2

Full Time Recommender Systems Jobs (NOW HIRING)

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 ...

... 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 ...

... 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 ...

$125K - $135K/yr

JOB STATUS: Full-time; salaried CLEARANCE: Secret Clearance Travel: Limited, as needed Astrion has ... Conduct reviews on program portfolio to evaluate and/or recommend alternative plans, improve ...

... recommend corrective actions to improve performance and efficiency. • Administer enterprise ... onsite full-time at the client's location in Dayton, OH. • Must be able to work in a network ...

Produce technical reports on storage management practices and emerging technologies; recommend ... Must be able to work onsite full-time at the client's location in Dayton, OH. Work Environment and ...

Produce technical reports on storage management practices and emerging technologies; recommend ... Must be able to work onsite full-time at the client's location in Dayton, OH. Work Environment and ...

next page

Showing results 1-20

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:
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.
AI Architect

Full-time

Medical, Retirement, PTO

This job post has expired 1 day ago. Applications are no longer accepted.


Job description

Job Description

SUMMARY: The AI Architect is responsible for designing and delivering production-ready AI solutions across the organization. This individual will manage a small team of AI Engineers and occasionally flex roles from UX, front-end, and subject matter experts to create innovative solutions that leverage machine learning for enterprise-wide impact. The AI Architect will provide direct oversight of AI solution design, infrastructure, and deployment using standardized coding best practices. This position will mentor team members in technical development and ensure alignment with business objectives in the K12 and broader enterprise context. The AI Architect is deeply experienced, passionate about applied AI, comfortable building end-to-end ML pipelines in cloud environments, and able to set priorities and organize the collaborative work of a small technical team. They will drive the timely delivery of AI products directly linked to actionable business decisions.

ESSENTIAL FUNCTIONS: Reasonable accommodations may be made to enable individuals with disabilities to perform the essential duties.

  • Drives the conception, prototyping, and deployment of machine learning models-particularly in natural language processing (NLP) and recommender systems-across multiple product lines;

  • Leverages AWS and Azure ecosystems, containerization, and infrastructure-as-code tools to create and maintain production-ready AI systems that can scale efficiently;

  • Working from proof-of-concept through full productization, including data ingestion, model training, deployment, monitoring for performance drift, and iteration for continuous improvement;

  • Informs, influences, and supports product decisions.

  • Directly supervises 1-3 AI Engineers and occasional flex contributors on specific projects. Provides guidance on coding best practices, model development, and data governance;

  • Works closely with Product, Engineering, IT Managers, Data Stewards, and high-level executives to align AI initiatives with organizational objectives. Ensures that AI solutions adhere to governance and compliance requirements, including student data privacy;

  • Provides critical input and execution support, identifying practical opportunities for applied AI to drive business outcomes;

  • Ensures that AI deliverables are well-documented, reproducible, and monitored for ongoing performance and data integrity. Partners with governance teams and legal to adapt to evolving data privacy regulations and responsible AI practices;

  • Stays abreast of emerging trends and tools-such as large language models-and evaluates their applicability. Advocates for robust DevOps/MLOps, agile methodologies, and cross-functional collaboration to maximize the impact of AI deployments;

  • Aligns K12's AI capabilities with the needs of our schools & school services teams that will drive the adoption and use of actionable data to deliver improved outcomes;

  • Identifies and defines strategic opportunities for leveraging data science across other businesses and functions in support of K12's mission, vision and long-term strategy;

  • Demonstrates a passion for education and the K12 experience, actively motivating and encouraging the same passion in employees.

Supervisory Responsibilities: Directly supervises 1 - 3 Full-time Equivalent (FTE) regular employees and/or contractors. Carries out supervisory responsibilities in accordance with the organization's policies and applicable laws. Responsibilities include interviewing, hiring, and training employees; planning, assigning, and directing work; appraising performance; rewarding and disciplining employees; addressing complaints and resolving problems.

MINIMUM REQUIRED QUALIFICATIONS:

  • Bachelor's degree in Computer Science, Math, Physics, Engineering, or related quantitative field AND

  • Six (6) years' related experience; OR

  • Equivalent combination of education and experience

Certificates and Licenses: None required. Certifications such as AWS Solutions Architect or Azure Solutions Architect are a plus but not mandatory.

OTHER REQUIRED QUALIFICATIONS:

  • Experience designing and deploying machine learning models-especially NLP and recommender systems-using Python and common ML libraries (e.g., PyTorch, TensorFlow, scikit-learn).

  • Proficiency with AWS, Azure, Docker, Kubernetes, and Terraform to build scalable, secure, and high-performing environments.

  • High attention to detail and high level of accuracy.

  • Strong analytical skills.

  • Strong customer service orientation.

  • Professional integrity necessary to maintain confidentiality.

  • Strong planning skills and ability to manage multiple projects simultaneously.

  • Excellent verbal and written communication skills.

  • Ability to complete assigned duties within critical deadlines.

  • Ability to work independently and in a team environment.

  • Proficient with Microsoft Office (Word, Excel, PowerPoint, and Outlook).

  • Proficiency with modern reporting platforms and tools.

  • Ability to grasp complex platform concepts and business models, in a digital context.

  • Ability to clear required background check

PREFERRED QUALIFICATIONS:

  • Experience with MLOps frameworks such as MLflow or Kubeflow.

  • Familiarity with K12 education data or prior EdTech experience.

  • Experience working in an environment that emphasizes responsible AI, ethical AI, or complex governance practices.

WORK ENVIRONMENT: The work environment characteristics described here are representative of those an employee encounters while performing the essential functions of this job. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.

  • This is a home-based position

Compensation & Benefits:Stride, Inc. considers a person's education, experience, and qualifications, as well as the position's work location, expected quality and quantity of work, required travel (if any), external market and internal value when determining a new employee's salary level. Salaries will differ based on these factors, the position's level and expected contribution, and the employee's benefits elections. Offers will typically be in the bottom half of the range.

We anticipate the salary range to be $113,073.75to $180,000.00. Eligible employees may receive a bonus. This salary is not guaranteed, as an individual's compensation can vary based on several factors. These factors include, but are not limited to, geographic location, experience, training, education, and local market conditions.Stride offers a robust benefits package for eligible employees that can include health benefits, retirement contributions, and paid time off.

Job Type

Regular

The above job is not intended to be an all-inclusive list of duties and standards of the position. Incumbents will follow any other instructions, and perform any other related duties, as assigned by their supervisor. All employment is "at-will" as governed by the law of the state where the employee works. It is further understood that the "at-will" nature of employment is one aspect of employment that cannot be changed except in writing and signed by an authorized officer.

If you are a job seeker with a disability and require a reasonable accommodation to apply for one of our jobs, you can request the appropriate accommodation by contacting stridecareers@k12.com.

Equal Opportunity Employer/Protected Veterans/Individuals with Disabilities

Stride, Inc. is an equal opportunity employer. Applicants receive consideration for employment based on merit without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability or protected veteran status, or any other basis prohibited by federal, state, or local law. Stride, Inc. complies with all legally required affirmative action obligations. Applicants will not be discriminated against because they have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant.