Machine Learning Engineer
Aiimi Ltd, United Kingdom

We are currently looking for an experienced Machine Learning Engineer to join our Research team - an incredible opportunity for someone who is driven by collaboration and contributing towards and exciting new division.

What does a day in the life of an Aiimi Machine Learning Engineer look like?

• Collaborate with stakeholders to understand their goals and requirements, and then formulate machine learning problems that align with those objectives.
• Identify relevant data sources, collect and pre-process data to ensure it's clean, properly formatted, and ready for analysis. This involves data cleaning, transformation, feature engineering, and handling missing values.
• Design, develop, and implement machine learning algorithms tailored to specific problems. This includes selecting appropriate algorithms, fine-tuning hyperparameters, and optimizing models for performance.
• Train machine learning models using prepared data and evaluate their performance using appropriate metrics.
• Design and conduct experiments to compare different algorithms, models, and techniques to find the best approach for a given problem - document and analyse the results to drive decision-making.
• Create prototypes and proof-of-concept implementations to demonstrate the feasibility of using machine learning for solving problems or achieving specific goals.
• Stay up-to-date with the latest research papers, publications, and advancements in the field of machine learning.
• Work closely with cross-functional teams to ensure alignment between technical solutions and business goals.
• Write clean, maintainable code and use version control systems like Git to manage codebase changes and collaborate effectively with team members.
• Collaborate with software engineering teams to deploy machine learning models into production environments. Ensure that models are scalable, efficient, and properly integrated with other systems.
• Continuously monitor the performance of deployed models, troubleshoot issues, and update as needed to maintain accuracy and effectiveness over time.
• Assess potential biases, fairness, and privacy concerns in machine learning solutions - implement strategies to mitigate these ethical challenges.
• Create comprehensive documentation for models, algorithms, methodologies, and experiments.
• Clearly communicate findings, insights, and technical details to both technical and non-technical audiences through presentations, reports, and discussions.

Requirements

• Proficiency in Python. Must be able to write efficient and clean code for developing and implementing machine learning algorithms.
• Preferable experience in Information Retrieval (elastic stack).
• A deep understanding of various machine learning algorithms, including both supervised and unsupervised learning methods, neural networks, decision trees, support vector machines, clustering algorithms, and more.
• Skills in data cleaning, feature engineering, and handling missing values are important to ensure the quality of the input data.
• Understanding how to evaluate and select the appropriate model for a given problem is crucial. Knowledge of metrics like accuracy, precision, recall, F1-score, and techniques like cross-validation helps in model selection.
• Knowing how to optimize the hyperparameters of machine learning models to achieve the best performance is important. This involves techniques like grid search, random search, and Bayesian optimization.
• Proficiency with version control tools like Git is essential for collaborating with other team members and keeping track of code changes.
• Effective communication skills the ability to translate complex technical concepts to both technical and non-technical stakeholders is important. This includes presenting research findings, explaining model outputs, and collaborating with cross-functional teams.
• The ability to approach problems creatively and think critically is essential for developing innovative solutions to challenging machine learning problems.
• Understanding how to deploy machine learning models into production environments and ensure they are scalable, efficient, and maintainable.
• Being aware of ethical considerations in machine learning, such as bias, fairness, and privacy, and taking steps to mitigate potential issues.

Benefits

• Up to 10% of basic salary in flexible benefits (to include death in service and critical illness cover as standard plus private healthcare, dental, pension etc.)
• 25 Days holiday (excluding bank holidays) increasing by a day every 2 years
• Flexible working
• Promote training and personal development
• Bi-annual company retreats

Job Rewards and Benefits