Senior/Lead Machine Learning Engineer

Description

The company makes an AI-powered process intelligence platform that analyzes a team's work and then generates AI agents to carry it out, helping businesses cut bottlenecks and automate the right tasks.

The company has raised $26 million in Series B funding, is growing quickly, and counts Fortune 500 companies among its customers. Investors include Khosla Ventures and Paladin Capital. Accel named it a Top 100 AI Start-up and the Everest Group named it an industry Leader.

The team is remote-first and spread across more than 16 countries. It is made up of curious, collaborative people who like solving complex problems. The culture centers on high ownership, bottom-up ideas, and open communication.

The role sits on the Data Intelligence team, which turns raw recorded data into clean, structured, anonymized data that other teams depend on.

You will join as a Machine Learning Engineer or Data Scientist and own key parts of the ML pipelines end to end, making them more accurate, cost-efficient, and trustworthy. You will build evaluations and LLM judges, run experiments, and turn promising results into validated production changes. The work is applied and LLM-heavy, including evaluating and fine-tuning open-source models and building smaller, task-specific models.

Responsibilities

• Own, debug, and improve production pipelines for accuracy and efficiency.
• Build and iterate on LLM evaluations and LLM-as-a-judge systems, including datasets, metrics, and benchmarks that track quality.
• Run careful experiments on prompts, pipeline design, model choice, and inputs such as OCR text versus image plus OCR, then turn the findings into clear recommendations.
• Make LLM pipelines cheaper and faster by comparing models, restructuring steps, and swapping LLM calls for smaller, purpose-built models where that makes sense.
• Extend the anonymization pipeline, including evaluating and adapting existing models.
• Work with Python and quality engineers to move experiments into production, and share responsibility for the code you ship.
• Write up findings in documents such as experiment reports and design docs.

Requirements

• An experiment-driven, data-minded approach with solid coding habits.
• A solid background in machine learning or data science, including experimental design, data splits, metrics, bias, and statistical significance, so you can tell when a result is real.
• Hands-on experience using LLMs in production or near-production, especially evaluating them by building eval sets, LLM judges, and benchmarks, then iterating on prompts and pipelines against them.
• Comfort working deep in messy data: exploring it, troubleshooting problems, and turning what you find into measurable gains.
• Strong Python and solid engineering skills. You write clear, readable, tested code that others can take to production, even if you do not own the infrastructure.
• Ability to work independently on ambiguous problems, including framing the question, choosing the approach, and setting the acceptance criteria for shipping.
• Effective use of AI coding tools, with accountability for the output: you understand, can explain, and can defend everything you ship.
• Clear communication in spoken and written English, including explaining technical trade-offs to technical and non-technical audiences.
• Optional: experience with classical ML or deep learning model development.
• Optional: experience with image or OCR pipelines, or with face-related anonymization.
• Optional: experience shipping end-to-end ML features in a startup or scale-up.

Conditions

Fully remote. Candidates must be based in the UK or Europe and work in time zones compatible with London, typically UTC-1 to UTC+3.

Hiring process: a 30-45 minute talent partner screen; a flexible take-home challenge plus a 1-hour technical discussion with the team; a 1-hour technical design exercise and situational questions with two ML Engineers; a 1-hour behavioral interview with engineering leadership; then an offer and identity verification.

The company only contacts candidates from its own email domain and never asks for banking or sensitive personal information during recruiting.

Benefits

• Generous compensation plus stock options, set by an internal framework, market data, and individual skills.
• Work from anywhere: fully remote, from the company's hubs, or a mix of both.
• Company-issued laptop, remote setup stipend, and co-working budget.
• Flexible schedules and locations.
• Generous paid time off on top of local public holidays.
• Enhanced parental leave.
• Health and retirement benefits.
• Annual learning and development budget.
• Annual retreat plus regular virtual and in-person socials.
• The chance to work on projects that shape the future of work.
• Some benefits may vary by location and role.