Staff Machine Learning Engineer (Personalization)

$100,000 - $220,000/Yr

Fetch Agency - Chicago, IL

Full-time - Senior

Chicago, IL

Apparel Manufacturing

About the position

As a Staff Machine Learning Engineer specializing in personalization, you will play a crucial role in developing models and algorithms that tailor the user experience by recommending the most relevant offers. Your primary responsibility will be to design, build, and implement personalization systems that optimize for user engagement, offer relevancy, and customer satisfaction. This position is pivotal as your work will directly impact our ability to deliver value to both users and brand partners, ensuring that our offerings are not only relevant but also enhance the overall user experience. In this role, you will develop scalable machine learning models and systems to personalize the offer experience for millions of users. You will leverage data from user behavior, preferences, and transaction history to drive personalized recommendations. Collaboration will be key, as you will work closely with cross-functional teams including product, engineering, data science, and marketing to define and refine personalization strategies. You will also employ A/B testing and other evaluation techniques to continuously improve personalization models, ensuring that they meet the evolving needs of our users. Your algorithms will be designed to optimize for user satisfaction, engagement, and long-term loyalty, making your contributions essential to the success of our personalization initiatives. Additionally, you will have the opportunity to mentor junior engineers, sharing your expertise and helping to shape Fetch's machine learning best practices, thereby fostering a culture of learning and innovation within the team.

Responsibilities

• Develop scalable machine learning models and systems to personalize the offer experience for millions of users.
• Leverage data from user behavior, preferences, and transaction history to drive personalized recommendations.
• Collaborate with cross-functional teams including product, engineering, data science, and marketing to define personalization strategies.
• Use A/B testing and other evaluation techniques to continuously improve personalization models.
• Implement algorithms that optimize for user satisfaction, engagement, and long-term loyalty.
• Mentor junior engineers and contribute to shaping Fetch's machine learning best practices.

Requirements

• 7+ years of experience in machine learning with a focus on personalization, recommendation systems, or similar fields.
• Strong expertise in machine learning algorithms, deep learning, and large-scale recommendation systems.
• Proficiency in Python, TensorFlow, PyTorch, or other ML frameworks.
• Experience with large datasets, data pipelines, and deploying ML models to production environments.
• Familiarity with A/B testing, experimentation, and optimization techniques.
• Excellent communication skills and the ability to translate technical concepts into business impact.

Nice-to-haves

• Experience working in the loyalty, retail, or consumer goods space.
• Knowledge of reinforcement learning and multi-armed bandits for personalized recommendation.
• Prior experience working in high-growth tech environments.
• Experience with Feature Stores and other data infrastructure for personalization.

Benefits

• Competitive compensation packages with a base salary range of $100,000 - $220,000.
• Equity in Fetch for all employees to benefit from the company's growth.
• 401k Match: Dollar-for-dollar match up to 4%.
• Comprehensive medical, dental, and vision plans for employees and their pets.
• $10,000 per year in education reimbursement for continuing education.
• Participation in employee-led Resource Groups focused on diversity and inclusion.
• Flexible Paid Time Off (PTO) plus 9 paid holidays, including Juneteenth and Indigenous People's Day.
• 20 weeks of paid parental leave for primary caregivers and 14 weeks for secondary caregivers.
• $2,000 Calvin Care Cash incentive for employees welcoming new family members.