Overview

Full Stack Software Engineer - AI/ML

The location of the role is London (hybrid working). The duration of the contract is 6 months. The pay rate on offer is £750 - £880 per day (via Umbrella - inside IR35).

Key skills: Python, generative AI, language models/NLP, AI ML, front end development

Summary

Competitive candidates will have a track record of writing and shipping quality, well-documented and well-tested software in the AI/ML industry. We are looking for candidates with experience in the field of Responsible AI, preferably for generative AI or language applications. In addition to ML engineering and data science skills, ideal candidates will demonstrate a keen interest in ethical and safety aspects of using AI in drug discovery and the clinic.

Key responsibilities

• Define and implement key safety metrics for generative AI (and in particular language) applications.
• Perform user research to understand the application's risks and failure modes.
• Integration of AI/ML components with frontend, backend, data and compute infrastructure
• Responsible for high quality software implementations according to best practices and expected to participate in code reviews.
• Liaise with other technical staff and data engineers in the team and across allied teams, to build an end-to-end pipeline consuming other data products
• Deliver ML- and data-driven insights on GenAI datasets.

Basic qualifications

• A degree in a quantitative or engineering discipline (e.g., computer science, computational biology, bioinformatics, engineering, among others); OR equivalent work experience as a professional ML engineer or data scientist.
• Prior experience carrying out user research for software development
• Knowledge of generative AI and large language models
• Strong skills in Python, including development and testing
• Knowledge of agile practices and able to perform in agile software development environments
• Strong knowledge of modern software development tools / ways of working (e.g. git/GitHub)

Preferred qualifications

• Knowledge of AI/ML approaches and deployment of AI/ML powered applications - especially using language models or NLP and agent-based approaches (e.g. LangGraph, PydanticAI, AutoGen)
• Prior experience conducting user research for AI applications
• Knowledge of AI/ML evaluation and benchmarking approaches, including safety and robustness evaluations
• Web-based front end development experience