Member of Technical Staff, Machine Learning

ActAI; Greater London; On-site; GBP 90,000 - 140,000; Full time

Job summary

ActAI is hiring ML engineers to build and improve AI-native applications. You will own end-to-end ML solutions from model development to production deployment, focusing on classification, ranking, retrieval and personalization.

You will work on production-ready pipelines, from data handling and model evaluation to deploying scalable inference systems, collaborating with engineers and product teams to ship reliable ML-powered features.

Qualifications

• Solid ML foundations and modern neural architectures.
• Experience deploying ML models to production systems.
• Background in classification, ranking, retrieval or personalization.
• Familiar with model evaluation, experimentation and metrics.
• Writes production-quality code with real-world data.
• Balances model quality, latency, cost and reliability.
• Strong ownership and problem-solving skills.

Responsibilities

• Build and improve ML models for classification, ranking and personalization.
• Develop efficient classifiers and optimize accuracy, latency and cost.
• Improve search relevance and context retrieval using embeddings and reranking.
• Build personalization models that respect user privacy.
• Develop evaluation pipelines, datasets and metrics to measure quality.
• Fine-tune and adapt models with sufficient data.
• Build and maintain ML training, inference and deployment pipelines.

Skills

ML foundations

Production ML

Classification/Ranking

Model evaluation

Production code

Latency optimization

Ownership

Tools

Python

PyTorch/JAX

Embeddings/Vector search

Production ML pipelines

Job description

There are over 5 billion users using basic applications today such email, notes, tasks, calendar and they're not AI-native. Our mission is to build proactive applications for anyone in the world, who are not used to complex prompting. We aim to bring intelligence to conversations, errands, organising and workflows, with minimal to no prompting.

Our first application is an agent-first mobile communication app. We believe building applications that users use daily for communication, coordination and collaboration needs will help integrate AI into their lives. The net effect is to eliminate misinformation, achieve low coordination costs, enable high quality tasks and collaboration at scale with minimal or little prompting.

About

There are over 5 billion users using basic applications today such email, notes, tasks, calendar and they're not AI-native. Our mission is to build proactive applications for anyone in the world, who are not used to complex prompting. We aim to bring intelligence to conversations, errands, organising and workflows, with minimal to no prompting.

Our first application is an agent-first mobile communication app. We believe building applications that users use daily for communication, coordination and collaboration needs will help integrate AI into their lives. The net effect is to eliminate misinformation, achieve low coordination costs, enable high quality tasks and collaboration at scale with minimal or little prompting.

Role

Build and improve ML systems that power our AI-native applications.

Work on classification, retrieval, ranking, personalization and evaluation, taking ownership from model development to production deployment.

This role is for engineers who enjoy solving practical ML problems, improving model quality and shipping reliable systems used by real users.

Focus

• Build and improve ML models for classification, ranking, retrieval and personalization.
• Develop efficient classifiers and optimize model accuracy, precision, latency and cost.
• Improve search relevance and context retrieval using embeddings, hybrid search and reranking.
• Build personalization models that learn from user behavior while respecting privacy.
• Develop evaluation pipelines, labeled datasets and metrics to measure model quality.
• Fine-tune and adapt existing models when supported by sufficient data.
• Build and maintain ML training, inference and deployment pipelines.
• Debug production issues, monitor model performance and ship improvements based on real-world feedback.

Tech Stack

• Python
• PyTorch / JAX
• Embeddings, vector search and ranking models
• Production ML systems and inference pipelines

Ideal Experience

• Strong foundations in machine learning and modern neural architectures.
• Hands-on experience building and deploying ML models in production.
• Experience in classification, ranking, retrieval, recommendation systems or personalization.
• Understanding of model evaluation, experimentation and performance metrics.
• Comfortable writing production-quality code and working with real-world datasets.
• Able to balance model quality, latency, cost and reliability.
• Strong ownership, problem-solving skills

Outcomes

• ML models in production meet expected accuracy, relevance, latency and reliability targets.
• Classification and ranking improvements deliver measurable gains in model quality.
• Evaluation pipelines identify regressions and support reliable model releases.
• Training, inference and data pipelines are robust, reproducible and maintainable.
• Model improvements are driven by real-world feedback and measurable results.
• Collaborates effectively with engineering and product teams to deliver reliable ML-powered features.

How We Work

The best products in the world are built by small, world-class teams. We are a high-talent-density, hands-on team. We make decisions collectively and move quickly, balancing high-quality work with continuous learning.

Joining our team requires the ability to bring structure, exercise judgment and execute independently. Our goal is to put a truly magical product into the hands of our users.

Interview Process

If there appears to be a fit, we'll reach out to schedule 3, but no more than 4 interviews.

Applications are evaluated by our technical team members. Interviews will be conducted virtually and/or onsite.

We value transparency and efficiency, so expect a prompt decision. If you've demonstrated the exceptional skills and mindset we're looking for, we'll extend an offer to join us.