[Hiring] Principal ML Architect @Lutra

Role Description

This is an opportunity to help define what the next generation of machine learning systems looks like. You’ll operate across the entire path from raw data through feature generation, modelling, delivery, deployment, inference, and production monitoring, helping establish architectures that allow sophisticated ML systems to operate reliably at extraordinary scale.

Because the client’s production ML capability is still maturing, you’ll have considerable influence over the architectures, patterns, and technical approaches that underpin its next generation of ML systems.

Key Responsibilities

• ML architecture: Design and evolve end-to-end architectures for large-scale machine learning systems spanning data preparation, feature generation, model delivery, deployment, serving, and production monitoring.
• Data-centric systems: Design solutions for validating, transforming, and generating features from extremely large datasets while ensuring consistently high-quality inputs for downstream model development.
• Model delivery: Architect systems and patterns that allow models to move reliably from development into production environments.
• Deployment & hosting: Develop effective approaches for deploying, hosting, and operating ML models at scale, balancing performance, reliability, maintainability, and infrastructure constraints.
• Production ML: Establish robust approaches for managing and monitoring deployed models and the systems that support them.
• Performance: Identify and pursue opportunities to optimize the performance of ML systems across data processing, model execution, and serving infrastructure.
• Technical design: Lead functional and process design across ML initiatives (including scenario design, flow mapping, prototyping, testing, and defining appropriate support procedures).
• Engineering standards: Bring strong software engineering principles into the design of ML systems so that experimentation can translate into maintainable, production-grade software.
• Cross-functional collaboration: Work across data science, machine learning, software engineering, and other stakeholders to source the right data, establish technical requirements, and define what success actually looks like.
• Technical leadership: Provide architectural direction across complex ML initiatives and influence how the broader organization approaches machine learning as its use of the technology continues to mature.

Qualifications

• You have a Masters or PhD in a STEM field or equivalent depth of experience gained as a Data Scientist, Machine Learning Engineer, ML Architect, or in a closely related role.
• You understand what it takes to move ML beyond experimentation and operate it reliably in production.
• You have extensive knowledge of the infrastructure and pipelines that support production ML systems (aka MLOps).
• You have meaningful experience across several modelling approaches such as classification, regression, pattern recognition, recommendation systems, targeting systems, or ranking systems.
• You bring deep knowledge of mathematics, probability, statistics, and algorithms and can apply that knowledge to practical engineering and product challenges.
• You understand how to design the systems surrounding a model, including data preparation, feature generation, delivery, deployment, monitoring, and operational support.
• You have an interest in performance optimization and can reason about the trade-offs involved in operating ML systems at significant scale.
• You understand software engineering best practices and can work effectively alongside advanced engineering teams building production systems.
• You have strong business acumen and can work cross-functionally to source data, establish requirements, identify meaningful constraints, and define measures of success.
• You’re comfortable operating at “principal level” (aka defining architecture, navigating ambiguity, influencing technical direction, and making decisions whose consequences extend beyond an individual project or team).

It’s a bonus if

• You have a software engineering background in addition to your machine learning expertise.
• You have experience deploying ML models for rapid/real-time inference.
• You’ve published articles, research, or papers related to machine learning, statistics, optimization, or adjacent fields.
• You’ve worked in another environment where enormous data volumes, high transaction throughput, and tight latency constraints materially change how ML systems need to be designed.

Interested in learning more?

Please send your resume or LinkedIn profile URL to [email protected] with “Principal Machine Learning Architect” as the subject line. One of our talent partners will be in contact shortly!