Interested in this AI/ML Engineer role at CodePath.org?
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About This Role
CodePath is the largest educator of college computer science students in the country. We've trained over 40,000 students from 1,000\+ universities, and our partners include Amazon, Google, and other leading technology companies. We've spent nearly a decade training the next generation of technical talent, and we just launched a $150M initiative with Anthropic, building one of the most ambitious AI workforce programs in the world.
We're now expanding into new markets and scaling our team so we can move at the speed AI is transforming the workforce. People joining CodePath now will have the opportunity to help architect the next frontier of our work.
We believe technical skill is the fastest way to turn raw talent into real opportunity. We train the engineers the AI era runs on, building toward millions of learners, hundreds of millions in revenue, and billions in economic impact. If you want to own something and be part of a 0\-to\-1 journey at an organization moving at the speed of AI, this is the place to build it.
About the Role
Location: Remote, United States
Role\-Type: Full\-Time, W2 Employee
Reports To: Senior Director, AI Programs
Compensation: $130,000 to $148,000 per year
Start Date: September 21st, ahead of the October 19th cohort launch
Together with Anthropic, CodePath launched Claude Corps in June 2026, a national fellowship program with a $150 million commitment to place 1,000 AI\-trained fellows at nonprofits across America. The mentorship model works, but we need experienced professionals to coach and guide those mentors, as well as be responsible for the success of our mentor’s performance and our curriculum delivered to the Fellows. A CodePath Mentor coaches about 20 fellows, so a thousand fellows means more than forty mentors, and someone has to own whether all of them actually deliver. That someone is the Senior Manager of AI Practice.
You manage a team of mentors and you own the delivery outcome for their fellows: at full scale, about ten mentors and 200 fellows, fewer while the program ramps. You are accountable for whether fellows are actually learning, pod by pod and mentor by mentor. When a mentor's pods stall, when a fellow's work drifts off track, when the same problem shows up across three pods at once, it is yours to catch and yours to fix.
Curriculum will be developed, with your influence, at scale, you own the delivery layer: seeing where it lands across your pods and where it doesn't. You keep Commons central to how your mentors and fellows work, and you close the loop back to the curriculum team so the next version is sharper than the last.
This is a hands\-on, operational role, and it grows with the program. You start by running the first cohort's small mentor team directly and build the delivery systems that let quality hold as the program climbs from 100 fellows to 1,000\. A real part of the craft is reading when a lesson should come from a mentor inside a pod and when the whole group needs to hear it from you in real time. It is built for someone who has run delivery for a team before and would rather raise ten people's game than do all the work themselves.
Key Responsibilities
Manage the mentor team
- Manage a team of mentors, about ten at full scale and fewer during the ramp: hire, onboard, coach, and hold the bar on the quality of their coaching
- Run your team's operating cadence: weekly syncs, 1:1s, and the escalation path when a mentor or a fellow needs help
- Make sure the day\-to\-day the model depends on actually happens across your team: pull requests reviewed on time, milestone reviews run, host managers engaged
Own delivery for your fellows
- Own the delivery outcome for your portfolio, roughly 200 fellows at full scale: whether instruction lands, not whether mentors look busy
- Own the agenda\-based instructional time for your whole portfolio: set the agenda, and decide each session whether to teach it yourself, live and to everyone at once, or hand it to your mentors to run in their pods. Either way, whether it lands is on you
- Hold the quality framework for mentorship: what good coaching looks like, how it gets reviewed, and what happens when it misses the bar
- Own the milestone\-review roll\-up across your fellows, and step in on the ones whose work is at risk
- Replace hand\-tracking with delivery systems that hold at volume; the model ran on spreadsheets at 100 fellows and won't at 1,000
Own the CodePath\-to\-Commons layer
- Partner with the Claude Corps strategic partners as their counterpart in the field: you see what is landing across your mentors and fellows, and you bring it back as structured signal
- Own how your team uses Commons: the curriculum ships through the platform, and you own whether it is used the way it was designed
- Turn what you see in the field into curriculum change requests the curriculum team can act on, and close the loop when they ship
Key Success Metrics
- 100% of your mentors are running their weekly cadence on schedule and you can pull that from your dashboard, not from what mentors report in standup
- You can name where each mentor is strong and where they need help, and your weakest spot has a concrete plan implemented within 30 days
- You've surfaced your top 3\-5 delivery patterns across your fellows to the Claude Corps leadership team, and at least 1 has shipped as a curriculum change
- You have a single delivery view that shows the state of your portfolio at a glance, in place of whatever was hand\-tracked before you
Qualifications
Required
- You have managed people and can point to specific ways you raised a team's performance, ideally technical or coaching staff
- 3\-5 years software engineering, software engineering management, product management, or other engineering\-adjacent management experience to judge the quality of technical coaching and read what fellows are shipping; you don't have to be the deepest engineer in the room, but the mentors have to respect your judgment
- You can hold a room, teaching a live session and keeping it sharp, because some weeks you are the one in front of the group
- You have owned a delivery or operations outcome at volume, personally on the hook for its quality
- You have replaced manual, hand\-run processes with systems that held up as the work scaled
- You have shipped real work using AI\-assisted development tools and can describe specifically how your workflow changed; you have to hold your mentors and fellows to that bar
- Strong written communication: feedback, roll\-ups, escalations, and change requests, in a remote and mostly asynchronous org
- Comfortable with remote, synchronous obligations across US time zones and about two evenings a month for cohort sessions and milestone reviews
Preferred
- You have managed coaches, instructors, or other managers in a bootcamp, fellowship, residency, or other cohort\-based program
- You have scaled a delivery team through fast headcount growth without letting quality slip
- Comfort with a platform\- or LMS\-delivered learning model, and reading delivery data to find risk before it surfaces
Compensation
CodePath has standardized salaries based on the position's level, no matter where you live. For this role, we're hiring at an annual salary of $130,000 to $148,000\. Salary is determined based on your relevant experience and skills as evaluated through our interview process.
Full\-Time Employee Benefits
This is a 100% remote position—work from anywhere in the U.S.! CodePath prioritizes employee well\-being with a competitive benefits package to support your health, financial security, and work\-life balance.
- Health \& Wellness: Medical, dental, and vision insurance (90% employer\-covered for employees and dependents), employer\-funded healthcare reimbursement, FSAs, and Employee Assistance Program
- Financial Security: 401(k), employer\-paid life \& disability insurance, and identity theft protection
- Work\-Life Balance: Generous PTO, paid holidays, 10 weeks of fully paid parental leave, and an annual year\-end company closure (Dec 24 – Jan 2\)
- Professional Growth: $1,000 annual professional development stipend and home office setup support
- Student Loan Forgiveness: CodePath is a qualifying employer for Public Service Loan Forgiveness (PSLF), helping employees manage student loan debt
- Additional Perks: Pet wellness plans, legal services, home/auto insurance discounts, and exclusive marketplace savings
Pay range
$130,000 \- $148,000 USD
Salary Context
This $130K-$148K 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 CodePath.org, 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. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($139K) sits 35% below the category median. Disclosed range: $130K to $148K.
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.
CodePath.org AI Hiring
CodePath.org has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $148K - $148K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 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 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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