Lead ML Engineer, Recommendation Systems

Launch Potato; Vancouver; Hybrid; CAD 137,000 - 193,000; Full time

Job summary

A technology company is seeking a Machine Learning Engineer to build and optimize recommendation systems driving engagement for millions of users. In this hybrid role, you will design, deploy, and scale ML solutions that deliver over 100 million predictions daily, directly impacting user experience and business growth. The ideal candidate has extensive experience in machine learning and strong technical skills in data processing and algorithms.

Benefits

Career growth opportunities

Remote work flexibility

Inclusive company culture

Qualifications

• 7+ years building and scaling production ML systems with measurable impact.
• Strong background in ranking algorithms and deep learning.
• Experience with SQL and modern data warehouses.

Responsibilities

• Build and deploy ML models serving over 100M predictions daily.
• Enhance data processing pipelines with efficiency improvements.
• Design ranking algorithms for relevance and revenue.

Skills

Building ML systems

Ranking algorithms

Python

SQL

Distributed computing

A/B testing

Tools

TensorFlow

PyTorch

Snowflake

Spark

Job description

As The Discovery and Conversion Company, our mission is to connect consumers with the world’s leading brands through data-driven content and technology.

Headquartered in South Florida with a remote-first team spanning over 15 countries, we’ve built a high-growth, high-performance culture where speed, ownership, and measurable impact drive success.

WHY JOIN US?

At Launch Potato, you’ll accelerate your career by owning outcomes, moving fast, and driving impact with a global team of high-performers.

We convert audience attention into action through data, machine learning, and continuous optimization.

We’re hiring a Machine Learning Engineer (Recommendation Systems) to build the personalization engine behind our portfolio of brands. You’ll design, deploy, and scale ML systems that power real-time recommendations across millions of user journeys. This role gives you the chance to work on systems serving 100M+ predictions daily, directly impacting engagement, retention, and revenue at scale.

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.

We do not discriminate based on race, religion, color, national origin, gender (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics.

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