AI Engineer, Normal Computing
$150-240k
+ Offers equity
Generative AI for enterprise
Open for applications
Generative AI for enterprise
21-100 employees
Open for applications
$150-240k
+ Offers equity
21-100 employees
Company mission
To bridge artificial intelligence to the most sensitive industrial and advanced manufacturing applications around the globe.
Role
Who you are
- Experience with engineering projects, ideally related to ML or AI
- Strong software engineering skills, especially with distributed systems
- Proficiency in Python and ML frameworks (PyTorch, Hugging Face)
- Experience with prompt engineering and deploying language models
- Ability to handle and preprocess large, diverse datasets
- Understanding of AI safety and responsible development
- Knowledge of AI evaluation and benchmarking
- Clear communication of complex AI concepts
Desirable
- Experience deploying AI in production environments
- Cloud platform knowledge for AI deployment
- Familiarity with advanced AI techniques (few-shot learning, meta-learning)
- Open-source AI contributions or relevant publications
- Ability to balance AI innovation with production-ready systems
- Adaptability in a fast-paced, evolving AI landscape
What the job involves
- We are looking for an AI Engineer to help us take systems that understand large technical documents and turn them into code
- This is a job of excitement and exploration, requiring AI expertise and software engineering skills to build complex systems. Knowledge of semiconductors is a plus
- At Normal Computing, you will contribute to cutting-edge machine learning applications across various aspects of our business, working on solutions from the early stages of research and development to production deployment for our customers
- Contribute to AI projects from concept to production deployment
- Design and build systems using large language models to process complex technical documents
- Develop solutions for multi-modal data handling (PDFs, logs, tables)
- Solve challenging AI and software engineering problems
- Work with other teams to integrate AI into our products
- Create robust AI evaluation frameworks
- Develop strategies to manage AI-specific challenges (latency, variance, errors)
- Keep up with AI advancements, especially in language models and multi-modal AI
Our take
As the rate of AI adoption accelerates, Normal Computing sets out to make it more usable and scalable for enterprises, where risk is a central barrier to its implementation. The hope is that this will allow enterprises to make use of technologies such as Generative AI which has so far only been considered appropriate for early consumer applications.
The company's Probabilistic AI gives control over reliability, adaptivity, and auditability to AI models powered by its customers' private data. It boasts the ability to support such applications as automating complex underwriting processes, generating and validating specialized code, and mitigating risks in situations where the conditions are dynamic and fluctuating.
By targeting a major roadblock to enterprise AI adoption, Normal Computing has the chance to make a major (and profitable) contribution to accelerating complex AI use cases. The company has raised considerable funding to support its endeavours, which it will use to double down on the R&D of its application development platform and Probabilistic AI technology.
Freddie
Company Specialist at Welcome to the Jungle
Insights
Few candidates hear
back within 2 weeks
Company
Funding (last 2 of 3 rounds)
Nov 2024
$63.2m
GRANT
Jun 2023
$8.5m
SEED
Total funding: $80.2m
Company HQ
Civic Center, New York, NY
Articles
Leadership
Faris Sbahi
(Co-Founder & CEO)Has worked as a Research Scientist at X, the moonshot factory, a Software Engineer at Google, and as a Forward Deployed Engineer at Palantir Technologies.
Matthias Tan
(Co-founder)Venture Partner at NextGen Venture Partners. Former Principal of Strategy and Partnerships at X, the moonshot factory, and Director of Product and Partnerships at Pony.ai.
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