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About This Role
Job Description
SUMMARY: The AI Engineer is responsible for end\-to\-end development and deployment of AI\-powered data products based on machine learning over large data sets. The AI Engineer will be responsible for model design, evaluation, and production rollout using standardized Stride coding best practices. The AI Engineer will provide technical guidance to cross\-functional teams and develop an understanding of Stride’s business objectives, using AI to improve student retention and academic outcomes. The AI Engineer is deeply experienced, passionate about solving problems through applied ML techniques, comfortable manipulating and summarizing data, and able to set priorities and facilitate collaborative work on complex projects. They will drive the creation of production 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.
- Applies expertise in applied AI, model, API, and pipeline creation, as well as the presentation of data to see beyond the numbers and understand how our users interact with our core products;
- Provide strategic influence with Product and Engineering teams to solve problems and identify trends and opportunities;
- Collaborates with IT managers, data stewards, and data engineers to ensure that AI workflows are efficiently provisioned with high\-quality data and follow best practices in security, privacy, and scalability;
- Informs, influences, and supports product direction by translating business needs into innovative yet practical AI solutions that drive measurable outcomes;
- Ensures that AI output is measurable, documented, reproducible, and actively monitored for performance drift. Implements practices for continuous improvement and retraining as needed;
- Manages the development of AI resources by gathering requirements, organizing data sources, and supporting the integration of AI models into product throughout the product lifecycle;
- Holds a key thought leadership role on teams responsible for defining and evaluating strategies to improve academic outcomes, leveraging applied AI expertise to inform decisions;
- Ensures that Stride’s AI capabilities support schools and school services teams, facilitating adoption and use of actionable data to deliver improved outcomes;
- Identifies and defines new opportunities to leverage AI across different business units and functions in support of Stride’s mission, vision, and long\-term strategy;
- Demonstrates genuine enthusiasm for education and the Stride experience, inspiring colleagues and stakeholders to harness AI for meaningful impact on student success;
Supervisory Responsibilities: This position has no formal supervisory responsibilities.
Certificates and Licenses: None required.
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
OTHER REQUIRED QUALIFICATIONS:
- Hands\-on experience with modern ML libraries (e.g., Python\-based frameworks such as TensorFlow, PyTorch, scikit\-learn) and understanding of statistical principles.
- Proficiency with AWS, Azure, Docker, Kubernetes, and Terraform to build scalable, secure, and high\-performing environments.
- Ability to design, implement, and maintain robust ML pipelines, including version control, containerization (Docker, Kubernetes), and CI/CD processes.
- Ensures model integrity and reliability, validating outputs to deliver precise and actionable insights.
- 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
DESIRED QUALIFICATIONS:
- Master’s/Doctorate preferred
- Additional experience with MLOps frameworks (e.g., MLflow, Kubeflow) or advanced ML techniques (e.g., NLP, recommender systems, deep learning)
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 $66,379\.50 to $145,000\.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
RegularThe 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.
Salary Context
This $66K-$145K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 2064 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,963 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Stride, Inc., this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $180,000 based on 12,398 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $163,400. This role's midpoint ($105K) sits 41% below the category median. Disclosed range: $66K to $145K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($290,000) and AI Safety ($274,200). By seniority level: Entry: $97,760; Mid: $163,400; Senior: $227,400; Director: $244,800; VP: $250,000.
Stride, Inc. AI Hiring
Stride, Inc. has 2 open AI roles right now. They're hiring across AI/ML Engineer, AI Architect. Based in Remote, US. Compensation range: $145K - $180K.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,883 positions. About 15% of all AI roles offer remote work.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,963 open positions tracked in our dataset. By seniority: 116 entry-level, 1,875 mid-level, 1,532 senior, and 440 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (593 positions). The remaining 3,349 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($290,000 median, 39 roles); AI Safety ($274,200 median, 52 roles); Research Engineer ($260,000 median, 421 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,963 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,783), Data Scientist (297), AI Software Engineer (279). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (116) are outnumbered by mid-level (1,875) and senior (1,532) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 440 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (593 positions), with 3,349 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $290,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,043 postings), Aws (1,241 postings), Azure (934 postings), Rag (886 postings), Gcp (774 postings), Pytorch (614 postings), Prompt Engineering (614 postings), Claude (564 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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