Offer summary

Qualifications:

Over 6 years of experience in applied machine learning solving real-world problems., Deep expertise in modeling techniques, data workflows, and ML infrastructure., Experience building and deploying low-latency, real-time ML systems in enterprise environments., Proficiency with cloud platforms like AWS and GCP, and familiarity with tools like Spark and XGBoost..

Key responsibilities:

Lead the development and deployment of production ML models that deliver measurable value.

Guide and mentor ML engineers and cross-functional teams through code reviews and modeling walkthroughs.

Debug and optimize ML pipelines, feature stores, and deployment infrastructure in production.

Contribute to architecture design for real-time, micro-cohort analysis and campaign optimization.

Job description

Staff Applied Machine Learning Scientist

You are a seasoned applied machine learning scientist (6+ years solving real-world ML problems) with deeply held perspectives on modeling techniques, data workflows, and the infrastructure that unlocks world-class ML systems. Your opinions are rooted in experience—trial, error, and relentless iteration—and they make you surgically effective at building models that actually move the needle.

While you are deeply knowledgeable about LLMs and agents (and getting tired of explaining AI to your friends) you aren't here to just “talk to the LLM.” You build and deploy models that matter—real-time, low-latency systems powered by massive feature stores that serve predictions in the critical path for the world’s biggest enterprises. You work where modeling meets engineering, and you're driven by one thing: making machine learning deliver actual value in production. You’ve wrangled your share of Spark jobs, tuned XGBoost until your eyeballs hurt, and carried a real-time model from notebook to uptime SLA.

You are a technical leader. You guide ML engineers and cross-functional peers by sharing your insights, offering principled critiques, and demonstrating the courage to make the hard calls. You celebrate your team’s wins as much as your own. You mentor through code reviews and modeling walkthroughs. You thrive in the flow state—those moments where a thorny modeling challenge untangles in your mind, and you can just build.

You don’t believe in ivory towers. When data quality is a mess, you dive in. When your model doesn’t work in production, you debug the pipeline, the feature store, and the service endpoint until it does. You're insatiably curious. New modeling techniques? Let’s test them. Advances in streaming architectures? You're already thinking about integration points. You read papers, try repos, and turn research into production-ready code. You’ve deployed ML to AWS, GCP, or both, and know what it takes to make a model fast, scalable, and maintainable.

We’re not looking for someone to prompt their way to mediocre answers. We’re looking for someone who can model the hard stuff—fast decisions, messy data, and enterprise-scale expectations.

Think you’ve got what we need? Let’s find out.

About GrowthLoop

GrowthLoop is the leading AI Growth Platform, combining the power of the data cloud and AI to transform how enterprises build audience segments, orchestrate cross-channel journeys, and iterate on campaign results. Built for and by marketers, GrowthLoop was the first composable CDP with a flexible, no-code solution that empowers teams to unlock the full potential of their first-party data. Its platform helps companies thrive by breaking down data silos and enabling cross-functional collaboration to unify data, strategy, and teams. With its rapid expansion and dedication to creating an intelligent data-to-action loop, GrowthLoop is leading the charge for a new era of customers.

We apply best-in-class architecture and technology to build a system for marketing teams that is both functional and beautiful.

What You’ll Do

Continuously measure and optimize the effectiveness of marketing campaigns depending on goal and audiences.
Realtime Support: Contribute to and develop an architecture for enabling real-time capabilities in micro-cohort analysis.
Simulate micro-cohorts and individual reactions before executing campaigns.
Optimize creative selection, messaging, and promotions for individual customers.

The estimated salary is between $200,000 to $250,000 CAD or $180,000 to $230,000 USD. The total compensation will also include a meaningful variable component and equity in the company. Final base salary decisions will be based on a variety of non-discriminatory factors unique to each candidate, such as the individual’s skill set, depth of experience, and qualifications.

Benefits

Rewards