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About This Role
AST SpaceMobile is building the first and only global cellular broadband network in space to operate directly with standard, unmodified mobile devices based on our extensive IP and patent portfolio and designed for both commercial and government applications. Our engineers and space scientists are on a mission to eliminate the connectivity gaps faced by today's five billion mobile subscribers and finally bring broadband to the billions who remain unconnected.
Position Overview
We are seeking an AI SaaS Product Manager to own the released application portfolio built in-house by the AI & Autonomy division — the AI-powered web, mobile, and spatial applications used daily by manufacturing, satellite operations, analytics, and executive teams, along with AI capabilities delivered to telecom partners. This person is the connective tissue between the engineers who build these applications, the internal customers who depend on them, and the IT organization that helps support them, deciding what ships next based on real usage and ensuring everything that ships is documented, learnable, and supportable. As the organization matures its operational support model for AI applications, this role also supports the Chief of AI and Autonomy in executing the transition roadmap that moves these applications from build-team support into steady-state operations. This is a hands-on, writing-heavy product role for someone fluent enough in AI/ML systems to document them accurately, and user-obsessed enough to measure success by adoption, time-to-resolution, and how rarely people need to ask for help.
Key Responsibilities:
- Own the post-launch product lifecycle for the AI division's released applications — prioritizing enhancements, defects, and retirements based on usage data, user feedback, and business impact — and maintain a published roadmap internal customers can plan against.
- Partner daily with the full-stack, ML, and data science engineering teams to translate user needs into clear requirements, user stories, and acceptance criteria; participate in sprint planning, backlog grooming, and release readiness reviews.
- Run structured discovery with internal customers — quality inspectors, manufacturing engineers, satellite operations staff, analysts, and executives — through interviews, floor-side shadowing, and usage analytics, converting what you learn into prioritized product decisions.
- Define and track product success metrics for each released application (adoption, active usage, task completion, feature engagement, support volume, user satisfaction) and report them to the Chief of AI and Autonomy.
- Own release readiness as a gate: no application ships without release notes, updated user documentation, a known-issues list, and an agreed support plan.
- Write and maintain end-user product documentation for every released application — getting-started guides, workflow walkthroughs, feature references, administrator guides, and versioned release notes.
- Design and maintain in-app guidance: onboarding tours, contextual help, empty states, and error messaging written so users can self-correct without filing a ticket.
- Build and maintain the self-help troubleshooting library — a catalog of common errors, their causes, and step-by-step resolution paths mapped to actual error codes and failure modes, including AI-specific ones such as low-confidence predictions, model timeouts, and degraded offline sync.
- Partner with engineering to ensure errors are instrumented, human-readable, and traceable, turning raw exceptions and API failures into diagnosable user-facing messages with documented remediation steps.
- Co-own the support model with Corporate IT: define tiering and escalation paths, write the IT-facing runbooks and triage scripts used at first contact, and train the help desk on each application before it launches.
- Serve as the escalation point for internal customer issues IT cannot resolve — coordinating with engineering on root cause, workarounds, and permanent fixes, and closing the loop with the affected users.
- Support the Chief of AI and Autonomy in building and executing the transition roadmap as the organization matures its operational support model for AI applications — tracking phases, milestones, and readiness criteria, and flagging risks to the handoff of operational ownership from engineering to steady-state operations.
- Partner with factory NOC staff to define how each released AI application is monitored and supported in production: service catalog entries, health dashboards, alert thresholds and routing, on-call rotations, and escalation paths.
- Help coordinate across the internal AI research lab, data center operations, engineering, and Corporate IT to document clear ownership (RACI) for applications, models, and the compute and network infrastructure they run on, so nothing falls between teams during the transition.
- Write NOC-facing runbooks, standard operating procedures, and incident playbooks for every application; prepare and facilitate transition readiness reviews and tabletop exercises before each responsibility moves.
- Help put incident, problem, and change management practices in place with the NOC — post-incident reviews, change calendars, and planned-maintenance communications to internal customers — so users experience the transition as continuity, not disruption.
- Analyze support tickets and self-help usage to identify recurring failure patterns, feeding documentation updates, UX fixes, and backlog items that measurably reduce ticket volume over time.
- Establish and report service metrics for the portfolio: first-contact resolution, time-to-resolution, self-service deflection, documentation coverage, and user satisfaction.
- Own the documentation system itself — information architecture, style guide, templates, review cadence, and version control — so content stays accurate as applications evolve release over release.
- Run enablement for every new release: launch communications, training sessions, office hours, and champion networks inside each internal customer organization.
- Support partner-facing AI features with externally appropriate documentation, onboarding material, and support workflows in coordination with the teams serving telecom partners.
Qualifications
Education:
Bachelor's degree in a technical, business, or communications field, or equivalent experience.
Experience:
- 3-5 years of product management experience, including at least 1 year owning software already in production and used daily — internal platform, enterprise SaaS, or B2B product.
- Demonstrated ownership of product documentation: personally written user guides, release notes, and help content, not only reviewed what others wrote.
- Experience building or running a self-service support model — knowledge base, troubleshooting content, tiering and escalation, and the metrics that prove it is working.
- Working fluency with AI/ML-powered products: able to discuss model behavior, inference, latency, confidence scores, and failure modes credibly with ML engineers, and explain them plainly to a non-technical user.
- Experience partnering with IT or help desk organizations on application support, onboarding, access, and permissions workflows.
- Experience contributing to an operational transition — helping move applications or services from a build team into a NOC, SRE, or managed-operations function — including tracking milestones and readiness criteria against a roadmap.
- Strong analytical skills: comfortable with product analytics and support data, and with SQL or an equivalent for pulling your own numbers.
- Experience working in an agile engineering environment with a mix of remote and on-site teams.
Preferred Qualifications:
- Experience documenting or supporting applications with conversational AI, LLM/RAG, or computer-vision features.
- Background in technical writing, developer documentation, or docs-as-code workflows (Markdown, Git, static site generators).
- Experience with knowledge base and service platforms (Confluence, Notion, Zendesk, Jira Service Management, ServiceNow) and in-app guidance tools (Pendo, Appcues, WalkMe).
- Familiarity with product analytics and instrumentation planning (Amplitude, Mixpanel, PostHog).
- Familiarity with NOC and IT service management practices (ITIL incident, problem, and change management) and observability platforms (Grafana, Prometheus, Datadog).
- Experience with factory or operational-technology (OT) networks, data center operations, or GPU compute environments supporting AI workloads.
- Experience supporting users in manufacturing, operations, or field environments where downtime carries real cost.
- Experience turning internally built tools into genuine products adopted across an organization.
- Experience with partner-facing or multi-tenant products, including white-label documentation and support requirements.
- Familiarity with API and developer documentation for internal platform consumers.
- Background in aerospace, defense, satellite, or telecommunications.
Soft Skills:
- Exceptional written communication — clear, concise, structured writing is the core of this role.
- Strong cross-functional collaboration skills, partnering effectively with engineering, IT, and internal customer teams.
- User-obsessed mindset, measuring success by adoption, time-to-resolution, and how rarely people need to ask for help.
Technology Stack:
- Knowledge base and service management platforms (Confluence, Notion, Zendesk, Jira Service Management, ServiceNow).
- In-app guidance tools (Pendo, Appcues, WalkMe).
- Product analytics and instrumentation tools (Amplitude, Mixpanel, PostHog).
- SQL or equivalent for product analytics and support data.
- Observability platforms (Grafana, Prometheus, Datadog).
- Docs-as-code tooling (Markdown, Git, static site generators).
Physical Requirements
- Ability to lift up to 25 lbs.
- Ability to use a computer for extended periods.
- Ability to work in a standard office environment, with occasional visits to manufacturing floor and operations environments.
- Ability to travel occasionally to Midland, TX and Lanham, MD to work alongside users on the manufacturing floor and in operations.
This job description may not be inclusive to the duties and responsibilities listed. Additional tasks may be assigned to the employee from time to time or the scope of the job may change as needed by business demands*.*
AST SpaceMobile is an Equal Opportunity, at will Employer; employment is governed on the basis of merit, competence and qualifications and will not be influenced in any manner by race, color, religion, gender, national origin/ethnicity, veteran status, disability status, age, sexual orientation, gender identity, marital status, mental or physical disability or any other legally protected status.
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 AST SpaceMobile, 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. Mid-level AI roles across all categories have a median of $193,200.
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.
AST SpaceMobile AI Hiring
AST SpaceMobile has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Midland, TX, 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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