Lead ML Engineer, Recommendation Systems

Launch Potato; New Brunswick; Hybrid; CAD 100,000 - 130,000; Full time

Job summary

A leading tech company in New Brunswick is seeking an experienced ML Engineer to enhance data-driven personalization. In this role, you will build scalable ML systems, develop innovative ranking algorithms, and collaborate closely with product and engineering teams to drive business growth through data insights. Candidates should have extensive experience in ML frameworks like TensorFlow or PyTorch and a strong background in data analytics.

Qualifications

• 7+ years building and scaling production ML systems with measurable business impact.
• Strong background in ranking algorithms and ML architecture.

Responsibilities

• Drive business growth by optimizing recommendation systems.
• Build and deploy ML models serving 100M+ predictions per day.

Skills

Large-scale ML systems

Ranking algorithms

Python and ML frameworks

SQL and data warehouses

Distributed computing

A/B testing platforms

Tools

TensorFlow

PyTorch

Spark

MLflow

Job description

MUST HAVE

You’ve shipped large-scale ML systems into production that power personalization at scale. You’re fluent in ranking algorithms and know how to turn data into engagement and conversions. Specifically:

• 7+ years building and scaling production ML systems with measurable business impact
• Strong background in ranking algorithms (collaborative filtering, learning-to-rank, deep learning)
• Proficiency with Python and ML frameworks (TensorFlow or PyTorch)
• Skilled with SQL and modern data warehouses (Snowflake, BigQuery, Redshift) plus data lakes
• Familiarity with distributed computing (Spark, Ray) and LLM/AI Agent frameworks
• Track record of improving business KPIs via ML-powered personalization
• Experience with A/B testing platforms and experiment logging best practices

YOUR ROLE

Your mission: Drive business growth by building and optimizing the recommendation systems that personalize experience for millions of users daily. You’ll own the modeling, feature engineering, data pipelines, and experimentation that make personalization smarter, faster, and more impactful.

OUTCOMES

• Build and deploy ML models serving 100M+ predictions per day to personalize user experiences at scale
• Enhance data processing pipelines (Spark, Beam, Dask) with efficiency and reliability improvements
• Design ranking algorithms that balance relevance, diversity, and revenue
• Deliver real-time personalization with latency <50ms across key product surfaces
• Run statistically rigorous A/B tests to measure true business impact
• Optimize for latency, throughput, and cost efficiency in production
• Partner with product, engineering, and analytics to launch high-impact personalization features
• Implement monitoring systems and maintain clear ownership for model reliability

COMPETENCIES

• Technical Mastery: You know ML architecture, deployment, and tradeoffs inside out
• Experimentation Infrastructure: You set up systems for rapid testing and retraining (MLflow, W&B)
• Impact-Driven: You design models that move revenue, retention, or engagement
• Collaborative: You thrive working with engineers, PMs, and analysts to scope features
• Analytical Thinking: You break down data trends and design rigorous test methodologies
• Ownership Mentality: You own your models post-deployment and continuously improve them
• Execution-Oriented: You deliver production-grade systems quickly without sacrificing rigor
• Curious & Innovative: You stay on top of ML advances and apply them to personalization

Want to accelerate your career? Apply now!

Since day one, we've been committed to having a diverse, inclusive team and culture. We are proud to be an Equal Employment Opportunity company. We value diversity, equity, and inclusion.