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About This Role
Description:
Quantum Sky is searching for a Director, Axis — Decision Advantage & AI Applications. Axis is where Quantum Sky builds the AI applications that put decision advantage in mission hands. You own it — and the engineering discipline every Quantum Sky software team ships with.
Project 137 is Quantum Sky's applied-research and IP engine — a small, cleared, elite team that builds the differentiated technology our business units take to DoW, IC, and Federal Civilian missions. It runs four pillars: Groundstate(Quantum), SkyWatch (Cyber), Matrix (Digital Platforms & Infrastructure), and Axis (Decision Advantage / AI applications).
The mission: make Quantum Sky the fastest credible builder of mission AI software in the federal market. That means an AI-native software development lifecycle (the AI SDLC) that turns a validated mission need into a working, accredited application in weeks, not quarters — and the evidence to prove it. This is not a delivery-management role. You decide what we build, how we build it, and how we show clients the difference. Responsibilities:* Three things you will own: + The AI SDLC and Convergence Forge. Convergence Forge is Quantum Sky's engineering foundation; the AI SDLC is its core. You own both and enforce them enterprise-wide — across Project 137 and the business unit delivery programs. + The agentic backplane (with Matrix). The shared control plane, A2A/MCP plumbing, and guardrails that let agents operate across Quantum Sky systems. Co-developed by Axis and Matrix; leveraged by every pillar. Axis owns the application and agent layer. + The Axis product line. AI applications, agents, and solution accelerators for decision advantage — built as IP Quantum Sky keeps.
- You lead the Axis pillar: its roadmap, its IP, and a team of 5–8 engineers spanning AI/ML, software engineering, and DevSecOps. Axis stays small and elite; scale happens inside the business units, running on the AI SDLC you set.
- Establish and enforce the AI SDLC. Define how Quantum Sky builds software with AI agents in the loop — requirements, design, code generation, test, security, documentation, and accreditation evidence. Set the standards, tooling, guardrails, and gates. Drive adoption across every BU delivery program, and hold the line on quality and security.
- Impact metrics that win work. Build the measurement system for our app dev shop: cycle time from need to working software, defect escape, rework, cost per feature, time to ATO evidence, mission outcome measures. Package them so BUs, BD, and Growth can put them in front of clients and in proposals. Numbers we can defend, not vanity metrics.
- Solutions and products. Build Axis applications, agents, and accelerators for decision advantage across DoW, IC, and FedCiv. Quantum Sky retains IP by default and chooses the motion — solution, managed service, or product.
- Integration across Project 137. + Matrix — co-develop the agentic backplane; Axis apps run on Matrix's cleared cloud and MLOps fabric. + SkyWatch — secure-by-design pipelines, SBOMs, and agentic AppSec built into the AI SDLC; Axis builds the applications behind SkyWatch's agentic SOC. + Groundstate — crypto-agile applications by default; build the classical and hybrid application layer around Groundstate's quantum algorithm work.
- Hunting as a squad. Pair with the business units (mission intimacy) and Growth (market and contract value) to validate need, market, and feasibility before building. Take working IP to test-and-evaluation events to harden it against real mission scenarios.
- M&A in software engineering and app dev. Source, assess, and diligence targets; judge engineering quality, IP, and talent; own technical integration onto the AI SDLC after close.
- The technical face of Axis. Lead whiteboarding with program offices and CIO/CTO/CDAO organizations. Brief in the Project 137 lab and Executive Briefing Center.
- A multi-disciplinary team. Hire and develop engineers who go from mission problem to accredited production without silos.
Qualifications: Required:* The blueprint. 12+ years building and shipping software, including 5+ years delivering mission applications for DoW, IC, or Federal Civilian customers. You have shipped production software at federal scale and accreditation level.
- The scars. 8+ years hands-on across modern software engineering: cloud-native architecture, DevSecOps and CI/CD, APIs and data integration, and full-stack or ML application development. You have led architecture, delivery, and operations — not just oversight.
- The AI-native edge. 2+ years leading teams that build with AI in the loop — AI coding agents, LLM-powered applications, RAG, multi-agent systems — and the evals, guardrails, and governance that make them trustworthy. You know the difference between a demo and a fielded agent, and you have shipped the latter.
- The measurement discipline. You have stood up engineering metrics (DORA or equivalent) and tied them to business and mission outcomes. You know which numbers survive scrutiny from a skeptical program office.
- The accreditation path. You have taken software from prototype to ATO, ideally continuous ATO via a software factory. Fluent in the DoW Software Acquisition Pathway, CSOs and OTAs, software factories, SBOMs, secure-by-design, and the SWFT direction of travel.
- The standards. Working fluency in NIST 800-53 and 800-218 (SSDF), FedRAMP, the NIST AI RMF, and OMB M-25-21 and M-25-22 on federal AI use and acquisition.
- The builder mindset. You have hired, developed, and kept senior engineers in a competitive market, and you set engineering standards that other teams actually adopt.
Desired:* SDLC visionary. An operationalized point of view on how AI changes the software lifecycle — conference talks, public writing, named patterns or open-source work.
- M&A experience. Technical diligence or post-merger integration of software or app dev firms.
- Decision advantage domain depth. C2, ISR, intelligence analysis, logistics, or fraud and benefits processing.
- Lab rat. You still write code, build reference implementations, and break vendor claims to understand them.
Clearance:* Active Secret required. Active TS/SCI preferred; strong candidates may be sponsored.
About Quantum Sky: Compensation:* Compensation is unique to each candidate and relative to the skills and experience they bring to the position. The salary range for this position is typically between $225,000-$275,000. This does not guarantee a specific salary as compensation is based upon multiple factors such as education, experience, certifications, and other requirements, and may fall outside of the above-stated range.
Benefits:* Highlights of our benefits include Health/Dental/Vision, 401(k) match, Paid Time Off, STD/LTD/Life Insurance, Referral Bonuses, professional development reimbursement, and parental leave.
The world the mission operates in is going post-quantum, contested, and machine-speed. Quantum Sky engineers the advantage across cyber, networks, software, and quantum because the mission demands dominance, not parity. We don't follow the map. We draw it.
At Quantum Sky, we believe that success starts with our people. We foster a collaborative, innovative, and mission-driven environment where every team member plays a critical role in shaping the future of technology. Are you ready to join #TeamQuantumSky?
Quantum Sky Engineering LLC is an Equal Opportunity Employer; all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, [sexual orientation, gender identity,] national origin, disability, status as a protected veteran, or any characteristic protected by applicable law.
Salary Context
This $225K-$275K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Quantum Sky, 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 $212,000 based on 11,857 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,275. This role's midpoint ($250K) sits 18% above the category median. Disclosed range: $225K to $275K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Quantum Sky AI Hiring
Quantum Sky has 3 open AI roles right now. They're hiring across AI/ML Engineer. Based in Reston, VA, US. Compensation range: $140K - $275K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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