Lead Machine Learning Engineer
The basics
Your title will be Lead Machine Learning Engineer
Matt, Cofounder and CTO, is the hiring manager
We work Mon-Thu in our office in Chancery Lane, London, Fri from anywhere
Compensation: £200k+ salary, and matched equity
What are we building?
Walk around the average office and you’ll see people’s days taken up by emails, Slack and meetings instead of real work.
People in client facing roles - think estate agents, insurance brokers, recruiters - feel this pain most acutely. Instead of meeting clients, they spend hours doing admin. Following up. Scheduling meetings, then taking notes on them. Answering questions they’ve been asked a thousand times. Sorting through the mess that is their inbox.
We’ve built an AI executive assistant that looks at all your emails, messages and meetings, and uses that knowledge to answer your email, schedule meetings, take next steps from meetings and organise your inbox.
How has it been going?
Since launching in April 2024, we've gone from $0 to $30m in ARR and raised a $30m Series B from top investors. We’re currently a team of 18 engineers.
What do we value?
Autonomy, agency, and ownership.
Each of our engineers owns a business area. They own both the strategy (we have no product managers and have no plans for that to change) and the execution of that strategy.
They choose when to bring in qualitative data (customer interviews, surveys etc) and quantitative, supported by our data engineering department.
We’re very intentional about adding new people. We think a small team of exceptional people working hard at a problem they care about will always beat a larger, less focused team. That does mean you’ll need to bring an intensity to this role that might not be asked at other companies. But it also means you will be fast tracked into more senior roles and responsibilities far earlier.
What will I do?
Currently, Fyxer predicts the next email a salesperson will send, to save them time - both when a reply is needed, and what the user will say.
In 2026, we’ll predict the next action a salesperson should take to move their important relationships forward.
Our competitive edge is the quality of our AI models. We have 30+ fine tuned custom models live in production, all specialised at a specific use case, and working in concert to produce a great experience. Users send 52% of the draft content we generate.
To produce these fine tuned models, we’ve built a world class human data division, composed of 60+ data annotators that have experience at the world’s top AI labs and model providers. They provide the data our models are trained and evaluated on, since it requires subjective human judgment - what emails are marketing, when to set up a meeting, etc. You’ll have this team as a resource.
You’ll own how we build out and improve the system for predicting the next action our users (salespeople) should take to move their relationships forward:
Selecting the best model architecture and overall approach to use. It will need to be a complex system involving a mixture of LLM steps and traditional ML models.
Picking evaluation metrics, and designing systems to analyse models in production to identify improvement areas
Identifying when to use our human data team to provide training or validation datasets
Reading relevant research to find the best approach for our use case
In partnership with our CTO, defining how ML works with product engineering and our model ops and human data teams, and how the team develops from here
What does our ideal hire look like?
You’ve worked at a scaleup tech company as an ML/AI engineer, or been a founder of an AI focused startup
You want to drive the strategy in your area, rather than just being handed tickets. You’ll be expected to proactively discover possible improvements by looking at usage data, reading relevant research papers, and evaluating models in production
You’re product focused: you can translate context on what the product is trying to achieve for users into technical decisions, such as on model architecture, category sets/ontology, evaluation methods etc.
Bonus: you’ve spent a large portion of the last 4 years working with systems involving generative AI
Bonus: you’ve built recommendation systems in past roles
Urgency and intensity in your work
Our tech stack
We use the following stack for the human data platform. It’s not a requirement to have worked with every tool in this stack, but the more the better!
A 60 person data annotation team working on a custom platform, to produce training or validation data where human judgment is needed
API integrations with the OpenAI API and Google Vertex AI
Typescript for backend code
Firestore as our database
Firebase Auth as our auth system
Backend deployed on Firebase Functions, and making use of PubSub and Cloud Storage
React frontend, using ShadCN for components, TailwindCSS for styling, React Query for state management
Sentry and Google Cloud Logging for monitoring
Github Actions for CI/CD
The application process
Submit your CV (no need for a cover letter)
An initial call with someone from the Fyxer AI team to review your experience and motivation for joining (15 mins)
An interview with the hiring manager discussing your experience (45 minutes)
Take home test
Review of take home test, in office (60 minutes) + meet team in office (30 minutes)