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About This Role
We believe in the power and joy of learning
At Cengage, our employees have a direct impact in helping learners around the world discover the power and joy of learning. We are bonded by our shared purpose – driving innovation that helps millions of learners improve their lives and achieve their dreams through education.
About This Role
Cengage is at an inflection point. As we scale our AI\-powered learning ecosystem including Student Assistant, AI faculty insights, and Cengage Unlimited the gap between a polished platform demonstration and a deeply embedded, outcomes\-driving deployment at an institution is where the real work lives. The Lead Field Development Engineer closes that gap.
As a Lead FDE, you will embed directly with Cengage's most strategic institutional partners to architect, configure, and ship production\-grade AI and platform solutions tailored to their academic, compliance, and pedagogicalenvironments. This is not a sales engineering role: you will write and own production code, influence Cengage's core platform roadmap with field\-derived insights, mentor other engineers, and establish the standard for complex institutional AI deployments.
What You'll Own
STRATEGIC INSTITUTIONAL DEPLOYMENT
- Embed with 3–5 strategic institutional accounts at a time, working directly with partners to understand instructional workflows, legacy LMS architectures, and institutional data environments before proposing a solution
- Lead end\-to\-end delivery of MindTap AI, WebAssign, Cengage Unlimited, and custom GenAI integrations from discovery through production launch and ongoing iteration
- Design and build institution\-specific configurations including adaptive learning paths, RAG\-backed course assistants, and auto\-graded problem banks that address pedagogical challenges at scale
- Drive LTI 1\.3 and LTI Advantage integrations between Cengage platforms and institutional LMS environments such as Canvas, Blackboard, D2L, and Moodle, including SSO, grade passback, and data flows
TECHNICAL ARCHITECTURE \& ENGINEERING
- Write production\-quality code in Python, JavaScript/TypeScript, and SQL to build integration middleware, data pipelines, and custom tooling that extend Cengage's core platforms
- Architect and deploy agentic AI workflows using LLM APIs and retrieval\-augmented generation pipelines grounded in institutional course content
- Build and maintain automated evaluation frameworks that measure the accuracy, safety, and pedagogical quality of AI\-generated student guidance at the institution level
- Ensure deployments meet FERPA, WCAG 2\.1 AA accessibility, institutional data\-governance requirements, and Cengage's AI safety standards
- Translate field\-derived deployment patterns, integration heuristics, and failure modes into first\-class contributions to Cengage's product and engineering roadmap
LEADERSHIP \& ENABLEMENT
- Serve as the technical authority for field deployment practices, establishing standards, reusable integration templates, and a shared knowledge base of institutional patterns
- Mentor junior and mid\-level FDEs and conduct technical reviews of deployment architectures, code, and
stakeholder communication
- Partner closely with Cengage product managers, platform engineers, content teams, Sales, and Customer Success to prioritize roadmap features and define technical success criteria
- Present deployment architecture, outcomes data, and AI safety posture to institutional CIOs, Chief
Academic Officers, and VP\-level stakeholders with authority and clarity
- Define adoption milestones and renewal\-driving outcomes for strategic accounts, ensuring technical delivery translates into measurable institutional value
WHAT YOU'LL BUILD IN YOUR FIRST 12 MONTHS
- A reference deployment architecture for Cengage AI and LTI 1\.3 integration that can serve as the team standard across institutions
- Custom RAG\-powered course\-assistant deployments embedded inside MindTap for strategic university
partners, with measurable engagement and learning\-outcome targets
- An automated AI evaluation harness for Cengage Student Assistant covering accuracy, academic\-
integrity safety, and response quality across FDE\-managed accounts
- A faculty analytics integration layer connecting Student Assistant interaction data to institutional LMS gradebooks and early\-alert systems
- A library of reusable integration modules for Canvas, Blackboard, D2L, and Moodle that reduces institutional onboarding time from weeks to days
What You Bring
TECHNICAL
- 7\+ years of software engineering experience with a track record of shipping production systems in complex, customer\-facing environments
- 3\+ years in a customer\-embedded or field\-facing engineering role such as FDE, Solutions Engineer, Applied AI Engineer, or Implementation Architect, with ownership of full deployments rather than demonstrations along
- Strong full\-stack engineering skills, including Python, JavaScript/TypeScript, REST or GraphQL API design, and modern application frameworks
- Hands\-on experience building and deploying LLM\-based applications in production, including RAG pipelines, prompt engineering, tool\-calling agents, and evaluation frameworks
- Demonstrated experience with LMS integration standards such as LTI 1\.3, LTI Advantage, AGS, NRPS, and Deep Linking
- Proficiency with cloud platforms; AWS is preferred, with experience across services such as Lambda, ECS or EKS, RDS or Aurora, S3, API Gateway, and CloudWatch
- Working knowledge of learning analytics standards such as xAPI or Caliper and educational data\-privacy frameworks including FERPA, COPPA, and applicable state requirements
LEADERSHIP \& COMMUNICATION
- Demonstrated ability to translate ambiguous institutional requirements into a concrete technical plan, own the plan end to end, and remain accountable for outcomes
- Experience presenting technical architecture and AI product strategy to C\-suite and senior academic leadership, with credibility in both engineering and executive settings
- Track record of mentoring engineers and raising the technical bar of a team, not only executing individual work
- Comfort with up to 30% travel to institutional partner sites throughout the academic year
Preferred Qualifications
- Experience in higher education technology, edtech, or academic publishing, including an understanding of how universities procure, adopt, and measure learning technology
- Familiarity with adaptive learning platforms, learning engineering, and learning\-science research
- Experience with enterprise AI governance frameworks, responsible AI evaluation, and AI safety in production deployments
- Contributions to open\-source projects, published technical writing, or conference presentations related to AI deployment, platform engineering, or edtech
- AWS Certified Solutions Architect, Google Cloud Professional Machine Learning Engineer, or an equivalent certification
- Graduate degree in Computer Science, Data Science, Educational Technology, or a related field
Cengage is committed to working with broad talent pools to attract and hire strong and most qualified individuals. Our job applicants are considered regardless of any classification protected by applicable federal, state, provincial or local laws.
Cengage is also committed to providing reasonable accommodations for qualified individuals with disabilities including during our job application process. If you are an applicant with a disability and require reasonable accommodation in our job application process, please contact us at [email protected].
About Cengage
Cengage, a global education technology company serving millions of learners, provides affordable, quality digital products and services that equip students with the skills and competencies needed to be job ready. For more than 100 years, we have enabled the power and joy of learning with trusted, engaging content, and now, integrated digital platforms. We serve the higher education, workforce skills, secondary education, English language teaching and research markets worldwide. Through our scalable technology, including MindTap and Cengage Unlimited, we support all learners who seek to improve their lives and achieve their dreams through education.
Compensation
At Cengage Group, we take great pride in our commitment to providing a comprehensive and rewarding Total Rewards package designed to support and empower our employees. Click here to learn more about our *Total Rewards Philosophy*.
The full base pay range has been provided for this position. Individual base pay will vary based on work schedule, qualifications, experience, internal equity, and geographic location. Sales roles often incorporate a significant incentive compensation program beyond this base pay range.
In this position, you will be eligible to participate in the company’s discretionary incentive bonus program. This position's bonus target amount, which is not guaranteed and is dependent on individual performance and overall company results among other factors, is provided below.
15% Annual: Individual Target
$117,100\.00 \- $187,300\.00 USD
Salary Context
This $117K-$187K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Cengage, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($152K) sits 29% below the category median. Disclosed range: $117K to $187K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Cengage AI Hiring
Cengage has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US. Compensation range: $187K - $187K.
Location Context
AI roles in Austin pay a median of $214,343 across 143 tracked positions.
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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