Founding AI Engineer

IC Resources; Greater London; On-site; GBP 70,000 - 110,000; Full time

Benefits offered by this job

Equity

Job summary

IC Resources is seeking a technically strong Research Scientist or Engineer to join early and help build systems that learn from user interactions and shape the experience.

You'll work at the intersection of ML, applied research and product-facing engineering, with real influence on how the core technology evolves as the product takes shape in a formative stage.

Qualifications

• A few years of hands-on experience shipping ML or AI-driven products.
• Exposure to model fine-tuning, evaluation work, or working with human feedback data.
• Comfortable in Python, with practical experience in common deep learning tooling.
• Self-starter who's happy figuring things out without a lot of structure.

Responsibilities

• Build out ways to measure whether the system is responding appropriately given what it knows about a user.
• Turn real usage and feedback into structured signals the model can learn from.
• Run training experiments aimed at improving response quality based on human feedback.
• Dig into cases where the system gets it wrong—misreads a user, holds onto outdated info, or responds inconsistently.
• Weigh in on technical decisions as the product architecture comes together.

Skills

ML engineering

Model evaluation

Human feedback data

Self-starter

Tools

Python

Deep learning tooling

Fine-tuning

Job description

A growing AI company is looking for a technically strong Research Scientist or Engineer to join early and help build systems that get smarter about the people using them, learning from how someone interacts with the product and using that to shape the experience they get. The team blends machine learning, applied research, and product-facing engineering, and is led by a founder with a solid research background.

You'd be joining at a formative stage, with real input into how the product's core technology takes shape.

The work involves

• Building out ways to measure whether the system is responding appropriately given what it knows about a given user
• Turning real usage and feedback into structured signals the model can learn from
• Running training experiments aimed at improving response quality based on human feedback
• Digging into cases where the system gets it wrong — misreads a user, holds onto outdated info, or responds inconsistently
• Weighing in on technical decisions as the product architecture comes together

Ideal background

• A few years of hands-on experience shipping ML or AI-driven products
• Exposure to model fine-tuning, evaluation work, or working with human feedback data
• Comfortable in Python, with practical experience in common deep learning tooling
• Self-starter who's happy figuring things out without a lot of structure

Competitive salary + equity.