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About This Role
540 is seeking a Senior AI/ML Engineer to support a mission\-critical technology modernization effort for the Department of War. You will lead the design and evolution of production AI/ML services and infrastructure that enable teams to develop, deploy, monitor, and scale models supporting complex defense missions.
Working with software engineers, data engineers, data scientists, cybersecurity teams, and mission stakeholders, you will translate complex requirements into secure, scalable AI/ML solutions. You will define MLOps standards, guide technical delivery, and establish reusable capabilities supporting the end\-to\-end machine learning lifecycle.
Location: Arlington, VA
Citizenship \& Clearance Requirement: Per client requirements, candidates must be U.S. Citizens with an active DoW Secret (or higher) clearance
Education Requirement: Bachelor's degree in Computer Science, Engineering, or a related technical field preferred; equivalent combinations of education and relevant experience will be considered
540 Internal Thrive Level: Senior Software Engineer
WHY 540?
540 is a forward\-thinking company that the government turns to in order to \#getshitdone. We don't just talk about innovation – we deliver it. We break down barriers, build impactful technology, and solve mission\-critical problems.
HOW YOU'LL DRIVE IMPACT
- Lead the architecture and evolution of AI/ML services, platforms, and lifecycle capabilities supporting WDP
- Translate mission requirements into scalable AI/ML architectures and implementation strategies
- Define MLOps standards, reusable patterns, and best practices across engineering teams
- Architect automated pipelines for model training, validation, testing, deployment, and monitoring
- Develop reusable frameworks, libraries, and shared components that accelerate AI/ML delivery
- Design model\-serving platforms supporting secure, scalable, and reliable batch or real\-time inference
- Establish model monitoring, performance tracking, drift detection, explainability, and governance capabilities
- Define practices for model versioning, artifact management, reproducibility, feature engineering, and data lineage
- Optimize AI/ML services and infrastructure for performance, scalability, reliability, and cost efficiency
- Establish CI/CD, infrastructure\-as\-code, automated testing, and operational practices for AI/ML systems
- Lead technical reviews and resolve complex issues spanning models, applications, data, infrastructure, and production services
- Partner with cybersecurity teams to incorporate security, access control, auditing, and governance requirements
- Communicate architecture decisions and mentor engineers and data scientists on AI/ML engineering and MLOps practices
REQUIRED SKILLS \& EXPERIENCE
- 9\+ years of relevant AI/ML engineering, software engineering, or data science experience
- Experience leading the design and delivery of enterprise\-scale, production\-grade AI/ML systems
- Advanced software engineering experience using Python and commonly used AI/ML frameworks
- Experience architecting automated model training, validation, deployment, and monitoring pipelines
- Experience defining MLOps architecture, standards, and practices across engineering teams
- Experience designing model\-serving capabilities for batch and real\-time inference
- Experience deploying and operating models in cloud\-based or containerized environments
- Strong understanding of model evaluation, monitoring, drift detection, explainability, reproducibility, and governance
- Experience with Docker, Kubernetes, or similar containerization and orchestration technologies
- Experience establishing CI/CD, infrastructure\-as\-code, automated testing, and source\-control practices
- Experience architecting AI/ML solutions within AWS, Azure, or Google Cloud
- Experience with data pipelines, distributed data processing, feature engineering, and data versioning
- Ability to evaluate technical approaches and clearly communicate architecture decisions, risks, and tradeoffs
- Experience leading technical reviews, mentoring engineers, and influencing technical direction
- Ability to troubleshoot complex issues across applications, infrastructure, data, and machine learning systems
NICE TO HAVE
- Experience leading AI/ML initiatives within DoW, federal, Advana, or other enterprise data environments
- Experience architecting solutions using AWS SageMaker or comparable cloud AI/ML platforms
- Experience with MLflow, Kubeflow, Airflow, Argo Workflows, Ray, Feast, or similar technologies
- Experience building AI/ML platforms in secure, regulated, classified, or mission\-critical environments
- Experience with large language models, generative AI, retrieval\-augmented generation, or foundation\-model operations
- Experience establishing responsible AI, model\-risk\-management, or AI\-governance practices
- Experience leading AI/ML platform modernization, technology evaluations, or proofs of concept
- Currently holds, or is willing to obtain within 30 days of employment, an approved certification such as CCSP, CFR, FITSP\-M, GSEC, Security\+, or SSCP
BENEFITS \& PERKS
- Flexible PTO \+ all Federal holidays off
- Health, dental and vision insurance plans
- Flexible Spending Account (FSA)
- 401k with employer match
- Company\-sponsored life insurance, short\- and long\-term disability
- Professional development (training, certifications, conferences)
- Paid cloud developer accounts
- Referral bonuses
- HQ office perks (parking / metro reimbursement, nitro coffee \& lunches)
- Annual social events (540 Week, hackathon, charity golf tournament, etc.)
- Access to 540's Washington Capitals \& Nationals tickets
EQUAL EMPLOYMENT OPPORTUNITY (EEO)
540's policy is to provide equal employment opportunity to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.
This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation and training.
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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At 540, 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
540 AI Hiring
540 has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Arlington, VA, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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