Interested in this AI/ML Engineer role at Coursedog?
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About This Role
### Description
The Opportunity
As Coursedog’s first AI Enablement Lead, you’ll define, operationalize, and scale how artificial intelligence is adopted across the company to drive measurable business impact. Sitting within our newly formed Centralized Operations & Systems team, this role owns end-to-end AI enablement — from enterprise strategy and operating model design to hands-on execution, governance, and adoption. You’ll partner closely with senior leaders, technical teams, and external vendors to translate business needs into scalable AI-powered workflows, tools, and systems.
This role is well-suited for a strategic, hands-on professional who thrives at the intersection of technology, systems, and outcomes. You’ll move fluidly between long-term vision and day-to-day execution, building foundational capabilities from scratch while driving real adoption across functions. If you’re energized by being a first-of-its-kind hire, enjoy turning experimentation into durable operating systems, and are motivated by improving how teams work through responsible AI, this is an opportunity to shape a critical capability at company scale.
This fully remote role reports to the SVP Of Operations and is open to candidates located anywhere in the continental US.
This role offers a competitive base salary ranging from $130,000–$145,000, depending on experience. The position includes a performance-based variable compensation plan, bringing the total on-target earnings (OTE) to $140,000–$160,000 when goals are met.
### What You'll Do:
AI Strategy & Operating Model
- Define and own Coursedog’s enterprise AI strategy, aligned to company priorities and business outcomes
- Build and maintain a multi-year roadmap for AI adoption, innovation, and governance
- Partner with senior leadership to translate business needs into prioritized AI initiatives and secure alignment and buy-in
- Track industry trends and emerging technologies to inform strategic direction and investment decisions
Execution, Orchestration & Delivery
- Build and own Coursedog’s AI Central Operating System, connecting agents, data, and workflows to enable cross-functional automation and decision-making
- Oversee the design, deployment, and scaling of AI applications, agents, and integrations across the organization
- Establish standards for AI integration, data usage, security, and safe use across tools and vendors
- Lead vendor selection, tool rationalization, experimentation, and scaling of high-impact AI solutions
Enablement, Adoption & Capability Building
- Own the AI enablement operating rhythm, including roadmap execution, learning content, internal documentation, and feedback loops
- Build AI fluency across the organization through onboarding, workshops, playbooks, and function-specific best practices
- Lead a cross-functional AI Scrum Team to develop reusable assets, guidance, and support for teams
- Partner with functional leaders to embed AI into workflows, KPIs, and operating playbooks
Measurement, Change & Communication
- Define and track AI adoption, ROI, productivity gains, and business outcomes
- Translate complex technical concepts into clear, actionable insights for executives and stakeholders
- Champion AI adoption through storytelling, executive briefings, and visible wins
- Maintain reporting and feedback loops to continuously refine AI priorities and maximize impact
### What You’ll Bring
- 4-6+ years of experience in a fast-paced tech or SaaS environment, including hands-on ownership of cross-functional initiatives.
- 2+ years leading AI or advanced technology adoption from experimentation through scaled, production use.
- Strong technical fluency in LLM-based systems (prompts, agents, retrieval patterns, embeddings, vector databases).
- Experience designing and integrating AI workflows across tools, data platforms, and APIs.
- Experience owning or evolving enterprise AI search solutions (e.g., Glean, Moveworks, Slackbot, Onyx, or similar).
- Hands-on experience with modern data platforms (e.g., Snowflake, Databricks).
- Ability to establish AI governance, safe-use policies, and risk controls in partnership with Security and IT, including day-to-day best practices for company-wide AI use.
- Proven track record enabling and training non-technical teams and driving adoption at scale.
- Entrepreneurial, process-oriented mindset with strong communication, judgment, and comfort operating in ambiguity.
Not sure if you should apply? Research shows that women and people from underrepresented backgrounds are less likely to apply unless they meet every qualification listed. At Coursedog, we’re focused on finding the best person for the role, and that person may come from a non-traditional background. We encourage you to apply even if you don’t meet every requirement — our evaluation focuses on your ability to thrive in this role and make an impact.
### Working at Coursedog
Healthcare, Dental & Vision
We follow a fixed contribution model to promote equity and transparency. Every employee receives the same annual dollar amount to apply toward medical, dental, and vision coverage. This approach gives you the flexibility to choose the plan that best fits your needs, whether that is a base-level option or more comprehensive coverage.
Depending on the selected coverage level, the company covers:
- Employee-only coverage: 65% to 99% of monthly premiums
- Employee plus dependents: 45% to 75% of monthly premiums
We partner with Aetna and Guardian, and offer access to best-in-class wellness and support programs, including:
- Spring Health (mental health support)
- XP Health (vision benefits)
- Carrot Fertility (family planning support)
- One Medical (primary care)
We also support pre-tax savings through Health Savings Accounts (HSA) and Flexible Spending Accounts (FSA).
To support long-term financial health, we offer a Safe Harbor 401(k) plan from day one of employment. Coursedog provides a 4% employer match on your total compensation (subject to federal limits) each pay period for employees contributing to a tax-advantaged retirement account.
We trust our employees to manage their time responsibly. That’s why we offer an unlimited Paid Time Off policy — because flexibility and balance are critical for sustained excellence and personal well-being.
We were built for distributed work. Our culture emphasizes flexibility over rigidity, outcomes over hours, and transparency over gatekeeping. We are committed to being a remote-first organization indefinitely, empowering you to work where and how you thrive best.
As a valued contributor to our collective success, you’ll participate in the upside. All employees are granted equity in the company aligned to your role and tenure. Equity grants are reviewed and adjusted with promotions to reflect your evolving impact.
We proudly support working parents with industry-leading benefits. Employees are eligible for up to 12 weeks of fully paid leave. This benefit is available to all eligible employees, regardless of gender or family structure.
### About Coursedog
Our mission is to break down barriers to opportunity for students.
Coursedog provides higher ed with modern technology solutions – empowering institutions, students, and communities across the globe.
Coursedog's founding story is rooted in the desire to help students achieve their highest goals. In 2018, Coursedog's co-founders and then college students, Justin Wenig and Nick Diao, were frustrated by how difficult it was to get into the classes they needed to graduate on time. After speaking with higher education provosts and registrars to better understand how academic scheduling works, they came away with a vision that permeates the company today.
Coursedog has raised $113M total capital as a remote-first, hyper-growth startup currently backed by JMI Equity, and with past investments from YC and First Round Capital. We were recently ranked in Forbes top 500 US startup employers and our employee engagement scores rank in the top 5% of tech companies.
Salary Context
This $130K-$160K range is below the median for AI/ML Engineer roles in our dataset (median: $170K across 1414 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Coursedog, 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 $210,000 based on 1,345 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $240,000. This role's midpoint ($145K) sits 31% below the category median. Disclosed range: $130K to $160K.
Across all AI roles, the market median is $220,000. Top-quartile compensation starts at $260,000. The 90th percentile reaches $311,800. For comparison, the highest-paying categories include Research Scientist ($260,000) and AI Architect ($251,680). By seniority level: Entry: $125,000; Mid: $202,000; Senior: $240,000; Director: $255,600; VP: $225,000.
Coursedog AI Hiring
Coursedog has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $160K - $160K.
Remote Work Context
Remote AI roles pay a median of $193,725 across 129 positions. About 7% 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $220,000. Top-quartile compensation starts at $260,000. The 90th percentile reaches $311,800. Highest-paying categories: Research Scientist ($260,000 median, 48 roles); AI Architect ($251,680 median, 9 roles); Research Engineer ($250,200 median, 8 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $220,000. Top-quartile roles start at $260,000, and the 90th percentile reaches $311,800. 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. Research Scientist roles lead at $260,000 median, while AI/ML Engineer roles sit at $210,000. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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