Fetch
Staff Machine Learning Engineer (Personalization)
Fetch, Chicago, Illinois, United States, 60290
Role Overview:
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. You will design, build, and implement personalization systems that optimize for user engagement, offer relevancy, and customer satisfaction. Your work will directly impact our ability to deliver value to both users and brand partners.
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 Have:
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.
Compensation and Benefits:
At Fetch, we offer competitive compensation packages to the exceptional folks we hire. The base salary range is
$100,000 - $220,000.
We also offer all employees equity in Fetch, so that everyone can benefit from Fetch’s growth.
401k Match:
Dollar-for-dollar match up to 4%.
Benefits for humans and pets:
We offer comprehensive medical, dental and vision plans for everyone including your pets.
Continuing Education:
Fetch provides ten thousand per year in education reimbursement.
Paid Time Off:
On top of our flexible PTO, Fetch observes 9 paid holidays, including Juneteenth and Indigenous People’s Day, as well as our year-end week-long break.
Robust Leave Policies:
20 weeks of paid parental leave for primary caregivers, 14 weeks for secondary caregivers, and a flexible return to work schedule.
Calvin Care Cash:
Employees who are welcoming new family members will also receive a one-time $2,000 incentive to assist employees with covering the cost of childcare, clothing, diapers and much more!
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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. You will design, build, and implement personalization systems that optimize for user engagement, offer relevancy, and customer satisfaction. Your work will directly impact our ability to deliver value to both users and brand partners.
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 Have:
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.
Compensation and Benefits:
At Fetch, we offer competitive compensation packages to the exceptional folks we hire. The base salary range is
$100,000 - $220,000.
We also offer all employees equity in Fetch, so that everyone can benefit from Fetch’s growth.
401k Match:
Dollar-for-dollar match up to 4%.
Benefits for humans and pets:
We offer comprehensive medical, dental and vision plans for everyone including your pets.
Continuing Education:
Fetch provides ten thousand per year in education reimbursement.
Paid Time Off:
On top of our flexible PTO, Fetch observes 9 paid holidays, including Juneteenth and Indigenous People’s Day, as well as our year-end week-long break.
Robust Leave Policies:
20 weeks of paid parental leave for primary caregivers, 14 weeks for secondary caregivers, and a flexible return to work schedule.
Calvin Care Cash:
Employees who are welcoming new family members will also receive a one-time $2,000 incentive to assist employees with covering the cost of childcare, clothing, diapers and much more!
#J-18808-Ljbffr