ML Engineer in London

Member of Technical Staff, North Modelling (Evals)

Build evaluation systems, feedback loops, and applied modelling workflows to ensure model progress translates into better product outcomes for North users. Define and own the eval strategy for North, create high-quality evals from product realities, and continuously update evals based on user and customer feedback. Extract insights from eval results and product context to guide model selection, patches, and updates. Collaborate closely with product, customer-facing, and modelling teams to define success metrics and actionable recommendations.

Design and build end-to-end agentic systems for creative tasks; Develop novel approaches for training and adapting large language models for multimodal creative tasks; Design new objectives, datasets, and fine-tuning strategies to improve agent behavior and reliability; Explore multimodal reasoning and structured generation for creative control; Run systematic experiments to evaluate and improve agent performance in real-world tasks; Design evaluation frameworks for agentic workflows in video analysis and editing; Analyze failure modes across the full agent loop and iterate on improvements.

Train and optimize large-scale video and multimodal models; Improve efficiency across training and inference (memory, latency, cost); Implement techniques such as distillation, quantization, and pruning to accelerate diffusion and autoregressive generation; Build and maintain distributed training systems; Optimize GPU utilization, parallelism, and throughput; Develop tooling for experimentation, evaluation, and debugging; Translate research models into robust, production-ready systems; Monitor and improve model performance in real-world usage.

• Own ML projects end to end within a product team, including framing the problem, choosing the approach, executing the plan, shipping the feature, and monitoring its performance in production.
• Work closely with SMB, Scaler, or Enterprise product teams on focused ML problems tied to clear customer and business outcomes.
• Make build vs. buy decisions for ML models, deciding when to fine-tune or build models versus integrating external models or APIs.
• Move quickly from idea to production, prioritizing rapid iteration and customer impact.
• Collaborate with the ML team to leverage shared infrastructure, evaluation tooling, and expertise.
• Stay close to users by getting rapid feedback, monitoring usage, and iterating based on insights.
• Balance technical depth with pragmatism, knowing when deeper modeling is necessary and when existing solutions suffice.
• Write production-quality code directly in the product team's codebase.
• Own quality in production by building sound evaluations, monitoring performance, and investigating failures post-launch.

Set and evolve the research direction for A1’s core intelligence, including context representation, memory, reasoning, planning, and orchestration. Decide when to design new model architectures versus adapting or leveraging frontier open-source or commercial models. Define evaluation frameworks that measure real-world usefulness, robustness, safety, and long-term behavior. Own alignment, safety, and guardrail strategy as first-class product concerns. Guide exploration of frontier techniques such as retrieval-augmented training, mixture-of-experts, distillation, multi-agent orchestration, and multimodal systems. Shape early product intelligence direction in close partnership with product and application engineering. Set the technical bar for research rigor, judgment, and taste across the organization.

Set and evolve the research direction for A1’s core intelligence, including context representation, memory, reasoning, planning, and orchestration. Decide when to design new model architectures versus adapting or leveraging frontier open-source or commercial models. Define evaluation frameworks that measure real-world usefulness, robustness, safety, and long-term behavior. Own alignment, safety, and guardrail strategy as first-class product concerns. Guide exploration of frontier techniques such as retrieval-augmented training, mixture-of-experts, distillation, multi-agent orchestration, and multimodal systems. Shape early product intelligence direction in close partnership with product and application engineering. Set the technical bar for research rigor, judgment, and taste across the organization.

• Own ML projects end to end within a product team, including framing the problem, choosing the approach, executing the plan, shipping the feature, and monitoring its performance in production.
• Work closely with SMB, Scaler, or Enterprise product teams on focused ML problems tied to clear customer and business outcomes.
• Make build vs. buy decisions for ML models, deciding when to fine-tune or build models versus integrating external models or APIs.
• Move quickly from idea to production, prioritizing rapid iteration and customer impact.
• Collaborate with the ML team to leverage shared infrastructure, evaluation tooling, and expertise.
• Stay close to users by getting rapid feedback, monitoring usage, and iterating based on insights.
• Balance technical depth with pragmatism, knowing when deeper modeling is necessary and when existing solutions suffice.
• Write production-quality code directly in the product team's codebase.
• Own quality in production by building sound evaluations, monitoring performance, and investigating failures post-launch.

• Own ML projects end to end within a product team, including framing the problem, choosing the approach, executing the plan, shipping the feature, and monitoring its performance in production.
• Work closely with SMB, Scaler, or Enterprise product teams on focused ML problems tied to clear customer and business outcomes.
• Make build vs. buy decisions for ML models, deciding when to fine-tune or build models versus integrating external models or APIs.
• Move quickly from idea to production, prioritizing rapid iteration and customer impact.
• Collaborate with the ML team to leverage shared infrastructure, evaluation tooling, and expertise.
• Stay close to users by getting rapid feedback, monitoring usage, and iterating based on insights.
• Balance technical depth with pragmatism, knowing when deeper modeling is necessary and when existing solutions suffice.
• Write production-quality code directly in the product team's codebase.
• Own quality in production by building sound evaluations, monitoring performance, and investigating failures post-launch.

Design and evolve critical ML systems across training, inference, evaluation, and infrastructure. Architect and build large-scale ML systems including training pipelines, inference systems, and data systems. Implement evaluation pipelines for performance, robustness, safety, and bias. Own production deployment with GPU optimization, memory efficiency, latency reduction, and scaling. Collaborate with engineering teams to integrate ML systems into products. Make pragmatic trade-offs and ship improvements quickly under production constraints.

• Own ML projects end to end within a product team, including framing the problem, choosing the approach, executing the plan, shipping the feature, and monitoring its performance in production.
• Work closely with SMB, Scaler, or Enterprise product teams on focused ML problems tied to clear customer and business outcomes.
• Make build vs. buy decisions for ML models, deciding when to fine-tune or build models versus integrating external models or APIs.
• Move quickly from idea to production, prioritizing rapid iteration and customer impact.
• Collaborate with the ML team to leverage shared infrastructure, evaluation tooling, and expertise.
• Stay close to users by getting rapid feedback, monitoring usage, and iterating based on insights.
• Balance technical depth with pragmatism, knowing when deeper modeling is necessary and when existing solutions suffice.
• Write production-quality code directly in the product team's codebase.
• Own quality in production by building sound evaluations, monitoring performance, and investigating failures post-launch.

Own end-to-end ML system execution including data pipelines, training workflows, evaluation systems, inference architecture, and deployment. Fine-tune and adapt models using state-of-the-art methods. Architect and operate scalable inference systems balancing latency, cost, and reliability. Design and maintain data systems for high-quality training data. Implement evaluation pipelines for performance, robustness, safety, and bias. Own production deployment with GPU optimization, memory efficiency, latency reduction, and scaling policies. Collaborate with engineering teams to integrate ML systems into products. Make pragmatic trade-offs and ship improvements quickly under real production constraints.

Frequently Asked Questions

Have questions about roles, locations, or requirements for Machine Learning Engineer jobs?

What does a Machine Learning Engineer do?

Machine Learning Engineers design, build, and deploy AI systems that solve real-world problems. They transform research prototypes into production-ready solutions by creating scalable ML pipelines, optimizing model performance, and handling data preprocessing workflows. They integrate models with applications via APIs, implement monitoring systems, and ensure models perform reliably in production environments. Daily tasks include collaborating with data scientists, fine-tuning algorithms, building deployment infrastructure, and maintaining data privacy. They work across diverse applications like recommendation engines, fraud detection systems, and computer vision tools while ensuring models remain accurate and efficient.

What skills are required for Machine Learning Engineer jobs?

Strong programming skills in Python are fundamental, alongside proficiency with ML frameworks like TensorFlow and PyTorch. Machine Learning Engineers need solid mathematics and statistics knowledge, particularly in linear algebra, calculus, and probability theory. Experience with cloud platforms (AWS, GCP, Azure) is essential for deploying models at scale. Skills in data preprocessing, feature engineering, and model evaluation are critical for building effective systems. Engineers should understand MLOps practices, RESTful APIs, containerization tools like Docker, and version control systems. Practical experience with deep learning architectures and natural language processing is valuable for specialized roles.

What qualifications are needed for Machine Learning Engineer jobs?

Most Machine Learning Engineer positions require a bachelor's degree in computer science, mathematics, or related field, with many employers preferring advanced degrees for senior roles. Beyond formal education, employers value demonstrated experience building and deploying machine learning models. A strong portfolio showcasing completed projects is often more important than academic credentials alone. Relevant certifications from cloud providers or in specific ML frameworks can strengthen applications. Employers look for candidates with verifiable experience in model deployment, optimization, and maintenance. Knowledge of software engineering best practices like testing, version control, and documentation is increasingly essential in this hybrid role.

What is the salary range for Machine Learning Engineer jobs?

Machine Learning Engineer salaries vary based on several key factors. Geographic location significantly impacts compensation, with tech hubs like San Francisco, Seattle, and New York typically offering higher wages. Experience level creates substantial differences, with senior engineers earning considerably more than entry-level positions. Specialized expertise in areas like computer vision, reinforcement learning, or NLP can command premium compensation. Company size and industry also influence pay scales, with large tech companies and finance firms often offering higher salaries than startups or non-profits. Educational background, portfolio quality, and demonstrated impact on previous business outcomes further affect earning potential.

How long does it take to get hired as a Machine Learning Engineer?

The hiring timeline for Machine Learning Engineer positions typically ranges from 4-12 weeks, depending on the company's hiring process and your qualifications. The interview process often includes technical screenings, coding challenges, system design discussions, and model implementation exercises. Candidates with strong portfolios demonstrating deployed ML projects may progress more quickly through initial screens. Specialized roles requiring expertise in deep learning or specific domain knowledge might have longer evaluation periods. Companies often test both theoretical understanding and practical implementation skills through multi-stage interviews. Building relationships with hiring managers through professional networks can sometimes accelerate the process.

Machine Learning Engineer jobs remain in high demand across industries as organizations implement AI solutions to solve complex problems. Companies actively recruit ML Engineers for applications in recommendation systems, fraud detection, computer vision, natural language processing, and autonomous technologies. The role's hybrid nature—combining software engineering and data science expertise—makes qualified candidates particularly valuable. Organizations need specialists who can both develop models and deploy them in production environments. While the field is competitive, professionals with demonstrated experience building and maintaining ML systems at scale continue to find strong opportunities, especially those with specialized knowledge in emerging areas like reinforcement learning.

What is the difference between Machine Learning Engineer and Data Scientist?

Machine Learning Engineers focus on implementing and deploying models in production environments, while Data Scientists concentrate on research, analysis, and prototype development. ML Engineers build scalable pipelines, optimize model performance, and create deployment infrastructure using software engineering practices. Data Scientists explore data, develop statistical insights, and experiment with algorithms to solve business problems. ML Engineers build robust, production-ready systems that run at scale. ML Engineers work extensively with frameworks like TensorFlow and deployment tools, whereas Data Scientists may spend more time with analytical tools and statistical methods. While Data Scientists uncover patterns and build proofs of concept, ML Engineers transform these prototypes into robust, production-ready systems that can operate at scale.

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