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About This Role
Voyager is an innovative space, defense, and national security technology company committed to advancing and delivering transformative, mission-critical solutions. We tackle the most complex challenges to unlock new frontiers for human progress, fortify national security, and protect critical assets to lead in the race for technological and operational superiority from ground to space.
Forge the Future: Join Voyager Technologies
The future belongs to those who build it. At Voyager Technologies, we're building technologies that protect lives, expand frontiers and prepare us for what's next. And we're doing that with people who are wired to solve, build, adapt and lead. These roles are not for the faint of heart.
You'll help lay the foundation for humanity's future. Join a culture where innovation thrives, curiosity is rewarded, and impact is real. We're a company of doers, thinkers and builders, united by purpose and grounded in reality.
If you want to put your skills to work where the stakes are real and the mission is bigger than any one person, forge the future with Voyager.
Job Summary:
We are seeking an Agentic GEOINT Mission Management Lead to lead the technical effort building Voyager's Agentic Mission Management platform — an AI-native system that lets intelligence operators task and orchestrate large, heterogeneous satellite constellations through natural conversation instead of manual planning tools. In this role, you will own the technical vision and hands-on execution for a platform that turns operator intent into physically valid, explainable, executable collection plans — and matures it from working prototype to accredited operational capability.
You will sit at the intersection of GEOINT tasking tradecraft, collection orchestration, and agentic AI. This role is a fit for someone who has lived the tasking-to-dissemination cycle and gets impatient with monolithic, opaque, manual planning workflows — who knows that the target deck now changes by the hour while constellations have grown from a handful of exquisite assets to hundreds of government, commercial, and allied satellites, and who wants to see their agents task real sensors against real physics, not just push text around. The work also involves decomposing the judgment of collection managers and mission planners into agent definitions, tool integrations, scheduling and astrodynamics couplings, and evaluation suites.
You will be joining a specialized group within our Advanced Technology Development organization responsible for building Voyager's Agentic Mission Management platform — the AI-native system that translates human intent into mission management action. We design agentic AI systems that pair deeply with orbital mechanics, sensor models, and live mission data so that physical feasibility, explainability, and operator trust are built into every recommendation from the very first request — and so that autonomy grows deliberately, as the system earns it.
Responsibilities:
- Serve as Principal Investigator (PI) for the Agentic Mission Management effort — own the technical vision, technical roadmap, and execution; represent the work to Intelligence Community and national security space customers; and carry it from working prototype to fielded, accredited capability.
- Lead the design of an agentic architecture in which an AI orchestrator calls mission capabilities as tools, in whatever order the mission needs.
- Design, implement, and deploy production-ready AI agents (using state-of-the-art models from Google, OpenAI, etc) that translate operator intent expressed in plain language into structured collection requests and physically valid collection plans, checked against real orbital mechanics, sensor phenomenology, and access constraints — not just geometry.
- Build explainability and human-on-the-loop trust into the platform: every AI recommendation traceable, auditable, and defensible to the operator, with autonomy that expands as the system demonstrates reliability.
- Develop and maintain Model Context Protocol (MCP) servers that expose mission systems — target decks and collection requirements, satellite catalogs and ephemerides, orbit propagation and access-calculation services, tasking interfaces, and imagery archives — to agents as robust, secure tools.
- Build evaluation suites that score agents on realistic tasking scenarios: collection-deck satisfaction, revisit and timeliness, sensor-to-target pairing quality, schedule validity against high-fidelity astrodynamics and sensor phenomenology, and operator-trust metrics.
- Collaborate closely with GEOINT domain experts, former collection managers and operators, scheduling experts, and security/accreditation staff to encode tradecraft, policy, and safety constraints directly into agent behavior.
- Mentor and lead a small, high-tempo team of AI and mission-software engineers, and set the engineering standards — code, evaluation, and documentation — the platform is built to.
- Build a system that takes all-source inputs to generate context that informs collection needs.
- Use LLMs as a development accelerator for scaffolding code, writing tests, and generating documentation — you should be shipping with agents, not just building them.
Required Qualifications:
- 7+ years of professional experience in software engineering, mission systems, or GEOINT / remote-sensing systems, including demonstrated technical leadership (PI, tech lead, chief engineer, or equivalent) on R&D or prototype efforts for IC or DoD customers.
- Deep working knowledge of the GEOINT tasking and collection lifecycle — collection requirements and decks, access windows, sensor phenomenology (EO/IR, SAR, RF), constellation scheduling, and the operational realities of multi-source collection management.
- Hands-on experience building LLM-powered agents or tools (professional, research, or serious personal projects), and a demonstrated habit of using LLMs and agents to accelerate development rather than writing all boilerplate by hand.
- Strong proficiency in Python and experience building or directing backend services and APIs.
- Strong ability to decompose complex, ill-structured mission problems into structured, solvable sub-problems.
- Experience carrying a capability across the prototype-to-production gap: hardening, testing, security, and the discipline to be blunt about what is real, what is staged for demos, and what remains to be built.
- Multi-disciplinary collaborator who enters unfamiliar domains and establishes useful working depth in days-to-weeks rather than quarters, and who thrives in a short-cycle, high-tempo environment with real customers on the other end.
- Active U.S. Top Secret / SCI security clearance (or TS with SCI eligibility).
Preferred Qualifications:
- Bachelor's or advanced degree in Computer Science, Aerospace Engineering, Physics, Mathematics, or a related field; or equivalent experience.
- Prior service as a GEOINT collection manager, imagery analyst, or mission operator in the IC or military — or sustained work alongside them — sufficient to speak the operators' language and earn their trust.
- Familiarity with astrodynamics and access-calculation tools (STK, Orekit, GMAT, SGP4/SDP4 propagation) and with constellation scheduling or optimization approaches.
- Experience integrating commercial imagery tasking APIs and orchestrating collection across government, commercial, and allied constellations.
- Experience with agentic frameworks, MCP, evaluation harnesses, and growing autonomy in operational systems under human-in-the-loop and human-on-the-loop constructs.
- Familiarity with classified software development and IC accreditation — developing in and deploying to secure environments, and designing systems with an Authority to Operate (ATO) path in mind.
- Zeal for AI and national security space (and other cool tech)
Voyager offers a highly competitive total compensation package designed to support the well-being, growth, and success of our employees. Employees benefit from a flexible and comprehensive rewards program that supports both professional and personal well-being.
- Flexible Time Off (FTO), empowering employees to take the time they need to recharge and maintain a healthy work-life balance
- Comprehensive medical, dental, and vision coverage for employees and their families, with a significant portion of premiums covered by the company and many benefits paid at 100% for employees
- Flexible, affordable gym memberships with 12,700+ options nationwide, including 24 Hour Fitness, EoS Fitness, Crunch Fitness, Anytime Fitness, Blink Fitness, Chuze Fitness, and more! No long-term contracts and FREE on-demand workout videos before you enroll
- 401(k) retirement plan with a 50% company match on contributions up to 8%, supporting long-term financial security
- Company wellness programs that support physical and mental well-being
- Additional voluntary benefits and employee support resources
- The opportunity to work alongside a highly talented team in an innovative, mission-driven environment
Voyager is an Equal Opportunity Employer. Employment decisions are made without regard to race, color, religion, national or ethnic origin, sex, sexual orientation, gender identity or expression, age, disability, protected veteran status, or other characteristics protected by law.
To conform to U.S. Government export regulations, including the International Traffic in Arms Regulations (ITAR) (22 CFR Parts 120–130) and the Export Administration Regulations (EAR) (15 CFR Parts 730–774), applicants for this position must be a U.S. Person: a US Citizen, a lawful permanent resident of the U.S., or a protected individual as defined by 8 U.S.C. § 1324b(a)(3). Applicants must be eligible to obtain any required export authorizations from the U.S. Department of State or the U.S. Department of Commerce.
Minority/Female/Disabled/Veteran
The statements contained in this job description are intended to describe the general content and requirements for performance of this job. It is not intended to be an exhaustive list of all job duties, responsibilities, and requirements. This job description is not an employment agreement or contract. Management has the exclusive right to alter the scope of work within the framework of this job description at any time without prior notice.
Salary Context
This $170K-$230K range is above the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Voyager Technologies, Inc., 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. This role's midpoint ($200K) sits 6% below the category median. Disclosed range: $170K to $230K.
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.
Voyager Technologies, Inc. AI Hiring
Voyager Technologies, Inc. has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in El Segundo, CA, US. Compensation range: $230K - $230K.
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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