Machine Learning Engineer
Purpose of the role
The purpose of the Machine Learning Engineer role at Redgate is to leverage expertise in machine learning and software development to design, develop, deploy, and maintain robust machine learning solutions.
About the role
The Machine Learning Engineer specialises in building machine learning-focused software solutions to meet business needs. This includes addressing challenges in data preparation and feature engineering, and ensuring the seamless integration of ML solutions into existing products. Beyond building models and solving technical problems, ML engineers are key in identifying valuable AI/ML use cases and guiding teams on how these technologies can address specific business problems. They also evangelise and demystify AI/ML concepts to promote awareness and adoption across teams and the wider organisation.
Skills
Leadership
- Identifies, champions, and influences stakeholders on ML opportunities across Redgate.
- Collaborates across teams in aligning goals, sharing knowledge, and solving problems together through opportunities like communities of practice, team secondments, and timebox/tiger teams.
- Deputizes for Tech Lead or LSE in either individual meetings or for longer periods of time during holidays or sabbaticals.
- Outcome focused, helping the team get to the objective rather than getting stuck on the small day to day things.
- Shows great, positive collaboration to achieve the desired outcome.
- Able to grow other members of the team through leading and delegation.
- Able to develop and champion simple and maintainable code across Redgate.
- Actively contributes to Level Up activities.
Mentorship
- Actively seeks to be a role model by communicating and acting appropriately within and outside of the team and wider engineering.
- Shares new and existing knowledge in ML with people within and outside their team.
- Proactively supports the development of others on the team.
- Shares experience with others, this could be just in conversation, via community events or by contributing to Architectural Decisions.
- Supporting levelling up engineers as needed (reducing the bus number).
Role-Specific
- Evangelises the art of the possible in AI/ML by upskilling colleagues, sharing use cases, and demonstrating its value.
- Identifies AI/ML opportunities, and helps to translate business needs into clear, actionable ML objectives.
- Performs data cleaning and preprocessing to prepare data for analysis and model training.
- Uses ML algorithms and frameworks to develop, optimise, and deploy models.
- Evaluates and tests machine learning solution, applying appropriate strategies to ensure robustness and reliability.
- Able to benchmark model performance against alternatives, communicating trade-offs and insights effectively.
- Understands big data technologies for handling large datasets and scalable data processing.
- Demonstrates knowledge of classical and complex models and their application to various problems.
- Learns quickly from research papers and applies new ML technologies effectively.
- Researches and implements improvements to existing models for better performance.
- Understands domain-specific knowledge, like database principles, for solving relevant problems.
- Applies ML design patterns, such as rebalancing and hyperparameter tuning, to both model development and evaluation.
Communication
- Shares information and opinions with others adapting style and level of information to the audience.
- Explains complex technical issues to non-technical people in a way that can be understood.
- Communicates with stakeholders outside of the immediate team.
- Articulates and communicates the trade-offs around ML decisions (e.g., space, time, complexity).
Emotional Intelligence
- Recognizes the feelings of others and adapts approach accordingly.
- Able to draw out opinions of others to help make decisions.
- Encourages others in the team to display growth mindset, and leads by example.
Delivery
- Able to plan significant pieces of work – including clarifying requirements, facilitating meetings, breaking tasks down.
- Able to lead on significant pieces of work - including reporting on progress, checking progress with other team members.
- Can be relied upon to complete what they start. Gets things done.
- Contributes to technical processes such as Architecture Decisions with an ML perspective.
Business Knowledge
- Able to understand goals across engineering, and asks questions / challenges goals.
- Uses the context of company goals to inform ML-related work and project priorities.
- Able to collaborate with the product designer to perform and sometimes lead customer research.