ZipRecruiter

Log In

2

Full Time Recommender Systems Jobs (NOW HIRING)

Clera

Senior Machine Learning Engineer

San Francisco, CA · On-site

$123K - $169K/yr

Full-time, on-site in San Francisco. What you will do * Design and ship end-to-end ML systems: data ... Hands-on experience with LLMs, fine-tuning, RAG, or large-scale recommender systems * Strong Python ...

Clera

Senior AI/ML Engineer

San Francisco, CA · On-site

$123K - $169K/yr

Full-time, on-site in San Francisco. What you will do * Design and ship end-to-end ML systems: data ... Hands-on experience with LLMs, fine-tuning, RAG, or large-scale recommender systems * Strong Python ...

Clera

Senior AI/ML Engineer

San Francisco, CA · On-site

$123K - $169K/yr

Full-time, on-site in San Francisco. What you will do * Design and ship end-to-end ML systems: data ... Hands-on experience with LLMs, fine-tuning, RAG, or large-scale recommender systems * Strong Python ...

Clera

Senior Machine Learning Engineer

San Francisco, CA · On-site

$123K - $169K/yr

Full-time, on-site in San Francisco. What you will do * Design and ship end-to-end ML systems: data ... Hands-on experience with LLMs, fine-tuning, RAG, or large-scale recommender systems * Strong Python ...

next page

Showing results 1-20

All JobsFull Time Recommender Systems Jobs

Full Time Recommender Systems information

See salary details

$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:
Full Time Recommender Systems jobs near you
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.

Lead Machine Learning Engineer, Recommender Systems

HP IQ

Palo Alto, CA • On-site

$175K - $275K/yr

Full-time

Medical, Dental, Vision, Life, PTO

Posted 27 days ago

Job description

Who We Are
HP IQ is HP's new AI innovation lab. Combining startup agility with HP's global scale, we're building intelligent technologies that redefine how the world works, creates, and collaborates.
We're assembling a diverse, world-class team-engineers, designers, researchers, and product minds-focused on creating an intelligent ecosystem across HP's portfolio. Together, we're developing intuitive, adaptive solutions that spark creativity, boost productivity, and make collaboration seamless.
We create breakthrough solutions that make complex tasks feel effortless, teamwork more natural, and ideas more impactful-always with a human-centric mindset.
By embedding AI advancements into every HP product and service, we're expanding what's possible for individuals, organisations, and the future of work.
Join us as we reinvent work, so people everywhere can do their best work.
About The Role
As a Lead Machine Learning Engineer - Recommender Systems, you'll play a central role in improving HP's Retrieval-Augmented Generation (RAG) pipelines for private and local data. You'll build intelligent, context-aware retrieval systems that enhance user interactions with documents, meetings, and applications-all on-device. This role blends deep ML experience with product-focused engineering.
What You Might Do
  • Drive the design, implementation, and scaling of recommendation and retrieval algorithms for our AI Companion app
  • Set the technical vision for vector search and similarity matching models to identify relevant documents across structured and unstructured data
  • Analyze user interactions and system performance to guide algorithmic improvements
  • Partner with cross-functional leaders in ML, infrastructure, and product teams to deploy fast and efficient RAG workflows
  • Build and maintain retrieval indexes optimized for latency and memory
  • Mentor and guide engineers across the team, fostering best practices in experimentation, model evaluation, and production deployment.
Essential Qualifications
  • 8+ years of software development experience with exposure to ML engineering
  • Deep expertise in recommender systems, embeddings, and ranking models
  • Proven experience building or scaling document search or retrieval systems
  • Strong understanding of vector databases (e.g., FAISS, Pinecone, Qdrant)
  • Proficient in Python and one systems language (e.g., C++, Java)
Preferred Skills
  • Background in LLM integration or fine-tuning for RAG workflows
  • Industry experience at companies like Google (Search, YouTube), Meta (Feed, Ads), or Twitter (Timeline, Trends)
  • Experience with ML pipeline tools (Airflow, Ray, TorchServe)
  • Previous experience improving search relevance, click-through rate, or long-term engagement

Salary Range: $175,000 - $275,000
Compensation & Benefits (Full-Time Employees)
The salary range for this role is listed above. Final salary offered is based upon multiple factors including individual job-related qualifications, education, experience, knowledge and skills.
At HP IQ, we offer a competitive and comprehensive benefits package, including:
  • Health insurance
  • Dental insurance
  • Vision insurance
  • Long term/short term disability insurance
  • Employee assistance program
  • Flexible spending account
  • Life insurance
  • Generous time off policies, including;
    • 4-12 weeks fully paid parental leave based on tenure
    • 11 paid holidays
    • Additional flexible paid vacation and sick leave (US benefits overview)

Why HP IQ?
HP IQ is HP's new AI innovation lab, building the intelligence to empower humanity-reimagining how we work, create, and connect to shape the future of work.
  • Innovative WorkHelp shape the future of intelligent computing and workplace transformation.
  • Autonomy and Agility Work with the speed and focus of a startup, backed by HP's scale.
  • Meaningful Impact Build AI-powered solutions that help people and organisations thrive.
  • Flexible Work Environment Freedom and flexibility to do your best work.
  • Forward-Thinking CultureWe learn fast, stay future-focused, and imagine what comes next-together.

Equal Opportunity Employer (EEO) Statement
HP, Inc. provides equal employment opportunity to all employees and prospective employees, without regard to race, color, religion, sex, national origin, ancestry, citizenship, sexual orientation, age, disability, or status as a protected veteran, marital status, familial status, physical or mental disability, medical condition, pregnancy, genetic predisposition or carrier status, uniformed service status, political affiliation or any other characteristic protected by applicable national, federal, state, and local law(s).
Please be assured that you will not be subject to any adverse treatment if you choose to disclose the information requested. This information is provided voluntarily. The information obtained will be kept in strict confidence.
If you'd like more information about HP's EEO Policy or your EEO rights as an applicant under the law, please click here: Equal Employment Opportunity is the Law Equal Employment Opportunity is the Law - Supplement

Apply