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About This Role
Jeppesen ForeFlight builds industry\-leading aviation software used by pilots, aircraft operators, and major airlines worldwide. As a high\-growth, private equity\-backed company, we are focused on scaling our operations, strengthening our financial infrastructure, and driving operational excellence across the business. Our team combines deep domain expertise with a collaborative, high\-performance culture to solve complex challenges and support continued growth. Remote within the US
About the Role
As Platform Marketing Manager, AI, you will sit at the center of the Jeppesen ForeFlight Airflow™ launch \- AI that reasons in context, shows its work, and operates inside aviation's safety management system. You will own positioning, messaging, launch content, and enablement for the AI portfolio while project managing the rollout across Product, Engineering, Sales, and Communications. Your market and competitive analysis will inform go\-to\-market strategy, segmentation, and packaging, working day\-to\-day with senior Product and Marketing leadership. You should be AI\-native: fluent in modern AI tools and the developer ecosystem behind them, including agents and MCP servers. It is an early\-career role with unusual scope and executive visibility. You will help define how aviation understands AI it can trust.
Key Responsibilities:
- Develop positioning, messaging, and value narratives for the Jeppesen ForeFlight Airflow™ platform and its agent portfolio, translating technical capabilities like causal decision intelligence, the aviation context graph, and AI\-SMS governance into clear customer value.
- Project manage the AI portfolio rollout end\-to\-end: build and own launch plans, coordinate timelines and deliverables across Product, Engineering, Sales, and Communications, and support the limited beta program with partner\-facing communications and feedback loops.
- Partner with cross\-functional teams, including content, design, communications, product, and commercial, to create launch, campaign, and enablement content — web copy, one\-pagers, presentations, demo narratives, and sales and partner materials — that makes the Jeppesen ForeFlight Airflow™ value proposition clear to airlines and operators.
- Track the AI landscape, including model providers, agent frameworks, open standards like MCP, and competitive offerings, and turn what you learn into competitive insight that shapes go\-to\-market strategy, segmentation, and packaging decisions.
- Champion AI\-native ways of working: bring AI tools into the team's marketing workflows and share what you know about the modern AI stack.
Basic Qualifications:
- 3\-5 years of professional experience in product marketing, strategy consulting, or a similar strategic role, with at least some exposure to marketing a technical, B2B, or SaaS product.
- AI\-native: you use AI tools like Claude, ChatGPT, or Copilot daily, and you are familiar with the AI developer ecosystem. You can explain what an agent is, what an MCP server does, and why open, model\-agnostic standards matter.
- A strong writer and storyteller who can turn complex technical concepts into crisp, compelling narratives for both technical and executive audiences.
- An organized project manager: you are comfortable owning a launch plan, running cross\-functional check\-ins, tracking dependencies, and holding stakeholders accountable to deadlines.
- Comfortable with ambiguity: this is a new platform in a new category, and you are energized rather than intimidated by white space.
- A collaborative, low\-ego teammate who leads through influence and communicates proactively.
Preferred Qualifications:
- Experience marketing an AI product, developer platform, or API\-based product.
- Hands\-on experience building with AI, including configuring agents, setting up MCP servers, or prototyping with LLM APIs.
- Familiarity with aviation, aerospace, or another safety\-critical or highly regulated industry.
About Jeppesen ForeFlight:
Jeppesen ForeFlight is a leading provider of innovative aviation software solutions, serving the Commercial, Business, Military, and General Aviation sectors globally. Combining Jeppesen’s 90\-year legacy of accurate aeronautical data with ForeFlight’s expertise in cutting\-edge aviation technology, the company delivers an integrated suite of tools designed to enhance safety, improve operational efficiency, and sharpen decision\-making.
Why You Should Join (USA):
At Jeppesen ForeFlight, we know you want a rewarding career. To do that, you need challenging projects, a good work environment, and awesome coworkers. We believe in our employees, and we empower them to make a direct impact on our products and services messaging. We strive to provide our team and their loved ones with a world\-class benefits experience, focused on supporting their physical, financial, and emotional wellbeing. Our benefits package includes but not limited to the following:
- Medical, dental, vision insurance with Employer paid health premiums
- Open PTO Policy
- 401(k) with up to 10% company matching and immediate vesting
- 12 Weeks Paid Parent Leave
- Flight Training Rewards
Pay is based upon candidate experience and qualifications, as well market and business considerations: Summary Pay Range: $122,000\.00\-$150,000\.00
Jeppesen ForeFlight – EOE including Disability/Vets \| Pay Transparency \| E\-Verify Participant
Equal Opportunity Employer/Protected Veterans/Individuals with Disabilities
This employer is required to notify all applicants of their rights pursuant to federal employment laws. For further information, please review the Know Your Rights (https://www.eeoc.gov/poster) notice from the Department of Labor.
Salary Context
This $122K-$150K range is in the lower quartile 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 ForeFlight, 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 ($136K) sits 37% below the category median. Disclosed range: $122K to $150K.
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.
ForeFlight AI Hiring
ForeFlight has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $150K - $150K.
Location Context
AI roles in New York pay a median of $220,000 across 1,650 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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