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Full Time Recommender Systems Jobs (NOW HIRING)

Staff Machine Learning Engineer

New York, NY · On-site +1

$179K - $224K/yr

Our group builds conversational AI systems including Ebb, as well as search, recommendation ... The anticipated new hire base salary range for this full-time position is $140,400-$195,000 ...

$104K - $115K/yr

... STATUS: Full-Time; Salaried SECURITY CLEARANCE: MUST have an active Secret security clearance ... Conduct reviews on program portfolio to evaluate and/or recommend alternative plans, improve ...

... STATUS: Full-Time; Salaried SECURITY CLEARANCE: MUST have an active Secret security clearance ... Conduct reviews on program portfolio to evaluate and/or recommend alternative plans, improve ...

$105K - $131K/yr

... STATUS: Full-time; salaried CLEARANCE: Secret Clearance required Astrion has an exciting ... Conduct reviews on program portfolio to evaluate and/or recommend alternative plans, improve ...

$105K - $115K/yr

... STATUS: Full-time; salaried CLEARANCE: Secret Clearance required Astrion has an exciting ... Conduct reviews on program portfolio to evaluate and/or recommend alternative plans, improve ...

$105K - $115K/yr

... STATUS: Full-time; salaried CLEARANCE: Secret Clearance required Astrion has an exciting ... Conduct reviews on program portfolio to evaluate and/or recommend alternative plans, improve ...

Systems Engineer (Senior)

Gordon, GA · On-site

$98K - $134K/yr

Full-Time/Part-Time Full-Time Description RiVidium Inc. is seeking an experienced Senior Systems ... recommend system improvements. Work Environment: * Requires Top Secret/SCI clearance . About the ...

OR · On-site

... recommender systems-across multiple product lines; * Leverages AWS and Azure ecosystems ... Directly supervises 1 - 3 Full-time Equivalent (FTE) regular employees and/or contractors. Carries ...

Have 1-3+ years of full-time experience in an MLE role What We're Looking For: * Strong ML Foundations - Experience with recommender systems, embeddings, foundation models. You understand when to use ...

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Full Time Recommender Systems information

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$46K

$112K

$197K

How much do full time recommender systems jobs pay per year?

As of Jun 16, 2026, the average yearly pay for full time recommender systems in the United States is $111,995.00, according to ZipRecruiter salary data. Most workers in this role earn between $71,500.00 and $136,000.00 per year, depending on experience, location, and employer.

What are Recommender Systems?

Recommender systems are algorithms and software designed to suggest relevant items, such as products, movies, or content, to users based on their preferences and behavior. They are widely used in online platforms like e-commerce sites, streaming services, and social media to help users discover new items that match their interests. These systems use techniques such as collaborative filtering, content-based filtering, and hybrid approaches to analyze data and generate personalized recommendations. Full-time roles in recommender systems typically involve designing, building, and optimizing these algorithms to improve user engagement and satisfaction.

What are the key skills and qualifications needed to thrive as a Full Time Recommender Systems Engineer, and why are they important?

To thrive as a Full Time Recommender Systems Engineer, you need a solid background in computer science, machine learning, and data analysis, usually supported by a relevant degree. Familiarity with tools such as Python, TensorFlow, PyTorch, and large-scale data processing systems like Spark is essential, along with experience implementing collaborative filtering, content-based, or hybrid recommendation algorithms. Strong problem-solving abilities, communication skills, and a collaborative mindset help you effectively translate business needs into technical solutions. These skills ensure the development of accurate, scalable, and user-focused recommendation systems that drive engagement and business value.

What are some common challenges faced by professionals working full-time on recommender systems, and how can they be addressed?

Full-time professionals in recommender systems often face challenges such as handling large-scale data, ensuring recommendation diversity, and mitigating biases in algorithms. Collaborating closely with data engineers, product managers, and UX designers is crucial to refine recommendations and align them with user needs. Staying updated with the latest research and regularly evaluating model performance helps in overcoming these challenges and maintaining system effectiveness. Many teams also use A/B testing and continuous feedback loops to iteratively improve recommendations.

What is the difference between Full Time Recommender Systems vs Data Scientist?

AspectFull Time Recommender SystemsData Scientist
CredentialsDegree in Computer Science, Data Science, or related fields; experience with machine learningDegree in Statistics, Computer Science, or related fields; strong analytical skills
Work EnvironmentTech companies, e-commerce, streaming services focusing on recommendation algorithmsVarious industries including finance, healthcare, marketing, often involving data analysis and modeling
Industry UsagePrimarily in tech-driven sectors developing personalized recommendation systemsAcross multiple sectors analyzing data to inform business decisions

Full Time Recommender Systems specialists focus on developing and optimizing recommendation algorithms within tech companies, while Data Scientists analyze data across industries to support decision-making. Both roles require strong technical skills, but their primary focus and application environments differ.

More about Full Time Recommender Systems jobs
What cities are hiring for Full Time Recommender Systems jobs? Cities with the most Full Time Recommender Systems job openings:
What are the most commonly searched types of Recommender Systems jobs? The most popular types of Recommender Systems jobs are:
What states have the most Full Time Recommender Systems jobs? States with the most job openings for Full Time Recommender Systems jobs include:
What job categories do people searching Full Time Recommender Systems jobs look for? The top searched job categories for Full Time Recommender Systems jobs are:
Infographic showing various Full Time Recommender Systems job openings in the United States as of June 2026, with employment types broken down into 10% As Needed, 60% Part Time, and 30% Contract. Highlights an 95% Physical, 1% Hybrid, and 4% Remote job distribution, with an average salary of $111,995 per year, or $53.8 per hour.
Data Science Manager, Personalization

Data Science Manager, Personalization

CarMax, Inc.

Richmond, VA • On-site

Full-time

Posted 9 days ago


CarMax rating

8.0

Company rating: 8.0 out of 10

Based on 367 frontline employees who took The Breakroom Quiz

26th of 715 rated retailers


Job description

8901 - Corp Office West Crk - 12800 Tuckahoe Creek Parkway, Richmond, Virginia, 23238
CarMax, the way your career should be!
About The Team
The Personalization data science team builds and maintains search algorithms and recommender systems that are at the forefront of creating a modern, engaging, digital shopping experience for our customers. We see millions of customers every week across web and mobile. We deploy custom deep learning embedding models, ranking and segmentation algorithms, and are constantly testing new approaches. Our systems are used in dozens of product use cases across the retail and wholesale businesses, which means we partner with many diverse teams throughout the organization.
About The Role
Vehicle Recommender is our team's marquee data science product - a full-scale, modern software solution built on a custom embedding model. Launched in 2024 to replace our legacy system, it delivers intelligent, real-time, customizable product recommendations across our retail and wholesale businesses. We're looking for a data scientist who wants to own a product, not just build models. In this role, you'll develop deep technical expertise in our recommender system, but you'll spend just as much time working with partners across the business - understanding their needs, scoping use cases, advocating for adoption, and ensuring we deliver real value.
To be clear: this is a data scientist role, not a product manager role. You'll still be hands-on with data, experimentation, and model evaluation. But if you're the kind of DS who lights up in a roadmap discussion, loves translating ML capabilities into business terms, and wants to be the go-to expert that partners come to - this is your role. This role may or may not initially include direct reports, but that can depend on the individual candidate. It's ideal for an experienced data scientist interested in growing toward technical leadership or product-oriented career paths.
What You Will Do - Essential Responsibilities
Product Ownership & Partner Engagement
• Serve as the primary point of contact for Vehicle Recommender across the organization - owning relationships with product managers, business stakeholders, and engineering partners
• Drive adoption by helping partners understand what recommendations can do for them, scoping new use cases, and ensuring successful implementation
• Own the roadmap for Vehicle Recommender in partnership with engineering and DS leadership - prioritizing enhancements, maintenance, and new capabilities
• Translate between technical and business audiences; present to leadership, write strategy docs, and make the case for investment
Data Science & Technical
• Develop deep technical expertise in the Vehicle Recommender system - how the embedding models work, how recommendations are generated and served, and how performance is measured
• Design, execute, and interpret experiments (A/B tests, holdouts, pre/post analyses) to quantify impact and guide decisions
• Partner with data scientists and engineers on feature development, model validation, and system monitoring
• Stay current on recommender system research and best practices; bring informed perspective to technical decisions
Qualifications and Requirements
• 5+ years of experience in a data science role, preferably in e-commerce, marketplace, or a data-rich environment
• Strong development skills and experience with Python for data manipulation, analysis, and model development
• Solid foundation in statistics, including hypothesis testing, confidence intervals, and experimental design - you should be comfortable explaining why a test result is or isn't significant
• Experience working with large datasets using tools like Spark, Databricks, or similar
• Clear communication skills - you can explain a complex analysis to a PM or executive without losing them, and present a compelling argument for how and why a team will benefit from using recommendations
• Genuine enthusiasm for how search and recommendation systems work and passion for continued learning
• Bachelor's degree in a quantitative field (statistics, economics, math, engineering, or similar)
• Advanced degree (Master's/Ph.D.) is preferred
Work Authorization: Applicants must be currently authorized to work in the United States on a full-time basis. Sponsorship will not be considered for this specific role.
About CarMax
CarMax disrupted the auto industry by delivering the honest, transparent and high-integrity experience customers want and deserve. This innovative thinking around the way cars are bought and sold has helped us become the nation's largest retailer of used cars, with over 200 locations nationwide.
Our amazing team of more than 25,000 associates work together to deliver iconic customer experiences. Along the way, we help every associate grow their career and achieve their best, at work and in their community. We are recognized for our commitment to training and diversity and are one of the FORTUNE 100 Best Companies to Work For®.
Our Commitment to Diversity and Inclusion:
CarMax is committed to bringing together people from different backgrounds and perspectives, providing employees with a safe, welcoming, and inclusive work environment.
CarMax is an equal opportunity employer, and all qualified candidates will receive consideration for employment without regard to age, race, color, religion, sex, sexual orientation, gender identity, gender expression, genetic information, national origin, protected veteran status, disability status, and any other characteristics protected by law.
Upon an applicant's request, CarMax will consider reasonable accommodation to complete the CarMax Job Application.
Upon an applicant's request, CarMax will consider reasonable accommodation to complete the CarMax Job Application.

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