Senior Machine Learning Engineer

Key Responsibilities

• Machine Learning and Data Modeling – Model Productionization:
• – Utilizes machine learning (ML) and software development knowledge to
• implement ML models for production with minimal guidance.
• – Contributes to transforming machine learning prototypes into
• production-ready models.
• – Supports collaboration with multiple stakeholders such as Development
• Leads, Product Management, Operations, and Release Management to make, adopt,
• and communicate technical decisions, and shape the development and delivery of
• software.
• Model Development and Deployment – Model Deployment:
• – Contributes to ML model readiness for deployment by scaling models,
• cleaning model code, and ensuring production quality standards are met.
• – Contributes to the automation of machine learning workflows, from data
• extraction, transformation, and loading (ETL) to model deployment and
• monitoring, to establish the continuous integration and continuous delivery of
• machine learning solutions.
• Model Development and Deployment – Model Performance:
• – Utilizes infrastructure and frameworks to monitor the performance and
• alignment with design criteria of trained models and/or systems.
• – Monitors the performance of deployed models and troubleshoots
• independently or in collaboration with Data Science.
• – Interprets novel metrics that provide analytical insights to
• non-technical stakeholders on how well machine learning models are operating.
• Model Development and Deployment – Data Quality:
• – Identifies potential issues related to data quality (e.g., bias,
• fairness), data security, and data privacy, and contributes to minimizing their
• impacts on data analyses and modeling.
• – Contributes to tasks such as data cleaning, preprocessing, and feature
• identification to prepare for and enable model training.
• Internal Collaborations and Impacts – Model Integration and Operation:
• – Contributes to collaboration with multiple stakeholders (e.g., data
• scientists, software developers) to integrate ML models into new or existing
• systems.
• – Supports the partnership between model development and operations,
• ensuring smooth deployment and continuous improvement of ML models.
• – Learns operational considerations of model deployment (e.g.,
• performance, scalability, stability, maintenance).
• – Participates in troubleshooting and debugging support efforts, such as
• addressing issues in machine learning infrastructure and workflow, and helping
• to create robust solutions to prevent future problems.
• Internal Collaborations and Impacts – Tool Development:
• – Contributes to the development and maintenance of tools, platforms,
• environments, and services for internal use.
• Internal Collaborations and Impacts – Coding and Documentation:
• – Contributes to the development of efficient, bug-free, low-complexity
• code from scratch and properly maintains and organizes the existing codebase.
• – Adheres to best practices for version control, code review, and
• continuous integration in machine learning projects.
• – Updates and maintains professional documentation for technical
• processes (experimentation, data collection and analyses, model building).
• Machine Learning Expertise:
• – Develops familiarity with current developments in the machine learning
• field and integrates learnings into model development.
• – Builds familiarity with the usage and development of third-party
• machine learning frameworks, packages, and libraries (e.g., PyTorch, TensorFlow,
• Keras) to continuously evaluate their performance and scalability, and integrate
• them into production environments.
• Core Responsibilities
• Planning & Execution:
• – Independently manages work, monitoring timelines and deliverables to
• ensure projects or initiatives stay on track and meet requirements.
• – Proactively prioritizes work and adapts to resource or timeline
• shifts, suggesting adjustments to maintain project efficiency.
• Collaboration & Partnership:
• – Collaborates across teams to align on expectations and achieve shared
• objectives.
• – Builds and maintains a comprehensive understanding of business,
• stakeholder, and/or customer needs to build and support effective partnerships.
• – Actively listens to diverse perspectives and asks questions to ensure
• understanding of others.
• Problem Solving:
• – Independently identifies and addresses standard and non-standard
• issues in accordance with standard practices, escalating more complex issues as
• appropriate.
• – Analyzes data and/or information from multiple sources to troubleshoot
• standard and non-standard errors.
• – Contributes to knowledge sharing and best practices.
• Continuous Learning:
• – Embraces continuous learning by actively seeking to build knowledge
• and new skills and/or tools and staying current with industry trends and best
• practices.
• – Seeks out and leverages feedback and training to improve skills.
• – Contributes to a culture of continuous learning and knowledge sharing
• with team members.
• Continuous Improvement:
• – Develops ideas and recommends updates to increase the efficiency and
• effectiveness of processes, protocols, and workflows within a team.
• – Seeks input from team members on alternative approaches and methods
• for improving work.