Lutra

Principal ML Architect, Open Internet

Posted 3 Days Ago

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Hiring Remotely in Canada

Remote

280K-350K Annually

Entry level

Artificial Intelligence • Fintech • Professional Services • Software

The Role

Design and lead end-to-end architectures for production machine-learning systems at extraordinary scale. Responsibilities span data preparation, feature generation, model delivery, deployment, hosting, inference, monitoring, performance optimization, and technical design. The role establishes engineering standards, influences ML architecture, and collaborates across data science and software engineering to build reliable, maintainable systems for high-volume, low-latency workloads.

Summary Generated by Built In

Your opportunity

Our client builds a high throughput system powering hundreds of billions of daily transactions, each completed within milliseconds, across globally distributed infrastructure designed for reliability and efficiency. They have been quietly bootstrapping and growing in line with revenue for over 2 decades. They maintain independence from external stakeholders, enabling them to chart their course, maintain a long-term perspective and build an enduring, sustainable business that currently employs ~600 team members globally.

Data is central to nearly every part of their platform. High-volume event and transactional data powers customer reporting, billing, marketplace analytics, experimentation, machine-learning workflows, and product experiences. The organization is evolving from centralized, batch-oriented reporting toward a platform-driven architecture combining batch, streaming, and asynchronous processing, with APIs becoming a primary interface to data.

Our client has been stubbornly racking and stacking infrastructure around the world for the duration of their existence, a habit that allows for them to price themselves at the cost of electricity while their competitors are mired in rising cloud infrastructure costs. This long-term, somewhat contrarian, thinking puts them in a position to offer stability and career longevity evidenced by robust benefits that include RRSP matching.

Machine learning is already in production and they have successfully shipped their first generation of ML-powered systems. They’re now moving into the next generation, at a point where growing transaction and data volumes leave considerable room for ML to influence the economics, efficiency and intelligence of the platform.

This is an opportunity to help define what that next generation 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. Rather than inheriting a completely settled way of doing things, you’ll help determine what those systems should look like as the organization continues to expand its use of machine learning.

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

Your know-how

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!

Compensation

The base pay range for this role is CA$280,000 – CA$350,000 per year.

Skills Required

Master’s or PhD in a STEM field, or equivalent depth of experience as a Data Scientist, Machine Learning Engineer, ML Architect, or closely related professional
Experience moving machine learning systems beyond experimentation and operating them reliably in production
Extensive knowledge of infrastructure and pipelines supporting production ML systems, including MLOps
Meaningful experience with multiple modeling approaches, such as classification, regression, pattern recognition, recommendation, targeting, or ranking systems
Deep knowledge of mathematics, probability, statistics, and algorithms applied to engineering and product challenges
Experience designing systems around models, including data preparation, feature generation, delivery, deployment, monitoring, and operational support
Interest in performance optimization and ability to evaluate trade-offs in operating ML systems at scale
Understanding of software engineering best practices and ability to work with advanced engineering teams
Strong business acumen and ability to work cross-functionally to source data, establish requirements, identify constraints, and define success measures
Ability to operate at principal level by defining architecture, navigating ambiguity, influencing technical direction, and making decisions with broad impact
Software engineering background in addition to machine learning expertise
Experience deploying ML models for rapid or real-time inference
Published articles, research, or papers related to machine learning, statistics, optimization, or adjacent fields
Experience designing ML systems for enormous data volumes, high transaction throughput, and tight latency constraints