Lead Machine Learning Engineer

About the Role

As a Lead Machine Learning Engineer at Fyxer AI, you will be a key driver in shaping the future of our AI products. You will own the strategy and execution of building and improving the system for predicting the next action our users (salespeople) should take to move their relationships forward. This role requires a strong sense of autonomy, agency, and ownership, as you will be responsible for a specific business area within a small, highly focused team.

We operate in a hybrid model, working Monday-Thursday in our Chancery Lane, London office, and Friday remotely.

What We're Building

Fyxer AI is developing an AI executive assistant designed to tackle the administrative burden faced by client-facing professionals such as estate agents, insurance brokers, and recruiters. Our AI assistant handles emails, schedules meetings, takes notes, manages follow-ups, and organizes inboxes, allowing users to focus on client engagement. Our current focus is predicting the next email a salesperson will send, and in 2026, we aim to predict the next best action a salesperson should take to advance their important relationships.

Our Progress and Values

Since our launch in April 2024, we've achieved significant growth, reaching $30m in ARR and raising a $30m Series B from top investors. We are a lean team of 18 engineers, emphasizing a culture of autonomy, agency, and ownership. Each engineer is empowered to own their business area, including strategy and execution, supported by our data engineering department. We value intense dedication and a proactive approach, offering fast-tracked opportunities for senior roles and responsibilities.

Responsibilities

• Own the development and improvement of the system for predicting the next action users should take.
• Select the best model architecture and overall approach, which will involve a complex system of LLM steps and traditional ML models.
• Pick evaluation metrics and design systems to analyze models in production to identify areas for improvement.
• Identify opportunities to leverage our 60+ person human data team for training or validation datasets.
• Stay current with relevant research to find optimal approaches for our use cases.
• Partner with the CTO to define the collaboration between ML, product engineering, model operations, and human data teams, and to strategize team development.

Requirements

• Proven experience as an ML/AI engineer at a scaleup tech company or as a founder of an AI-focused startup.
• A strong desire to drive strategy in your area, proactively discovering improvements through usage data, research papers, and model evaluation in production.
• Product-focused mindset: ability to translate product goals into technical decisions regarding model architecture, category sets/ontology, evaluation methods, etc.
• Bonus: Extensive experience (large portion of the last 4 years) working with systems involving generative AI.
• Bonus: Prior experience building recommendation systems.
• Demonstrated urgency and intensity in your work.

Our Tech Stack

While not a strict requirement to have worked with every tool, familiarity with our stack is a plus:

• A 60-person custom data annotation platform team for human judgment data.
• API integrations with OpenAI API and Google Vertex AI.
• Typescript for backend code.
• Firestore as our database.
• Firebase Auth for our authentication system.
• Backend deployed on Firebase Functions, utilizing PubSub and Cloud Storage.
• React frontend, with ShadCN for components, TailwindCSS for styling, and React Query for state management.
• Sentry and Google Cloud Logging for monitoring.
• Github Actions for CI/CD.