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About This Role
ABOUT MAGELLAN JETS:
Founded in 2008 and now one of the largest private aviation providers, Quincy, MA-based Magellan Jets is a premier provider of private aviation solutions, offering a comprehensive range of Jet Card ownership, On-Demand Charter, and Aircraft Sales and Management. With a focus on personalized service, Magellan Jets is dedicated to delivering customized and unparalleled flying experiences to its Private Clients worldwide. Magellan Jets and its Flight Operations team lead one of the industry’s most comprehensive safety management systems and protocols providing peace of mind to the most discerning travelers.
POSITION SUMMARY:
Reporting directly to the Vice President of Product, the Senior AI-Native Platform Product Manager owns the internal platform and tools that power how Magellan Jets sells, services, and operates. This role takes problems from why they exist to solutions that measurably change how our teams work, and helps build our next generation of AI-powered automations and AI helpers.
ESSENTIAL FUNCTIONS:
As an Senior AI-Native Platform Product Manager, you will take a hands-on approach to product development, using AI tools to explore our codebase and build working prototypes, and test with users before engineering commits. In this role, you'll serve as the link between the teams who use our internal tools and the engineers who build them, directly improving how Magellan Jets delivers world-class service. This includes:
- Owning the vision and roadmap for internal tools that support sales, client service, and operations
- Starting with why: uncover the root problem behind each request and simplify workflows, not just digitize them
- Defining outcome-based success metrics up front, such as time saved, errors reduced, and adoption, and track them after launch
- Prototyping quickly with AI coding tools and Figma, building from our engineering code, and test with users before committing engineering time
- Identifying and build AI-powered automations and AI helpers that take manual, repetitive work off our teams
- Turning validated prototypes into clear user stories and acceptance criteria, and prioritize the backlog with engineering
- Owning intake and prioritization of internal requests, communicating trade-offs transparently
- Driving adoption through demos, rollout, and enablement, then iterate on usage data and feedback
COMPETENCIES:
Knowledge:
- 5+ years of software product management experience
- Bachelors Degree in computer science, engineering, business, or a related field, or equivalent practical experience
- Platform product experience shaping internal tools or enterprise software for small businesses, including API and system integrations
- Experience writing user stories and acceptance criteria with Agile engineering teams
- Experience with CRM software in sales and client service operations preferred
Skills:
- Hands-on proficiency with AI tools, including Claude Code, with the ability to pull from an engineering codebase and build working prototypes
- Working proficiency in Figma
- Clear communication with front-line users, leaders, and engineers alike
Abilities:
- Ask why and frame problems before proposing solutions
- Use data to deliver outcomes, not output.Communicate trade-offs transparently across competing internal priorities
- Work independently and as part of a fast-paced team environment
SUPERVISORY RESPONSIBILITIES (if applicable):
This position doesn’t have any supervisory responsibilities.
WORK ENVIRONMENT:
- Location: onsite at our Quincy, MA headquarters, working side by side with the users of the tools you build
- Conditions: General office environment with moderate noise levels.
PHYSICAL DEMANDS:
- Extended periods sitting at a desk or workstation
- Occasional walking/ movement between work areas (meetings, restrooms, or kitchen)
- Repetitive Hand and Wrist Movements via frequent typing and use of a mouse
- Light lifting of office supplies, documents, or equipment (up to 20 lbs)
- Screen Use: Prolonged use of a computer screen
POSITION TYPE AND EXPECTED HOURS OF WORK:
This is a Full-Time Exempt position.
TRAVEL:
Limited travel may be required.
ADDITIONAL ELIGIBILITY QUALIFICATIONS:
N/A
AFFIRMATIVE ACTION PLAN/EQUAL EMPLOYMENT OPPORTUNITY (AAP/EEO) STATEMENT:
Magellan Jets is an equal opportunity employer and is committed to diversity in its workforce. We consider all qualified applicants without regard to race, color, national origin, gender, age, disability, or veteran status, in accordance with applicable law.
OTHER DUTIES:
This job description is intended to convey information essential to understanding the scope of the Senior AI-Native Platform Product Manager position. It is not an exhaustive list of responsibilities, duties, and skills required for the role. Magellan Jets reserves the right to amend this job description at any time to reflect business needs and operational priorities.
BENEFITS:
At Magellan Jets, we value the well-being of our team and are proud to provide a robust benefits package for all full-time, exempt employees.
Health & Wellness Benefits:
- Generous vacation and sick time policy
- 9 company paid holidays
- Medical Insurance (HMO or PPO)
- Dental and Vision coverage
- Health Reimbursement Arrangement (HRA)
- Healthcare Flexible Spending Account (HFSA)
- Dependent Care Flexible Spending Account (DCFSA)
- Rx Cost Share Assistance Program
- Life Insurance & Disability Benefits (100% Employer Paid)
- Free Employee Assistance Program (EAP)
Financial & Retirement Benefits:
- 401(k) Retirement Plan with an employer match
- Fitness Reimbursement Program
- Pet Insurance Discount
The Senior AI-Native Platform Product Manager role offers a competitive annual base salary. Final compensation is based on experience and location, and the position includes eligibility for Magellan Jets’ comprehensive benefits package.
OUR CORE VALUES:
We believe the foundation of exceptional service begins with the best people in the business that see each interaction as an opportunity to grow a relationship. Our internal culture is felt externally by our private clients because of our alignment on our core values across the business.
- Lead with Safety: Deliver the uncompromising commitment to safety
- Care Deeply: Own everything you do and constantly improve
- Create Amazing: Develop, cultivate and share a prosperity mindset
Please be cautious of potential recruitment fraud. If you are interested in exploring opportunities at Magellan Jets, please go directly to our Careers Page: https://magellanjets.com/careers. Magellan Jets will never ask you to pay a fee or download software as part of the interview process with our company. In addition, Magellan Jets will not ask for your personal banking information until you have signed an offer of employment and completed onboarding paperwork that is provided by our People Operations team. All communications with Magellan Jets Talent and People Operations professionals will only be sent from @magellanjets.com email addresses. Legitimate emails will never originate from gmail.com, yahoo.com, or other commercial email services.
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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Magellan Jets, 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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Magellan Jets AI Hiring
Magellan Jets has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Quincy, MA, US.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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