Interested in this AI/ML Engineer role at Risepoint?
Apply Now →About This Role
Risepoint is an education technology company that provides world\-class support and trusted expertise to more than 100 universities and colleges. We primarily work with regional universities, helping them develop and grow their high\-ROI, workforce\-focused online degree programs in critical areas such as nursing, teaching, business, and public service. Risepoint is dedicated to increasing access to affordable education so that more students, especially working adults, can improve their careers and meet employer and community needs.
The Impact You Will Make
Risepoint is poised to transform the organization using technology and an AI\-first mindset. We are seeking a strategic and highly motivated Sr Director of Digital Product \& AI Strategy to lead the discovery, definition, and development of technology solutions that enhance the end\-to\-end lifecycle of online university programs. Own AI/digital use case development, work with a cross\-functional team to drive build vs buy decisions, and identify solutions with a rigorous, ROI first lens.
In this role, you will uncover friction points universities face—from student discovery and application to enrollment, persistence, and graduation—and turn those insights into scalable digital products that enhance outcomes for students and institutional partners.
The ideal candidate is an experienced product leader who combines strong product vision, analytical thinking, and exceptional execution skills. They have deep knowledge of university systems and processes and can confidently engage with senior leaders at partner institutions. This person thrives in an environment with limited oversight, demonstrates outstanding communication and executive presence, and can independently drive complex, multi\-stakeholder product initiatives from concept through delivery.
The ideal candidate is an experienced product leader who combines deep product management expertise with strong knowledge of higher education systems, student lifecycle processes, and enterprise technology solutions. This role would be strategically focused and would not have any direct reports during the initial year.
How You Will Bring Our Mission to Life
What You Will Do
University Lifecycle Opportunity Identification \& Product Strategy
- Conduct deep discovery to identify pain points, inefficiencies, and unmet needs across the online program lifecycle, including marketing, recruitment, admissions, onboarding, student support, and academic progression.
- Analyze partner processes, systems, personas, and workflows to uncover opportunities for innovation and digital transformation.
AI Strategy \& Use Case Development
- Work with cross\-functional owners to own the tech transformation roadmap (AI/Digital): intake, prioritization, and sequencing of a focused set of high\-impact initiatives across functions
- Identify and prioritize a small number of high\-value opportunities tied to measurable outcomes (conversion, cost\-to\-serve, speed, quality)
- Evaluate build vs. buy decisions and assess AI/digital tools, vendors, and solution approaches with a rigorous, ROI\-first lens
- Develop a measurement framework to baseline performance before AI/digital interventions and track impact post\-deployment
Workflow Transformation
- Redesign workflows end\-to\-end using a problem\-led, future\-back approach (not just layering AI or digital tools onto existing processes)
- Define how roles, tasks, and decision\-making shift with AI embedded
Partner with functions to move from insight pilot* scaled workflow changes
- Ensure solutions are durable, repeatable, and embedded into day\-to\-day operations
Product Strategy \& Roadmapping
- Develop and maintain a clear product vision, strategy, and multi\-quarter roadmap that aligns with business goals, partner needs, and the student experience.
- Define success metrics and use experiments, insights, and analytics to drive continuous improvement.
- Translate university painpoints into solutions.
Stakeholder Engagement \& Cross\-Functional Leadership
- Build strong relationships with university partners to understand their operational models, technical ecosystems, decision structures, and growth goals.
- Collaborate with partnerships, engineering, operations, marketing, and student success teams to shape product requirements and deliver scalable solutions.
Execution \& Delivery
- Own the end\-to\-end product development lifecycle—from discovery and prioritization through launch and iteration.
- Ensure proper documentation, communication, and change management to support adoption across partners and internal teams.
- Lead requirement definition, including user stories, acceptance criteria, systems integration needs, and user flows.
- Work closely with technical teams to ensure product delivery meets scope, timeline, quality, and performance expectations.
Leadership \& Influence
- Operate autonomously with minimal oversight; proactively identify issues and drive solutions.
- Influence decisions across the organization using data\-driven reasoning, storytelling, and a strong executive presence.
What Success Looks Like
- Identify and deliver high\-impact digital and AI\-enabled solutions to reduce friction points in the partner journey that improve operational efficiency, enhance the university partner and student experience, and drive measurable business outcomes.
- Build and execute a clear product and AI strategy roadmap that aligns cross\-functional stakeholders, prioritizes the highest\-value opportunities, and successfully moves initiatives from discovery through implementation and adoption.
- Redesign workflows and business processes in a scalable, sustainable way by embedding technology and AI into day\-to\-day operations, resulting in improved speed, quality, decision\-making, and organizational effectiveness.
How Impact Will be Measured
- Develop and gain alignment on a multi\-quarter digital product and enterprise AI roadmap that identifies and prioritizes high\-impact opportunities across the university partner and student lifecycle, with clear business outcomes and success metrics defined.
- Lead discovery, design, and implementation of a focused set of digital and AI\-enabled workflow transformation initiatives that improve operational efficiency, user experience, speed, quality, or cost\-to\-serve across key business functions.
- Establish strong cross\-functional partnerships and scalable product management practices by creating repeatable processes for intake, prioritization, measurement, stakeholder alignment, and delivery of enterprise digital and AI initiatives.
What You’ll Bring to the Team
Experience That Matters Most
- 8\+ years of tech product management experience, with significant time spent in digital product leadership roles. *(Must have)*
- Strong knowledge of university systems, processes, and online program operations (e.g., CRM, SIS, LMS, admissions workflows, student support models, marketing funnels). Either via experience with EdTech firms or with universities. *(Must have)*
- Proven experience (within the last 2–4 years) driving AI\-led product development and workflow transformation inside an organization, not just deploying tools or running pilots. You can point to workflows that changed and outcomes achieved, not just pilots that launched. *(Must have)*
- Demonstrated ability to thrive in a fast\-paced, evolving environment with a builder mentality, strong ownership mindset, and growth\-oriented approach to problem solving. Comfortable operating with ambiguity, establishing new processes and capabilities, and driving organizational transformation through innovation, adaptability, and continuous improvement.
Experience That’s Great to Have
- Prior consulting experience with top\-tier consulting firms will be a plus
- Proven track record of owning product strategy, roadmaps, and cross\-functional execution for complex, multi\-stakeholder systems.
- Exceptional communication, stakeholder management, and executive\-level presentation skills.
- Ability to independently lead discovery, build requirements, and translate business needs into technical solutions.
- Strong analytical mindset; able to structure ambiguous problems and make data\-driven decisions.
- Preferred \- Experience working with or for higher education institutions
- Preferred \- Familiarity with integrations, APIs, middleware, and data pipelines in university ecosystems.
*Risepoint is an equal\-opportunity employer and supports a diverse and inclusive workforce.*
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Risepoint, 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 in Demand for This Role
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 $181,170 based on 12,692 positions with disclosed compensation. Director-level AI roles across all categories have a median of $247,800.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Risepoint AI Hiring
Risepoint has 2 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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