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About This Role
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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 looking for an amazingly talented Autonomy and Computer Vision Product Owner to join our team! In this role, you will define, prioritize, integrate, and deliver autonomy, computer vision, and AI/ML capabilities for advanced unmanned systems and mission applications. Working with the Chief Engineer, program leadership, software teams, external performers, and operational users, you will translate mission needs into prioritized capabilities, measurable requirements, and testable acceptance criteria. You will help shape the product roadmap and deliver mission-ready capabilities from concept through operational evaluation.
What You'll Be Doing:
- Lead the autonomy and computer vision product roadmap. Partner with the Chief Engineer and program leadership to define the product vision, capability priorities, technical roadmap, and release strategy for autonomy, computer vision, and AI/ML software.
- Translate operational needs into software capabilities. Work with operators, mission analysts, systems engineers, and software teams to convert mission needs into clear, measurable, and testable software behaviors, performance thresholds, requirements, and acceptance criteria.
- Own product planning and backlog execution. Develop and maintain capability roadmaps, release plans, milestones, schedules, epics, user stories, acceptance criteria, and prioritized backlogs using Agile/Scrum practices.
- Coordinate multi-vendor software development. Manage capability dependencies, interfaces, integration priorities, and delivery risks across multiple vendors while maintaining compatibility with the government-owned software baseline.
- Provide autonomy capability oversight. Assess software supporting platform behavior, autonomous vehicle control, mission planning and execution, multi-vehicle coordination, dynamic mission response, fail-safe logic, geofencing, mission interruption, and recovery behaviors.
- Provide computer vision and AI/ML oversight. Work with data scientists, AI/ML engineers, MLOps specialists, and systems engineers to evaluate computer vision models, Automated Target Recognition (ATR) capabilities, performance benchmarks, datasets, synthetic data, labeling processes, and deployment readiness.
- Assess tactical edge deployment. Evaluate containerized ATR microservices, Software Development Kit (SDK) conformance, model optimization, compute and sensor requirements, resource utilization, and deployment readiness in resource-constrained tactical edge environments.
- Lead software integration planning. Determine whether vendor-developed capabilities satisfy established interfaces, architecture constraints, data requirements, performance standards, cybersecurity requirements, and integration criteria for incorporation into the government-owned baseline.
- Develop and validate operational mission threads. Work with operators, autonomy engineers, and systems engineers to define representative mission threads and translate validated mission needs into multi-platform and multi-domain autonomous behaviors.
- Support modeling and simulation. Coordinate with engineering and test teams to develop or execute Advanced Framework for Simulation, Integration, and Modeling (AFSIM) scenarios, or comparable simulations, to evaluate autonomous behaviors, system performance, and mission outcomes.
- Define technical acceptance criteria. Establish measurable acceptance standards for behavior trees, finite-state machines, fail-safe behaviors, geofence responses, dynamic-threat reactions, autonomous mission execution, AI/ML performance, and other platform-logic capabilities.
- Evaluate system performance. Review results from modeling and simulation, Software-in-the-Loop (SIL), Hardware-in-the-Loop (HIL), Vehicle-in-the-Loop (VIL), laboratory, and field testing to assess performance, reliability, safety, interoperability, and mission suitability.
- Support demonstrations and operational testing. Plan and support technical demonstrations, integration events, developmental and operational test activities, user evaluations, exercises, and capability acceptance milestones.
- Manage technical risk and vendor performance. Monitor technical deliverables, schedules, integration readiness, test results, dependencies, and applicable contract requirements. Identify risks and performance gaps early and provide technically grounded recommendations to program leadership.
- Incorporate operator feedback. Capture feedback from operational users, demonstrations, test events, exercises, and field evaluations, and incorporate validated findings into product priorities, backlog decisions, and future development plans.
Required Clearance:
Must be a U.S. citizen and posses an active Secret clearance.
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What Required Skills You'll Bring:
- Bachelor’s degree in Computer Science, Robotics, Electrical Engineering, Computer Engineering, Software Engineering, or a related technical discipline.
- 8+ years of relevant professional experience supporting autonomy, computer vision, AI/ML, software development, systems engineering, product ownership, or related technical programs.
- Demonstrated experience with autonomy software, autonomous platforms, computer vision architectures, Automated Target Recognition algorithms, deep-learning frameworks, and model deployment.
- Experience with technologies and frameworks such as PyTorch, ONNX, TensorRT, containerized microservices, and edge AI/ML deployment environments.
- Working knowledge of unmanned aerial systems, unmanned ground systems, Electro-Optical/Infrared sensors, tactical edge computing, autonomous vehicle integration, and operational mission workflows.
- Demonstrated experience as a Technical Product Owner, Product Manager, Systems Engineer, Technical Lead, or comparable technical leader supporting complex software systems.
- Experience applying Agile/Scrum methodologies to requirements development, backlog prioritization, sprint planning, release management, and capability validation.
- Experience evaluating software integration, system performance, and verification activities across simulation, laboratory, and operational field environments.
- Ability to communicate complex technical concepts clearly to software engineers, program leadership, external vendors, and operational users.
What Desired Skills You'll Bring:
- Master’s degree in Computer Science, Robotics, Engineering, Artificial Intelligence, or another relevant technical discipline.
- Experience with the Scaled Agile Framework (SAFe) and Atlassian tools, including Jira and Confluence.
- Experience supporting multi-vendor software development and integration within a government-owned software baseline.
- Experience with multi-vehicle coordination, collaborative autonomy, multi-domain operations, and autonomous mission planning and execution.
- Experience with agentic or cognitive Artificial Intelligence architectures.
- Experience with MLOps, synthetic data generation, data labeling, dataset curation, model benchmarking, and model optimization.
- Experience deploying computer vision or AI/ML capabilities to resource-constrained tactical edge devices.
- Experience developing or evaluating behavior trees, finite-state machines, autonomous platform behaviors, and mission logic.
- Experience with AFSIM or comparable modeling and simulation environments.
- Experience working directly with military operators and supporting operational demonstrations, developmental testing, operational testing, user evaluations, exercises, or fielding activities.
- Experience supporting Department of War autonomy, AI/ML, unmanned systems, C5ISR, or mission-command programs.
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: $125,100.00 - $225,200.00 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, 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.
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Salary Context
This $125K-$225K range is above the median 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 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 $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($175K) sits 17% below the category median. Disclosed range: $125K to $225K.
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.
Parsons AI Hiring
Parsons has 7 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, Research Scientist. Positions span FL, US, VA, US, MD, US. Compensation range: $130K - $266K.
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
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