Senior Machine Learning Engineer

PathwaysX, Inc.; United Kingdom; Hybrid; GBP 90,000 - 130,000; Full time

Benefits offered by this job

Health Insurance

Flexible Work

Paid Time Off

Learning & Budget

Retirement Plan

Wellness / Stipend

Job summary

NeuralForge AI Ltd seeks a Senior Machine Learning Engineer to design, develop, deploy, and optimise ML systems for real-world business impact. You will work with data scientists, software engineers, product managers, and AI leadership to turn research into scalable production solutions.

The role emphasises strong programming, practical model development, evaluation skills, and building maintainable ML pipelines with a focus on deployment, monitoring, and responsible AI practices.

Qualifications

• Strong Python programming skills and familiarity with ML frameworks and data processing tools.
• Build and maintain reliable data pipelines for model training, validation, and inference.
• Demonstrate ability to identify data quality issues, leakage risks, and pipeline bottlenecks.

Responsibilities

• Design, develop, train, and deploy machine learning models for business and product applications.
• Build scalable data processing, feature engineering, and model training pipelines.
• Collaborate with data scientists to translate experiments into production-ready solutions.
• Evaluate model performance using appropriate metrics, validation techniques, and testing methods.
• Implement model monitoring, drift detection, versioning, and retraining workflows.
• Optimise model inference performance, scalability, reliability, and resource utilisation.
• Integrate machine learning models into applications and services via APIs and backend systems.
• Apply responsible AI practices, including bias evaluation, explainability, and model limitations assessment.
• Maintain technical documentation covering datasets, experiments, models, and deployment processes.
• Investigate model failures and production issues, implementing corrective actions.
• Contribute to code reviews, engineering standards, and knowledge sharing across the AI/ML team.
• Stay informed about emerging ML frameworks, architectures, and deployment practices.

Skills

Python

ML frameworks

Data pipelines

Model evaluation

Job description

NeuralForge AI Ltd is seeking a Senior Machine Learning Engineer to design, develop, deploy, and optimise machine learning systems that solve real-world business problems. The successful candidate will work closely with data scientists, software engineers, product managers, and AI leadership to transform research and prototypes into reliable, scalable production solutions. This role requires strong programming skills, practical model development experience, sound understanding of model evaluation, and the ability to build maintainable machine learning pipelines. Success will be measured through model performance, system reliability, deployment efficiency, and measurable business impact.

Health Insurance

Private healthcare coverage supporting physical and mental wellbeing.

Flexible Work

Hybrid working arrangements with flexibility around work location and schedules.

Paid Time Off

Paid annual leave to support rest and work-life balance.

Learning & Budget

Dedicated budget for AI/ML courses, technical certifications, conferences, and professional development.

Retirement Plan

Wellness / Stipend

Wellbeing allowance for approved health, fitness, and wellness activities.

02 Responsibilities

• 01 * Design, develop, train, and deploy machine learning models for business and product applications.
• 02 * Build scalable data processing, feature engineering, and model training pipelines.
• 03 * Collaborate with data scientists to translate experimental models into production-ready solutions.
• 04 * Evaluate model performance using appropriate metrics, validation techniques, and testing methods.
• 05 * Implement model monitoring, drift detection, versioning, and retraining workflows.
• 06 * Optimise model inference performance, scalability, reliability, and resource utilisation.
• 07 * Integrate machine learning models into applications and services through APIs and backend systems.
• 08 * Apply responsible AI practices, including bias evaluation, explainability, and model limitations assessment.
• 09 * Maintain technical documentation covering datasets, experiments, models, and deployment processes.
• 10 * Investigate model failures and production issues, implementing corrective actions.
• 11 * Contribute to code reviews, engineering standards, and knowledge sharing across the AI/ML team.
• 12 * Stay informed about emerging machine learning frameworks, architectures, and deployment practices.

What we evaluate against.

Applied Technical Depth

Must have

• Build and maintain reliable data pipelines for model training, validation, and inference.
• Demonstrate strong Python programming skills and familiarity with relevant ML frameworks and data processing tools.
• Identify data quality issues, leakage risks, and pipeline bottlenecks before they affect production systems.

Nice to have

• Data pipelines produce consistent and validated outputs.
• Code follows maintainability, testing, and engineering standards.
• Data quality issues and pipeline failures are identified and addressed promptly.

Must have

• Deploy, version, monitor, and maintain machine learning models in production environments.
• Implement appropriate monitoring for model performance, data drift, service health, and inference latency.
• Collaborate with platform and software engineering teams to improve deployment automation and operational reliability.

Nice to have

• Model deployments are reproducible and follow documented release processes.
• Production issues are investigated, resolved, and documented with appropriate follow-up actions.

Customer & Domain Discovery

Must have

• Deploy, version, monitor, and maintain machine learning models in production environments.
• Implement appropriate monitoring for model performance, data drift, service health, and inference latency.
• Collaborate with platform and software engineering teams to improve deployment automation and operational reliability.

Nice to have

• Model deployments are reproducible and follow documented release processes.
• Production issues are investigated, resolved, and documented with appropriate follow-up actions.

Product & Solution Judgment

Must have

• Design controlled experiments to compare model approaches and validate improvements.
• Apply appropriate evaluation techniques to assess model accuracy, robustness, and generalisation.
• Consider bias, explainability, privacy, and model limitations throughout the development lifecycle.

Nice to have

• Experiments use defined metrics and reproducible methods.
• Model changes are supported by documented evaluation results.
• Relevant risks, limitations, and responsible AI considerations are assessed before deployment.

Execution Under Ambiguity

Must have

• Work effectively with data scientists, product managers, software engineers, and business stakeholders.
• Translate technical findings into actionable recommendations and delivery plans.
• Manage dependencies, communicate risks, and deliver production-ready ML capabilities against agreed priorities.

Nice to have

• Deliverables meet agreed scope and timelines.
• Technical decisions and dependencies are communicated clearly.
• Stakeholders can understand model capabilities, limitations, and business impact.

04 Team dynamics

Team Structure

Cross-functional collaboration with data scientists, AI engineers, software developers, and product teams.

Operating Rhythm

Weekly sprint planning, model performance reviews, technical discussions, and progress updates.

Data-driven collaboration focused on experimentation, knowledge sharing, and joint problem-solving.

Communication

Clear, evidence-based communication of model performance, technical risks, and recommendations.

Decision Making

Decisions supported by model evaluation results, experimental evidence, data quality, and business requirements.

Documentation Culture

Documented experiments, model versions, evaluation metrics, technical decisions, and implementation processes.

05 Company culture

Ownership

Employees take responsibility for AI initiatives, technical decisions, and measurable business outcomes.

Feedback

Teams share constructive feedback through model reviews, code reviews, and product retrospectives.

Transparency

AI decisions, model limits, experiment results, and project risks are communicated openly.

Teams explore emerging AI/ML technologies to build useful, scalable, and differentiated products.

Learning Culture

Employees continuously develop skills in machine learning, data science, model evaluation, and responsible AI.

Data scientists, ML engineers, software engineers, and product teams work together toward shared goals.

06 What success looks like

First 30 days

Understand NeuralForge AI's product portfolio, data environment, existing ML systems, engineering standards, and model deployment processes. Establish relationships with data science, software engineering, product, and AI leadership teams. Review existing models, pipelines, evaluation practices, and operational risks, and identify opportunities for improvement.

90 days

Deliver an agreed machine learning improvement or production capability, strengthen model evaluation and monitoring practices, improve the reliability of relevant data or training pipelines, and establish clear documentation for model performance, limitations, and deployment procedures.

Outcomes

Build and maintain reliable, scalable, and measurable machine learning capabilities that deliver sustained business value. Improve model quality, deployment efficiency, monitoring, and operational reliability while promoting reproducible experimentation, responsible AI practices, and strong collaboration across engineering and product teams.