The Sr Machine Learning Engineer is responsible for joining a product team and contributing to the software design, algorithm design, and overall product lifecycle for a product that our users love.
The engineering process is highly collaborative.
Sr ML Engineers are expected to pair daily as they work through user stories and support products as they evolve.
ML Engineers may be involved in designing and implementing AI/ML algorithms to embed directly into software products.
Activities may include using specific HD process techniques, integration, design, and development.
The role could interface with Business Stakeholders, Technology Infrastructure teams, and Development teams to ensure that business requirements are properly met within a machine learning solution.
The role may also be involved in performance tuning, testing, and product monitoring.
Other responsibilities may include performing customer outreach, designing ML educational material, and data engineering.
Sr ML Engineers should be able to operate independently though will typically work as part of a team with varying skill levels to create, support, and deploy production applications.
This role will review submitted code and provide feedback to improve, based on best practices.
Key Responsibilities:
70% Delivery and Execution:
Collaborates and pairs with other product team members (UX, engineering, and product management) to create secure, reliable, scalable machine learning solutions.
Documents, reviews, and ensures that all quality and change control standards are met.
Works with Product Team to ensure user stories that are developer-ready, easy to understand, and testable.
Writes custom code or scripts to automate infrastructure, monitoring services, and test cases.
Writes custom code or scripts to do "destructive testing" to ensure adequate resiliency in production.
Configures commercial off the shelf solutions to align with evolving business needs.
Creates meaningful dashboards, logging, alerting, and responses to ensure that issues are captured and addressed proactively
10% Learning:
Participates in learning activities around modern software design, machine learning, and development core practices (communities of practice).
Proactively views articles, tutorials, and videos to learn about new technologies and best practices being used within other technology organizations.
20% Support and Enablement:
Fields questions from other product teams or support teams.
Monitors tools and participates in conversations to encourage collaboration across product teams.
Provides application support for software running in production; Proactively monitors production Service Level Objectives for products.
Proactively reviews the Performance and Capacity of all aspects of production: code, infrastructure, data, message processing, and prediction quality.
Direct Manager/Direct Reports:
This Position typically reports to Software Engineer Manager or Sr. Software Engineer Manager.
This Position has 0 Direct Reports.
Travel Requirements:
Typically requires overnight travel 5% to 20% of the time.
Preferred Qualifications:
8+ years of experience in Machine Learning Engineering, AI Engineering, Software Engineering, or a related field, with a proven track record of building and deploying production-grade AI and machine learning solutions.
Experience designing and developing Agentic AI applications, LLM-powered solutions, retrieval-augmented generation (RAG) systems, and intelligent automation workflows.
Strong experience with knowledge graphs, graph engineering, network analysis, semantic search, and building enterprise knowledge layers that connect structured and unstructured data to enable AI and analytics use cases.
Experience developing scalable data pipelines, data products, and feedback loop architectures that support continuous model and agent improvement.
Proficiency in Python and modern AI/ML frameworks and libraries such as PyTorch, TensorFlow, Scikit-learn, Pandas, and related technologies.
Experience with cloud-native AI/ML platforms and infrastructure, preferably Google Cloud Platform (Vertex AI, BigQuery, BigQuery ML), including model deployment, monitoring, and MLOps practices.
Experience building and supporting AI infrastructure, including vector databases, model serving platforms, APIs, microservices, distributed systems, and high-availability architectures.
Strong understanding of software engineering best practices, including CI/CD, version control, automated testing, security, and performance optimization.
Experience working with large-scale structured and unstructured datasets, SQL, NoSQL, and modern data architecture patterns.
Strong communication, collaboration, and stakeholder management skills with the ability to influence technical decisions across engineering, data, analytics, and product teams.
demonstrated ability to thrive in ambiguous environments, rapidly learn emerging technologies, solve complex problems, and drive innovation in a fast-paced organization.
The knowledge, skills and abilities typically acquired through the completion of a high school diploma and/or GED
Competencies:
Global Perspective
Manages Ambiguity
Nimble Learning
Self-Development
Collaborates
Cultivates Innovation
Situational Adaptability
Communicates Effectively
Drives Results
Interpersonal Savvy