Interested in this AI/ML Engineer role at Systems Planning and Analysis, Inc. (SPA)?
Apply Now →About This Role
Overview:
Systems Planning and Analysis, Inc. (SPA) delivers high-impact, technical solutions to complex national security issues. With over 50 years of business expertise and consistent growth, we are known for continuous innovation for our government customers, in both the US and abroad. Our exceptionally talented team is highly collaborative in spirit and practice, producing Results that Matter. Come work with the best! We offer opportunity, unique challenges, and clear-sighted commitment to the mission. SPA: Objective. Responsive. Trusted.
SPA seeks an experienced attorney to serve as Associate General Counsel for Technology, AI, Intellectual Property, and Legal Operations. This role sits at the intersection of business and law, driving the development and implementation of AI-enabled processes and tools within the legal department and across SPA’s divisions. The successful candidate will partner closely with the Office of the CIO and SPA technologists to advance innovation, operational excellence, and compliance in a defense and intelligence context.
This is a new position and will be the second full-time hire for the SPA legal department. The successful candidate will have the ability to mentor and influence the legal team, helping to shape job descriptions, roles, responsibilities, and objectives. They will have the opportunity and be expected to work on a broad range of exciting and novel issues and may be asked to expand into areas they have not previously worked in. This is a highly-visible role that will have daily interaction with senior executives and employees at all levels of the organization across multiple disciplines. The primary place of performance will be hybrid from SPA Headquarters in Alexandria, VA with occasional travel.
Responsibilities: Key Responsibilities* Advise on legal matters related to technology, AI, IP, and data protection/privacy, with a focus on defense and intelligence service provider requirements.
- Lead and support the design, development, and deployment of AI-enabled legal processes and tools for SPA’s legal department and broader business operations.
- Collaborate with SPA’s CIO, IT teams, and technologists to ensure legal compliance and operational alignment with SPA’s AI strategy.
- Provide guidance on regulatory, licensing, and data processing and protection issues.
- Support SPA’s business objectives by integrating legal risk management into technology and AI initiatives.
- Develop and maintain policies and procedures for data protection, privacy, and ethical use of AI.
- Advise on AI governance aligned to NIST AI RMF and DoD (and other federal government agency) Responsible AI Tenets, including policy, model risk, testing/red teaming, human in the loop controls, and enterprise usage guidelines.
- Develop and implement IP strategy and operations—trade secrets, licensing, open source compliance, commercialization of software and associated tools, and coordination with outside counsel on patent filings—ensuring defensibility of AI enabled products and services.
- Foster cross-functional collaboration and drive change management for legal operations transformation as a trusted advisor.
SPA Business Priorities* Compliance & Ethics: Uphold SPA’s standards for objective, responsive, and trusted advice to our clients, ensuring compliance with government contracting requirements and ethical conduct in all business dealings.
- Operational Excellence: Drive continuous improvement in legal operations, leveraging AI and technology to streamline workflows and enhance efficiency across SPA’s business units.
- Innovation & AI Enablement: Advance SPA’s AI strategy by developing and deploying innovative legal tools and processes that support both the legal department and broader business objectives including software and service offerings.
- Risk Management: Integrate legal risk management and leverage AI to build robust, efficient, and effective processes and procedures for service delivery and corporate governance.
- Collaboration & Change Management: Work closely with SPA’s business transformation teams, HR, and compliance functions to align legal operations with SPA’s evolving priorities and strategic goals.
Qualifications: Qualifications
- JD from an accredited law school; licensed to practice in good standing in at least one U.S. jurisdiction.
- 7–10+ years of legal experience gained with the government, a law firm, or in-house, with a strong preference for relevant in-house counsel experience.
- Demonstrated expertise in technology, AI, IP, and legal operations, preferably within defense/intelligence, government contracting, or other highly-regulated industry.
- Strong knowledge and practical experience with AI, including legal and operational implications.
- Ability to obtain and maintain Secret-level security clearance. U.S. citizenship status is required for this role due to the requirement to have access certain controlled unclassified information (CUI).
- Experience with data protection and privacy laws/regulations (e.g., GDPR, CCPA, FISMA, NIST 800‑53/RMF, FedRAMP, and CMMC 2.0).
- Proven track record of ethical conduct and integrity; ability to pass background investigations with no history of adverse disciplinary action.
- Independent worker and self-starter, with ability to collaborate across functions and influence outcomes.
- Strong communication, leadership, and change management skills.
Preferred Attributes
- Experience partnering with CIOs, IT, and technology teams.
- Work with internal contracts, procurement, and finance teams to stand up CLM, matter management, and KPI dashboards; deploy automation/RPA and AI tools to reduce cycle times and cost-to-serve.
- Familiarity with defense/intelligence service provider requirements and SPA’s business operations.
- Strategic mindset with a passion for innovation and operational excellence.
Pay Range Information: At SPA, we strive to deliver a robust total compensation package that will attract and retain top talent. Elements of the compensation package include competitive base pay and variable compensation opportunities. SPA provides eligible employees with an opportunity to enroll in a variety of benefit programs, generally including health insurance, flexible spending accounts, health savings accounts, retirement savings plans, life and disability insurance programs, and a number of programs that provide for both paid and unpaid time away from work. The specific programs and options available to any given employee may vary depending on eligibility factors such as geographic location, date of hire, etc. Please note that the salary information shown below is a general guideline only. Salaries are commensurate with experience and qualifications, as well as market and business considerations. Virginia, Pay Transparency Salary range: USD $200,000.00/Yr. - USD $250,000.00/Yr.
Salary Context
This $200K-$250K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Systems Planning and Analysis, Inc. (SPA), 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 in Demand for This Role
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 11,866 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $110,408. This role's midpoint ($225K) sits 6% above the category median. Disclosed range: $200K to $250K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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: $272,250; VP: $235,000.
Systems Planning and Analysis, Inc. (SPA) AI Hiring
Systems Planning and Analysis, Inc. (SPA) has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Chantilly, VA, US, Alexandria, VA, US. Compensation range: $190K - $250K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.