Staff AI Engineer at Normal Computing Corporation
London, England, United Kingdom -
Full Time


Start Date

Immediate

Expiry Date

19 Nov, 25

Salary

0.0

Posted On

19 Aug, 25

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

Good communication skills

Industry

Information Technology/IT

Description

WHAT MAKES YOU A GREAT FIT:

  • Proven track record leading engineering projects, ideally in 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
  • Expertise in AI evaluation and benchmarking
  • Clear communication of complex AI concepts
Responsibilities

YOUR ROLE IN OUR MISSION:

We are looking for a Staff AI Engineer that can help us take systems that understand large technical documents and turn them into code. This is a demanding job, requiring both strong AI expertise, software engineering skills and the ability to dive deep into chip specifications. Knowledge of semis is a plus.
At Normal Computing, you will play a pivotal role in leading and managing projects, mentoring team members, and influencing the strategic direction of our AI initiatives. Your expertise will drive cutting-edge machine learning applications across various aspects of our business, driving solutions from the early stages of research and development all the way to production deployment for our customers.

RESPONSIBILITIES:

  • Lead 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
  • Guide junior engineers and promote best practices
  • 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, and synthetic data generation
Loading...