Senior Machine Learning Engineer

Posted 2 Days Ago
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London, Greater London, England, GBR
Hybrid
Senior level
Artificial Intelligence • Big Data • Machine Learning • Analytics
The Role
Lead the design, training, evaluation, and production deployment of machine learning and agentic systems for geopolitical intelligence. Build scalable workflows using small models, LLMs, retrieval systems, and agent orchestration. Partner with software engineering, product design, and domain experts to translate research into reliable products, make technical decisions, establish engineering and evaluation standards, and mentor team members.
Summary Generated by Built In
About ExTrac

ExTrac is a decision intelligence company used by governments, defence organisations, financial institutions, and corporates operating in complex, fast-moving environments. Our capabilities fuse curated data sources, domain-specific AI, and deep human expertise to transform information overload into clear, actionable foresight.

Our ambition is to become the analytical backbone that organisations rely on when geopolitical uncertainty becomes an opportunity or a strategic risk. More at extrac.ai.

The Role

Location: Hybrid (In-office in London, minimum 2 days per week)

We are looking for a Senior ML Engineer to join ExTrac's Machine Learning team, which owns our modelling capabilities end-to-end.

You will partner directly with software engineering and product design to understand requirements, design and run model evaluations, integrate optimised workflows into production pipelines and ensure results remain reliable at scale.

You will architect cutting-edge agentic systems for large-scale analysis of the geopolitical landscape, train and evaluate domain-specific models for the unique problems our customers face and work with design and engineering to bring them into the product. Alongside domain experts, you will translate research into practical solutions for analysts operating at the frontier of their field.

In this role, you will lead technical direction on major initiatives, set standards across the team, and mentor others. You will take projects from prototype to reliable production deployment, collaborate closely with the engineers who design the data collection and processing backend, and engage directly with senior stakeholders.

We look for people who enjoy challenging problems, keep learning and build systems that scale. We value low ego, genuine ownership and a passion for applying AI to high-stakes intelligence challenges.

What the job involves
  • Design systems that balance research velocity with real product and production constraints

  • Make technical decisions on model selection, training approaches, evaluation and deployment

  • Deliver production-ready systems at scale, combining research rigour with pragmatic engineering

  • Take projects from early exploration through to reliable deployment

  • Uphold high standards for architecture, code quality, evaluation and research practice

You should apply if
  • You like using machine learning to solve challenging problems in a complex domain

  • You enjoy working closely with product, design and the analysts who use what you build, and you take the lead in turning their feedback into technical decisions

  • You have hands-on experience with model serving (small models and LLMs), agent orchestration and retrieval systems, and a thoughtful, current view on effective workflows in this space

  • You care about engineering fundamentals: writing code that's clean and maintainable, and you hold the people around you to that same bar

  • You're comfortable owning ambiguity, setting direction, and mentoring others in a fast-paced environment

Where this role can take you
  • This role puts you at the frontier of applied AI for high-stakes decision-making, with real influence over how ExTrac's agentic systems are built and where they go next.

  • Strong performance here means deeper ownership of technical direction across the ML team, a bigger hand in setting the standards others are held to, and a path toward specialised domain AI leadership or broader technical leadership, depending on what you want to grow into.

Requirements

Due to the nature of our work and the clients we support, applicants must be eligible to obtain UK security clearance. We are currently only able to consider applicants who are nationals of a NATO member state, Australia, or New Zealand.

  • Technical excellence (working on production systems at scale, research that was used, or end-to-end ownership in an early-stage role)

  • Hands-on experience with model serving (small models and LLMs), agent orchestration and retrieval systems

  • Rigour in evaluation and benchmarking

  • Strong engineering fundamentals: ability to write clean, maintainable code and design robust systems

  • Comfort with a fast-paced environment and clear ownership

  • Experience with modern coding agents and thoughtful views on effective workflows

Desirable

  • Early-stage start-up experience

  • Building agentic tools and harnesses

  • Working on domain specific modelling problems

  • Clear writing, systematic thinking and strong communication

Interview Process
  • Initial Intro Interview - 30 Minutes

  • Technical Assessment - 45 minutes

  • System Design Assessment - 45 minutes

  • Founder interview - 30 Minutes

Benefits
  • Competitive salary based on skills and experience.

  • A generous benefits package, including Private Medical Health Insurance and enhanced pension contributions.

  • Enhanced parental leave and a workplace nursery scheme.

  • £500/year education budget with more expensive items (like conferences) covered with manager approval.

  • 33 days of leave across the year inclusive of bank holidays.

  • Flexible working, hybrid: minimum 2 days per week in our central London office.

ExTrac AI provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, colour, religion, sex, national origin, age, disability, genetic information, sexual orientation, gender identity, or gender expression. We are committed to a diverse and inclusive workforce and welcome people from all backgrounds, experiences, perspectives, and abilities.

ExTrac AI is committed to a fair and transparent hiring process. We confirm that this advertisement is for an active, existing open role within our organisation. Please be advised that we may use artificial intelligence-driven tools to assist our recruitment team in screening, assessing, and selecting candidates for this position but all hiring decisions will be made by a member of our team.

Skills Required

  • Eligible to obtain UK security clearance
  • Must be a national of a NATO member state, Australia, or New Zealand
  • Technical excellence through production systems at scale, used research, or end-to-end ownership in an early-stage role
  • Hands-on experience with model serving for small models and LLMs
  • Hands-on experience with agent orchestration
  • Hands-on experience with retrieval systems
  • Experience with rigorous evaluation and benchmarking
  • Strong engineering fundamentals, including clean maintainable code and robust system design
  • Comfort working in a fast-paced environment with clear ownership
  • Experience with modern coding agents and thoughtful views on effective workflows
  • Early-stage startup experience
  • Experience building agentic tools and harnesses
  • Experience working on domain-specific modeling problems
  • Clear writing, systematic thinking, and strong communication
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The Company
62 Employees

What We Do

ExTrac is a decision intelligence company used by governments, defence organisations, financial institutions, and corporates operating in complex, fast-moving environments. It combines curated, hard-to-access data, human expertise, and domain-specific AI (Co-Analyst) to transform information overload into clear, actionable insight about risks and opportunities emerging from world events, supporting defence and security, financial services, insurance, corporate risk, law enforcement, and tech platforms.

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