless features and integrations
  • Collaborate with research scientists and data scientists to develop new models and techniques

Normal Computing offers a generative AI platform for critical enterprise applications, leveraging Probabilistic AI to enhance reliability, adaptivity, and auditability of AI models. The company's technology, developed by former members of Google Brain, Palantir, and Alphabet X, focuses on enabling transformative value in high-stakes enterprise applications through novel full-stack probabilistic machine learning infrastructure driven by thermodynamic physics.

Company Stage

Seed

Total Funding

$8.5M

Headquarters

New York, New York

Founded

2022

Growth & Insights
Headcount

6 month growth

0%

1 year growth

0%

2 year growth

0%