ML Model Evaluation Engineer New London, UK
ML Model Evaluation Engineer New London, UK

ML Model Evaluation Engineer New London, UK

London Full-Time 43200 - 72000 £ / year (est.) No home office possible
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At a Glance

  • Tasks: Design experiments and evaluate AI models for structured reasoning capabilities.
  • Company: Join Symbolica, an innovative AI research lab transforming machine learning with category theory.
  • Benefits: Enjoy a competitive salary, equity options, and a high-trust work environment.
  • Why this job: Be part of a mission-driven team pushing the boundaries of AI and logical reasoning.
  • Qualifications: Experience in machine learning, Python, and deep learning model evaluation is essential.
  • Other info: Onsite role in London; visa sponsorship available for qualified candidates.

The predicted salary is between 43200 - 72000 £ per year.

About Us

Symbolica is an AI research lab pioneering the application of category theory to enable logical reasoning in machines. We’re a well-resourced, nimble team of experts on a mission to bridge the gap between theoretical mathematics and cutting-edge technologies, creating symbolic reasoning models that think like humans – precise, logical, and interpretable. While others focus on scaling data-hungry neural networks, we’re building AI that understands the structures of thought, not just patterns in data. Our approach combines rigorous research with fast-paced, results-driven execution. We’re reimagining the very foundations of intelligence while simultaneously developing product-focused machine learning models in a tight feedback loop, where research fuels application. Founded in 2022, we’ve raised over $30M from leading Silicon Valley investors, including Khosla Ventures, General Catalyst, Abstract Ventures, and Day One Ventures, to push the boundaries of applying formal mathematics and logic to machine learning. Our vision is to create AI systems that transform industries, empowering machines to solve humanity’s most complex challenges with precision and insight.

About the Role

As an ML Model Evaluation Engineer, you’ll play a critical role in helping us measure progress, design rigorous experiments, and surface meaningful signals as we build models with structured reasoning capabilities. You’ll work alongside researchers and ML engineers to design benchmarks, run large-scale evaluations, and analyse model behaviour — ensuring we’re focused on real-world performance, not just proxy metrics. This is a role for someone who thrives on experimentation, iteration, and tight feedback loops — someone who loves discovering what works (and what doesn’t) and can design systems to test hypotheses at scale. This is an onsite role based in our London office (66 City Rd).

Your Focus

  • Design and implement robust experiments to evaluate specific model capabilities
  • Build and maintain high-frequency evaluation pipelines using PyTorch or JAX
  • Engineer benchmark datasets — collecting, filtering, and decontaminating data for meaningful evals
  • Create evaluation protocols that measure the right capabilities and avoid metric gaming
  • Research and implement strong baselines from literature or current frontier models
  • Scale experiments and data analysis to match the demands of large model training runs
  • Analyse outputs from eval runs, identify bottlenecks, and present findings clearly to the team
  • Collaborate with researchers and engineers to refine evaluation design and keep feedback loops tight
  • Contribute to the development of a general-purpose evaluation suite integrated into infra and tooling

About You

  • Proficient hands-on experience in machine learning, ideally with a focus on experimental design or evaluation
  • Strong engineering skills in Python and PyTorch (or JAX)
  • Deep understanding of training and evaluating large-scale deep learning models
  • A scientific mindset — you know how to design a clean experiment and what makes a result trustworthy
  • Comfortable building infrastructure for benchmark automation and eval pipelines
  • Excellent analytical and data-mining skills; comfortable summarising experimental insights to inform team direction
  • Familiarity with recent literature and capability evaluations in the frontier AI space
  • Collaborative and thoughtful communicator — excited to work closely with both researchers and engineers
  • Bonus: experience building benchmark suites, red-teaming evals, or integrating eval infra into full-stack ML pipelines

What We Offer

  • Competitive salary and early-stage equity package
  • High trust, low bureaucracy environment focused on real impact
  • Opportunity to build foundational research tools and shape model development direction
  • Work closely with top-notch researchers and ML engineers pushing the edge of machine reasoning

We are able to sponsor a Skilled Worker visa for qualified candidates applying to this position. This specific role exceeds the minimum salary threshold set by the UK government for Skilled Worker visa sponsorship. Please note that English language proficiency at B2 level or higher is required for this role. Symbolica is an equal opportunities employer. We celebrate diversity and are committed to creating an inclusive environment for all employees, regardless of race, gender, age, religion, disability, or sexual orientation.

ML Model Evaluation Engineer New London, UK employer: Symbolica

At Symbolica, we pride ourselves on being an exceptional employer, offering a high-trust, low-bureaucracy environment that fosters innovation and real impact. Our London office is a hub for collaboration with top-tier researchers and engineers, providing unique opportunities for professional growth while working on groundbreaking AI technologies. With competitive salaries, equity packages, and a commitment to diversity and inclusion, we empower our employees to shape the future of machine reasoning in a supportive and dynamic setting.
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Contact Detail:

Symbolica Recruiting Team

StudySmarter Expert Advice 🤫

We think this is how you could land ML Model Evaluation Engineer New London, UK

✨Tip Number 1

Familiarise yourself with the latest research in machine learning, particularly around model evaluation and experimental design. This will not only help you understand the role better but also allow you to engage in meaningful conversations during interviews.

✨Tip Number 2

Showcase your hands-on experience with Python and frameworks like PyTorch or JAX. Consider working on personal projects or contributing to open-source initiatives that demonstrate your skills in building evaluation pipelines and conducting experiments.

✨Tip Number 3

Prepare to discuss specific examples of how you've designed experiments or evaluated models in the past. Be ready to explain your thought process, the challenges you faced, and how you overcame them, as this will highlight your scientific mindset.

✨Tip Number 4

Network with professionals in the AI and machine learning community. Attend relevant meetups or conferences where you can connect with researchers and engineers, which may provide insights into the company culture and the specifics of the role you're applying for.

We think you need these skills to ace ML Model Evaluation Engineer New London, UK

Machine Learning Expertise
Experimental Design
Python Programming
PyTorch or JAX Proficiency
Data Analysis
Benchmarking Techniques
Evaluation Protocol Development
Analytical Skills
Collaboration and Communication
Infrastructure Development for Automation
Understanding of Deep Learning Models
Literature Review in AI
Problem-Solving Skills
Attention to Detail

Some tips for your application 🫡

Tailor Your CV: Make sure your CV highlights relevant experience in machine learning, particularly in experimental design and evaluation. Emphasise your proficiency in Python and frameworks like PyTorch or JAX, as well as any experience with large-scale deep learning models.

Craft a Compelling Cover Letter: In your cover letter, express your enthusiasm for Symbolica's mission and how your skills align with their goals. Discuss your scientific mindset and provide examples of past projects where you designed experiments or evaluated model performance.

Showcase Relevant Projects: If applicable, include links to any relevant projects or publications that demonstrate your expertise in machine learning and evaluation. This could be GitHub repositories, research papers, or presentations that showcase your analytical skills and understanding of the field.

Prepare for Technical Questions: Anticipate technical questions related to machine learning evaluation and experimental design during the interview process. Brush up on recent literature and be ready to discuss how you would approach designing benchmarks and evaluation protocols.

How to prepare for a job interview at Symbolica

✨Understand the Role

Make sure you have a solid grasp of what an ML Model Evaluation Engineer does. Familiarise yourself with the specific responsibilities mentioned in the job description, such as designing experiments and building evaluation pipelines. This will help you articulate how your skills align with their needs.

✨Showcase Your Technical Skills

Be prepared to discuss your hands-on experience with Python, PyTorch, or JAX. Highlight any projects where you've designed experiments or evaluated models, and be ready to dive into technical details that demonstrate your expertise in machine learning.

✨Prepare for Problem-Solving Questions

Expect questions that assess your scientific mindset and problem-solving abilities. Think of examples where you've had to design clean experiments or troubleshoot issues in model evaluations. Use the STAR method (Situation, Task, Action, Result) to structure your responses.

✨Emphasise Collaboration

Since the role involves working closely with researchers and engineers, be sure to highlight your collaborative skills. Share experiences where you've successfully worked in teams, communicated findings, and contributed to refining evaluation designs. This will show you're a good fit for their team-oriented culture.

ML Model Evaluation Engineer New London, UK
Symbolica
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