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About This Role
In a world of possibilities, pursue one with endless opportunities. Imagine Next!
At Parsons, you can imagine a career where you thrive, work with exceptional people, and be yourself. Guided by our leadership vision of valuing people, embracing agility, and fostering growth, we cultivate an innovative culture that empowers you to achieve your full potential. Unleash your talent and redefine what’s possible. Job Description:
Parsons is seeking a Senior Software Developer to support our cutting\-edge Drone Armor counter\-unmanned aerial systems (C\-UAS) program. The Senior Developer will design and implement logical, functional software code across multiple programming languages, make informed technology choices for different environments, rapidly diagnose and correct complex software issues in mission\-critical systems, and help design, integrate, and optimize natural language processing (NLP) capabilities in support of operational missions.
What You'll Be Doing
Advanced Software Design \& Development
- Create logical and functional software code in a variety of programming languages to support Drone Armor capabilities
- Lead the design and implementation of software components, services, and interfaces based on system and mission requirements
- Ensure solutions are robust, secure, maintainable, and aligned with program architecture and coding standards
- Design and implement services and pipelines that incorporate NLP models and text processing components into C\-UAS workflows (e.g., analysis of communications, reports, or operator inputs)
NLP Integration \& Applied AI
- Collaborate with data scientists and ML/NLP engineers to integrate NLP models into production systems
- Implement and optimize NLP pipelines for tasks such as classification, entity extraction, summarization, translation, or intent detection, as required by mission needs
- Integrate large language models and other NLP components via APIs or model\-serving infrastructure, ensuring performance, reliability, and security
- Work with stakeholders to translate mission and operational requirements into concrete NLP features and user\-facing capabilities
Technology Evaluation \& Trade\-Offs
- Understand and articulate the benefits and risks associated with different coding languages and frameworks in various functional environments
- Recommend appropriate languages, tools, and design patterns based on performance, security, maintainability, and integration needs
- Evaluate and recommend NLP libraries, frameworks, and platforms (e.g., transformer\-based toolkits, vector databases, or text analytics services)
- Provide technical guidance to developers on language and framework selection, coding practices, architectural decisions, and NLP integration strategies
Troubleshooting, Debugging \& Quality
- React to software problems quickly and effectively, correcting code and related configurations as necessary
- Debug complex issues across multiple layers (application, service, interface, data) and environments (development, integration, field)
- Diagnose and resolve issues specific to NLP\-enabled features, including latency, accuracy, unexpected outputs, and integration failures
- Support and refine unit, integration, and system\-level tests, including tests for NLP components (e.g., input validation, model behavior checks, regression tests on text datasets) to validate functionality and prevent regressions
Leadership \& Collaboration
- Serve as a senior technical resource within the development team, mentoring junior and mid\-level developers
- Coach team members on best practices for integrating NLP capabilities into existing architectures and workflows
- Collaborate with systems engineers, test engineers, data scientists, NLP/ML engineers, and field personnel to resolve issues and improve system performance
- Contribute to technical reviews, design walkthroughs, and continuous improvement of development and AI/NLP integration practices
What Required Skills You'll Bring
Education
- Bachelor’s degree in Computer Science, Electronics Engineering, or other engineering or technical discipline is required with 5 years of experience OR
- 8 years of relevant software development experience may be substituted for education
Experience
- Experience creating logical and functional software code in multiple programming languages
- Experience understanding and clearly articulating the benefits and risks of different coding languages in different functional environments
- Experience reacting to software problems and correcting programs as necessary in complex or mission\-critical systems
- Experience integrating or supporting NLP or text\-analytics capabilities in production or mission\-focused software systems
Technical Competencies
- Proficiency in one or more modern programming languages (e.g., Python, C\+\+, Java, C\#, Go, or similar), with working knowledge of others
- Strong grasp of software engineering best practices, including design patterns, code reviews, version control, and CI/CD workflows
- Demonstrated ability to troubleshoot and resolve complex software defects efficiently
- Familiarity with NLP concepts and tools such as tokenization, embeddings, transformer models, and vector search, and with common NLP frameworks or libraries
- Strong analytical and communication skills, capable of explaining technical and NLP\-related trade\-offs to both technical and non\-technical stakeholders
Security \& Citizenship
- Must be a US Citizen
- SECRET security clearance
What Desired Skills You'll Bring
Advanced Education \& Certifications
- Bachelor’s or higher degree in Computer Science, Computer Engineering, or related discipline
- Relevant certifications in software architecture, cloud platforms, AI/ML, or DevSecOps
Specialized Experience
- Experience supporting DoD, defense, or C\-UAS\-related software systems
- Experience with distributed, real\-time, or high\-availability systems
- Experience applying NLP in operational, defense, or intelligence contexts (e.g., processing reports, chat/voice transcripts, or multilingual text sources)
Additional Technical Skills
- Experience with containerization (Docker), orchestration (Kubernetes), and cloud\-native development
- Experience with NLP/ML ecosystems and tools (e.g., transformer\-based frameworks, vector databases, or LLM APIs) and related deployment patterns
- Familiarity with Agile/Scrum methodologies and modern issue tracking/ALM tools
Security Clearance Requirement:
An active Secret security clearance is required for this position.
This position is part of our Federal Solutions team.
The Federal Solutions segment delivers resources to our US government customers that ensure the success of missions around the globe. Our intelligent employees drive the state of the art as they provide services and solutions in the areas of defense, security, intelligence, infrastructure, and environmental. We promote a culture of excellence and close\-knit teams that take pride in delivering, protecting, and sustaining our nation's most critical assets, from Earth to cyberspace. Throughout the company, our people are anticipating what’s next to deliver the solutions our customers need now.
Salary Range: $103,500\.00 \- $181,100\.00
We value our employees and want our employees to take care of their overall wellbeing, which is why we offer best\-in\-class benefits such as medical, dental, vision, paid time off, Employee Stock Ownership Plan (ESOP), 401(k), life insurance, flexible work schedules, and holidays to fit your busy lifestyle!
Parsons is an equal opportunity employer, and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, veteran status or any other protected status.
We truly invest and care about our employee’s wellbeing and provide endless growth opportunities as the sky is the limit, so aim for the stars! Imagine next and join the Parsons quest—APPLY TODAY!
Parsons is aware of fraudulent recruitment practices. To learn more about recruitment fraud and how to report it, please refer to https://www.parsons.com/fraudulent\-recruitment/.
COMPETITIVE BENEFIT OFFERINGS
Financial Wellness
We care about your financial wellbeing. Parsons offers competitive pay and retirement plans to help you build wealth for the future while giving you the flexibility to diversify your investments.
Work Life Harmony
Balance in life is important and time away from the office is imperative to allow you to refresh and focus your attention on the things that matter to you. Parsons supports your time away by providing paid time off and paid flexible holidays.
Career Development
We are committed to fostering the personal and professional growth of our employees. Develop and advance yourself though our comprehensive training, educational and mentorship programs.
Veteran Support
We provide Industry leading benefits to support veterans and active\-duty members to provide security for you and your family by offering robust leave and benefits; including paid active\-duty military leave and paid time off when transitioning back to civilian life.
Mind \& Body
At Parsons we inspire healthier habits, heathier minds, and a healthier you through our wellness program. Participate in our weekly Meditation Mondays and Wellness Wednesdays. Wellness, at Parsons, is more than just your annual checkup.
Health
Health is not a one size fits all. At Parsons, we offer a robust Employee Assistance Program as well as comprehensive medical, dental and vision plans through large, national carriers with the choice of regional PPO, HDHP, or HMO networks.
Salary Context
This $103K-$181K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Parsons, 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. This role's midpoint ($142K) sits 34% below the category median. Disclosed range: $103K to $181K.
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.
Parsons AI Hiring
Parsons has 8 open AI roles right now. They're hiring across AI/ML Engineer, Research Engineer, Data Scientist, MLOps Engineer. Positions span Baltimore, MD, US, Remote, US, Annapolis Junction, MD, US. Compensation range: $181K - $266K.
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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