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Job Title: Senior Engineering Manager, AI
FLSA status: Regular Full\-Time Remote (US\-Based)
Reports to: Head of Engineering
About TrueLearn
TrueLearn is an educational software company headquartered in Charlotte, NC, dedicated to empowering medical institutions and individual learners to maximize performance on high\-stakes licensure exams. Through a suite of innovative, learning science–driven tools, TrueLearn supports clinicians at every stage of their careers — from board preparation through continuing education.
To date, TrueLearn has prepared over 70,000 healthcare professionals in their pursuit of board certification and works closely with hundreds of medical and health science institutions and residency programs throughout the United States. The company is rapidly growing, continuously innovating its technology platform and expanding its portfolio to meet the evolving needs of the healthcare community.
At the heart of everything TrueLearn does is a simple but powerful truth: the work touches the people who touch patients. Every line of code, every model, and every experiment has the potential to ripple outward into better care, better outcomes, and better lives. That mission is what drives the company to remain data\-driven and customer\-focused in all that it does.
TrueLearn's culture is built around authenticity, empowerment, and a relentless commitment to advocating for learners. Strong core values guide decisions, priorities, and tradeoffs as the company scales — ensuring that growth never comes at the cost of alignment, integrity, or impact on healthcare professionals and patient care.
Position Summary
As an AI Engineering Manager, you will own the health and output of a team building AI\-powered product solutions at the intersection of learning science and clinical education. You will split your time across three areas:
- People and culture. You will manage, mentor, and grow a team of engineers. You will run 1:1s, support career development, give and solicit candid feedback, and shape hiring. You will be the kind of manager people want to work for — one who builds psychological safety, runs blameless retrospectives, drives buy\-in rather than compliance, and treats every teammate as a full human being first.
- Process and delivery. You will shape how the team plans, ships, and learns. You will own agile rituals that actually serve the work (not the other way around), help the team prioritize the highest\-impact problems, and create the conditions for fast, sustainable delivery.
- Technical contribution. You will stay hands\-on. You will write code, review PRs, weigh in on architecture, prototype with AI tools, and bring opinions about how production AI systems should be built. You should be someone the team trusts technically because you've earned it.
Key Responsibilities
- Lead a team of engineers building AI\-powered features and platforms that serve medical learners and educators.
- Build a team culture defined by trust, candor, psychological safety, and shared ownership — where people feel safe taking risks, naming problems, and disagreeing well.
- Design and evolve agile practices that fit the team and the work, removing ceremony that doesn't earn its keep.
- Contribute directly to the codebase: prototyping, building, reviewing, and helping the team solve hard technical problems.
- Establish and champion best practices for building production\-grade AI systems: evaluation, observability, cost and latency management, retrieval architectures, agent design, model selection, prompt and context engineering, guardrails, and the operational discipline that turns demos into dependable products.
- Use AI development tools fluently in your own work and help the team raise its leverage with them.
- Partner with product, design, and learning science content creators to translate ambiguous problems into shipped, measurable outcomes.
- Hire thoughtfully and grow careers intentionally.
Requirements
- 5\+ years in engineering leadership, with 2\+ years managing teams that shipped AI\-powered products to real market success — products with genuine adoption, engagement, and revenue, not pilots that quietly wound down. You've led in high\-trust, high\-autonomy environments and measure success by your team's growth and your customers' outcomes, not your own visibility. (Help Others Succeed.)
- Hands\-on experience designing and operating in probabilistic systems environments — confident when outputs are non\-deterministic, requiring evaluation, observability, cost and latency management, retrieval, agents, model selection, prompt and context engineering, human\-in\-the\-loop feedback, and guardrails. You’ve shipped systems that survived contact with real users at scale, and you can speak to what broke and what you'd do differently. (Own the Outcome.)
- A track record of evangelizing AI\-native ways of working — driving adoption of AI\-assisted development tools, establishing evaluation and experimentation practices, and shifting how teams design, build, ship, and fine\-tune. You've changed how a team works, not just how you work. You bring data, evaluation, and learning science into how you build and decide. (Lead with Evidence.)
- Hands\-on experience with our core stack: React, Angular, Node.js, and ASP.NET.
- Demonstrated experience establishing repeatable engineering patterns for AI systems at scale — the architectural decisions and operational practices that turn prototypes into reliable products. You direct your team toward the highest\-leverage problems rather than the loudest ones. (Make It Count.)
- Fluent daily use of modern AI development tools, with informed opinions about where they help, where they don't, and how to integrate them into a team's workflow. You stay current with a field that changes month over month. (Always Learning.)
- Strong partnership across product, data, and AI disciplines — ability to align engineering execution with AI product strategy, data infrastructure, and domain intelligence, including collaboration on areas such as knowledge graphs, personalization systems, adaptive learning, behavioral analytics, and proprietary data advantages. Your strong written and verbal communication keeps the team aligned and effective across functions.
- A management style rooted in trust rather than authority. You build buy\-in, listen well, make it safe to be wrong out loud, and treat humility as a strength.
- Genuine interest in the mission. You don't need a healthcare background, but you should care about the fact that this work matters.
Why This Role
This is a chance to do engineering leadership work at the frontier of AI, inside a company with a genuinely meaningful mission. You'll help build the systems that help medical professionals learn — and ultimately, help patients get better care. You'll do it with a team that wants to work the way good teams should work: with trust, with rigor, with humor, and with each other.
If that sounds like the kind of work you want to do, we'd love to talk.
Equal Employment Opportunity
TrueLearn is an Equal Opportunity Employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, or any other characteristic protected by applicable law.
Job Type: Full\-time
Pay: $200,000\.00 \- $225,000\.00 per year
Benefits:
- 401(k)
- 401(k) matching
- Dental insurance
- Employee assistance program
- Flexible spending account
- Health insurance
- Health savings account
- Life insurance
- Paid time off
- Parental leave
- Retirement plan
- Vision insurance
Application Question(s):
- How many years of experience do you have leading teams that shipped AI\-powered products to production?
- How many years have you spent in engineering management roles with direct reports?
- While this role is fully remote within the United States, are you comfortable working eastern/central time zone hours to maximize collaboration overlap with the team? (Yes/No)
Location:
- United States (Required)
Work Location: Remote
Salary Context
This $200K-$225K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At TrueLearn, 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. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($212K) sits 17% above the category median. Disclosed range: $200K to $225K.
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.
TrueLearn AI Hiring
TrueLearn has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US. Compensation range: $225K - $225K.
Location Context
AI roles in Austin pay a median of $215,300 across 523 tracked positions. That's 8% above the national median.
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.
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