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About This Role
Development Team Lead / Machine Learning Engineer
Position Summary: The Development Team Lead and Machine Learning Engineer will lead technical execution across AHR’s AI-driven defense and intelligence portfolio. This portfolio focuses on building operational AI solutions on AHR’s DarkStax™ platform, bringing together machine learning, secure software development, and mission-focused engineering. The lead will help translate customer and internal objectives into practical development plans, guide technical decisions, and ensure teams deliver integrated capabilities that can progress from prototype and demonstration to program use.
This role will lead development teams supporting AHR customer programs, internal research and development projects, and technical demonstrations. The lead will manage and track team activities across efforts such as the Cognitive Cyber Mission Planner, AI-enabled Help Desk, etc. Responsibilities will include organizing and prioritizing the team’s work, maintaining visibility into schedules and technical progress, coordinating dependencies, identifying delivery risks, and communicating status to program leadership and other stakeholders.
The role combines team leadership with hands-on AI /ML engineering. In addition to guiding the development team, this person will contribute directly to the design, development, integration, and evaluation of AI/ML capabilities. The ideal candidate enjoys leading people and projects while remaining close to the technology, solving difficult technical problems, and helping AHR turn emerging AI concepts into working defense and intelligence solutions.
Key Responsibilities
· Lead the day-to-day technical execution of a multidisciplinary development team, including work planning, technical decisions, code and model reviews, risk management, and delivery commitments.
· Serve as a Scrum Master / Product Owner by facilitating Agile ceremonies, maintaining and prioritizing the backlog, refining acceptance criteria, removing impediments, and keeping stakeholders informed of delivery progress.
· Design, train, evaluate, and improve machine learning models using reinforcement learning, neural networks, deep learning, and computer vision techniques.
· Define experiment plans, performance measures, validation methods, and data requirements that connect model results to operational needs.
· Guide the development of reproducible ML pipelines for data preparation, training, testing, versioning, deployment, and monitoring.
· Integrate models into secure software applications, services, APIs, and mission systems while maintaining reliability, scalability, and traceability.
· Assess model robustness, failure modes, and vulnerability to adversarial conditions, especially for cyber, adversarial AI, or communications use cases.
· Mentor engineers and data scientists, establish practical development standards, and improve the team's technical and Agile practices.
· Collaborate with systems engineers and mission stakeholders to align technical designs, interfaces, demonstrations, and releases with program objectives.
· Present technical approaches, tradeoffs, results, and risks clearly to engineering teams, program leadership, and government customers.
Required Qualifications
Education and Experience
· Bachelor's degree in computer science, machine learning, artificial intelligence, electrical engineering, software engineering, applied mathematics, or a related technical field.
· Seven to ten years of relevant experience in ML and software engineering, including experience leading technical teams or workstreams.
· Experience serving as a Scrum Master and/or Product Owner on an Agile development team.
· Hands-on experience developing and evaluating machine learning solutions using reinforcement learning, neural networks, deep learning, computer vision, etc.
· Experience working in at least one of the following domains: cyber, adversarial AI, or communication systems.
· Software development experience sufficient to build, test, integrate, and maintain production-quality ML capabilities.
· Must be able to obtain a TS clearance.
Technical Knowledge and Leadership
· Strong Python development skills and working knowledge of common machine learning frameworks and scientific computing tools.
· Understanding of data preparation, model training, evaluation, experiment tracking, configuration management, and reproducible development practices.
· Ability to design and review software interfaces, APIs, modular services, and integration approaches for ML-enabled systems.
· Experience with MLOps, DevSecOps, automated model testing, model monitoring, or data and model governance.
· Knowledge of software development, automated testing, CI / CD practices, containers, and deployment workflows.
· Ability to translate mission and user needs into a prioritized backlog, measurable technical objectives, and incremental releases.
· Ability to lead technical discussions, make sound tradeoffs, mentor team members, and resolve delivery or integration issues.
· Clear written and verbal communication with technical teams, program leadership, and government stakeholders.
Desired Qualifications
· Active Top Secret (TS) or Top Secret/Sensitive Compartmented Information (TS/SCI) security clearance.
· Prior experience supporting defense or intelligence community customers, including the United States Air Force, United States Space Force, National Reconnaissance Office, National Geospatial-Intelligence Agency, or Defense Intelligence Agency.
· Experience applying model-based systems engineering (MBSE) methods and tools to define requirements, architecture, interfaces, verification, or digital engineering artifacts.
· Experience deploying ML solutions in cloud, edge, constrained, or classified environments.
Work Location
This position may be based in Chantilly, VA / Washington, DC metro region, or Plano, TX.
Some travel is required.
Job Type: Full-time
Pay: From $150,000.00 per year
Education:
- Bachelor's (Preferred)
Security clearance:
- Top Secret (Required)
Ability to Commute:
- Chantilly, VA 20153 (Required)
Work Location: In person
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 Ad hoc Research Associates, 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. Senior-level AI roles across all categories have a median of $228,700.
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.
Ad hoc Research Associates AI Hiring
Ad hoc Research Associates has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Chantilly, VA, US.
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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