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About This Role
Description
Leidos is seeking a highly skilled Proposal Writer to join our Health Mission Solutions Proposal Development team. In this role, you will use your expertise in artificial intelligence, large language models (LLMs), and generative AI tools to help develop compliant, compelling, and high\-quality proposal and pre\-proposal responses.
Working closely with Capture Managers, Solution Architects, Program Managers, and Technical SMEs, you will translate solicitation requirements into structured outlines, annotated storyboards, and draft narrative content using AI\-enabled workflows while maintaining strict compliance, accuracy, and quality standards. You will also research and write responses to RFPs, RFIs, RFQs, SOWs, PWSs, as well as contribute to proposal content including executive summaries, technical approaches, management volumes, past performance sections, and resumes.
The ideal candidate brings strong proposal writing experience along with a practical understanding of AI and LLM capabilities, limitations, and best practices. This individual will play a key role in accelerating content development, improving proposal responsiveness, and helping teams apply AI responsibly and effectively across the proposal lifecycle.
Success in this role requires the ability to thrive in a fast\-paced, deadline\-driven environment, adapt quickly to changing priorities, and maintain exceptional attention to detail. Candidates should be proactive, resourceful, and collaborative, with a willingness to support both virtual and in\-person proposal activities.
This position is based in Reston, VA. In\-person support in the DMV area, including Reston, VA and Gaithersburg, MD, is required as needed for reviews, wall walks, and development sessions.
Key Responsibilities
- Analyze RFPs, RFIs, RFQs, SOWs, PWSs, and evaluation criteria to develop compliant proposal outlines, section structures, and annotated storyboards aligned to solicitation instructions and win strategy.
- Use in\-house and approved AI and LLM tools to accelerate drafting of proposal content, including executive summaries, management approaches, technical narratives, past performance summaries, and other response sections.
- Refine AI\-generated content into client\-ready proposal material that is accurate, persuasive, compliant, and tailored to customer needs.
- Build and maintain prompt libraries, content templates, style guides, and reusable AI\-assisted workflows for proposal development.
- Partner with capture managers, solution architects, SMEs, pricing, and proposal managers to gather information and convert technical input into clear, compelling prose.
- Ensure all drafts meet compliance requirements, page allocation constraints, formatting instructions, and proposal quality standards.
- Perform content gap analysis, review remediation support, and iterative draft improvement under tight deadlines.
- Validate AI\-assisted outputs for factual accuracy, consistency, tone, responsiveness, and alignment with approved solution strategies.
- Recommend best practices for secure, ethical, and effective use of AI in proposal environments, including protection of sensitive, proprietary, and customer information.
- Support continuous improvement of proposal operations through automation, workflow optimization, and knowledge management.
Required Qualifications
- Bachelor’s degree in English, Communications, Journalism, Business, Marketing, Technical Writing, Computer Science, or related field; equivalent experience may be considered.
- 5\+ years of experience in proposal writing, proposal management support, technical writing, or business development content development.
- Demonstrated success developing compliant proposal content in fast\-paced, deadline\-driven environments.
- Strong expertise in AI, generative AI, and LLM platforms for proposal development content, including practical experience using them to draft, refine, summarize, and organize complex written material.
- Strong understanding of prompt engineering, LLM strengths and limitations, hallucination risk, and human\-in\-the\-loop review practices.
- Exceptional writing, editing, and rewriting skills with the ability to tailor content to different audiences and evaluation criteria.
- Proven ability to interpret solicitation requirements and convert them into structured outlines and responsive proposal content.
- Experience working with SMEs and cross\-functional teams to synthesize technical concepts into persuasive written responses.
- High attention to detail, especially in compliance, quality control, grammar, and consistency.
- Proficiency with Microsoft Office tools and common proposal collaboration platforms.
Preferred Qualifications
- Experience supporting federal, health, defense, aerospace, public sector, or regulated industry proposals.
- Familiarity with formal proposal development methodologies, e.g. Shipley.
- Experience creating compliance matrices, storyboards, and content plans.
- Ability to design or optimize AI\-enabled proposal workflows, including prompt libraries, retrieval\-assisted drafting, and content reuse strategies.
- Experience evaluating or implementing governance for AI use in business development or proposal operations.
- Familiarity with knowledge management, content libraries, and proposal automation tools.
- Experience supporting large, complex, or multi\-volume proposal responses.
What Success Looks Like
- Rapid creation of compliant proposal outlines and first drafts with reduced cycle time leveraging in\-house AI\-enabled tools and proposal archives
- Higher\-quality early drafts that require less rework from proposal teams and SMEs
- Consistent use of AI tools in a secure, ethical, and controlled manner
- Improved proposal team efficiency and content reuse across pursuits
Additional Notes
This role requires sound judgment in the use of AI\-generated content. The selected candidate must be able to distinguish between acceleration and automation, ensuring all outputs are reviewed, validated, and tailored before submission. The Proposal Writer will be expected to champion responsible AI adoption while preserving confidentiality, compliance, and proposal integrity.
If you're looking for comfort, keep scrolling. At Leidos, we outthink, outbuild, and outpace the status quo — because the mission demands it. We're not hiring followers. We're recruiting the ones who disrupt, provoke, and refuse to fail. Step 10 is ancient history. We're already at step 30 — and moving faster than anyone else dares.
Original Posting:
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March 27, 2026
For U.S. Positions: While subject to change based on business needs, Leidos reasonably anticipates that this job requisition will remain open for at least 3 days with an anticipated close date of no earlier than 3 days after the original posting date as listed above.
Pay Range:
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Pay Range $87,100\.00 \- $157,450\.00
The Leidos pay range for this job level is a general guideline only and not a guarantee of compensation or salary. Additional factors considered in extending an offer include (but are not limited to) responsibilities of the job, education, experience, knowledge, skills, and abilities, as well as internal equity, alignment with market data, applicable bargaining agreement (if any), or other law.
About Leidos
Leidos is an industry and technology leader serving government and commercial customers with smarter, more efficient digital and mission innovations. Headquartered in Reston, Virginia, with 47,000 global employees, Leidos reported annual revenues of approximately $16\.7 billion for the fiscal year ended January 3, 2025\. For more information, visit www.Leidos.com.
Pay and Benefits
Pay and benefits are fundamental to any career decision. That's why we craft compensation packages that reflect the importance of the work we do for our customers. Employment benefits include competitive compensation, Health and Wellness programs, Income Protection, Paid Leave and Retirement. More details are available at www.leidos.com/careers/pay\-benefits.
Securing Your Data
Beware of fake employment opportunities using Leidos’ name. Leidos will never ask you to provide payment\-related information during any part of the employment application process (i.e., ask you for money), nor will Leidos ever advance money as part of the hiring process (i.e., send you a check or money order before doing any work). Further, Leidos will only communicate with you through emails that are generated by the Leidos.com automated system – never from free commercial services (e.g., Gmail, Yahoo, Hotmail) or via WhatsApp, Telegram, etc. If you received an email purporting to be from Leidos that asks for payment\-related information or any other personal information (e.g., about you or your previous employer), and you are concerned about its legitimacy, please make us aware immediately by emailing us at LeidosCareersFraud@leidos.com.
If you believe you are the victim of a scam, contact your local law enforcement and report the incident to the U.S. Federal Trade Commission.
Commitment to Non\-Discrimination
All qualified applicants will receive consideration for employment without regard to sex, race, ethnicity, age, national origin, citizenship, religion, physical or mental disability, medical condition, genetic information, pregnancy, family structure, marital status, ancestry, domestic partner status, sexual orientation, gender identity or expression, veteran or military status, or any other basis prohibited by law. Leidos will also consider for employment qualified applicants with criminal histories consistent with relevant laws.
Salary Context
This $87K-$157K range is above the median for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Leidos, 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 $166,983 based on 13,781 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300. This role's midpoint ($122K) sits 27% below the category median. Disclosed range: $87K to $157K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Leidos AI Hiring
Leidos has 34 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer, AI Software Engineer. Positions span Houston, TX, US, Reston, VA, US, OH, US. Compensation range: $91K - $278K.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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