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Job Title: Senior AI/ML Engineer
Location: Onsite at the NGA Facility in Springfield, VA
Clearance Required: Active Top Secret clearance with SCI Eligibility
Position Type: Full-Time
About the Company:
At VivSoft, we aim to solve complex federal problems using emerging and open technologies in a collaborative and rewarding environment. VivSoft is a diverse team of strategists, engineers, designers, and creators experienced in building high-performance, effective software, with a focus on impactful organizational design and software delivery dynamics. We build secure Software Factories based on DoD reference designs and NIST Frameworks for Cloud and DevSecOps. These factories deliver AI/ML Applications, Data Science Platforms, Blockchain, and Microservices for DoD, Healthcare, and Civilian Agencies.
Job Summary:
We are seeking a Senior AI/ML Engineer to support the design, development, integration, and optimization of Artificial Intelligence and Machine Learning (AI/ML) capabilities within complex, mission-critical environments. This role will focus on engineering scalable technical integration solutions, architecting integration frameworks, and ensuring the successful implementation of AI/ML technologies into existing enterprise systems and operational workflows.
The ideal candidate will bring a strong software engineering background, experience with systems integration and solution architecture, and an understanding of AI/ML technologies. This individual will collaborate closely with product managers, software engineers, technical stakeholders, and external partners to translate operational requirements into effective technical solutions while ensuring scalability, reliability, and alignment with organizational objectives.
Key Responsibilities:
- Design, develop, and implement technical integration solutions to support AI/ML capabilities across complex enterprise environments
- Collaborate with internal and external stakeholders to develop effective integration architectures and technical implementation strategies
- Translate user requirements and product management objectives into actionable technical designs and engineering solutions
- Lead the engineering and integration of AI/ML capabilities into existing applications, enterprise systems, and operational workflows
- Architect integration frameworks and provide technical recommendations to improve the interoperability and scalability of geospatial AI/ML capabilities
- Lead architecture design activities for AI/ML products and services, collaborating with product managers, engineering teams, and external technical partners
- Evaluate existing system architectures and identify opportunities to enhance AI/ML integration, performance, and operational effectiveness
- Develop and implement rapid technical solutions to address emerging requirements and time-sensitive operational needs
- Provide technical engineering expertise and guidance to cross-functional teams throughout the solution development and integration lifecycle
- Translate complex technical concepts into clear, actionable information for nontechnical stakeholders and program leadership
- Collaborate with multidisciplinary teams to resolve technical challenges and ensure successful implementation of AI/ML capabilities
- Support Agile software development activities and contribute to the continuous improvement of engineering and integration processes
Qualifications & Required Skills:
- Active U.S. Government Top Secret clearance with SCI eligibility
- Bachelor’s degree in Computer Science, Mathematics, Engineering, or a related technical field
- Demonstrated experience in software engineering, technical integration, or solution architecture within complex enterprise environments
- Experience developing software using the Scaled Agile Framework (SAFe) or another industry-standard Agile software development methodology
- Technical familiarity with enterprise systems of record, including their architecture, integration requirements, and operational dependencies
- Experience with government security authorization and approval processes, including Software Approval Processes (SWAP), Authority to Operate (ATO), or comparable frameworks
- Understanding of AI/ML technologies and their integration into enterprise applications and operational systems
- Ability to interpret operational and product requirements and translate them into effective technical solutions
- Strong analytical, critical-thinking, and problem-solving skills
- Demonstrated ability to collaborate effectively with technical and nontechnical stakeholders
- Experience working in multidisciplinary teams to solve complex engineering and integration challenges
- Strong written and verbal communication skills, with the ability to explain complex technical concepts to diverse audiences
Preferred Qualifications & Skills:
- Master’s degree in Computer Science, Mathematics, Engineering, or a related technical field
- Experience supporting the integration of AI/ML capabilities within complex, mission-critical environments
- Experience collaborating with cross-functional teams, including data scientists, software engineers, product managers, vendors, and technical stakeholders
- Experience supporting enterprise-scale AI/ML architectures and integration frameworks
- Experience working in highly regulated or security-sensitive technology environments
- Prior experience supporting federal acquisition programs or mission-support contracts
- Familiarity with DoDI 5000.87 or comparable software acquisition frameworks
- Active U.S. Government Counterintelligence (CI) Polygraph
Benefits:
- Comprehensive Medical, Dental, and Vision Plans (Healthcare benefits are 100% employer-paid for employees only)
- Life Insurance
- Paid Time Off (Flexible/Combined PTO, Bereavement Leave, 11 Company Paid Holidays)
- 401K Retirement Plan with employer match
- Professional Development Training Reimbursement
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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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At VivSoft technologies, 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 in Demand for This Role
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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
VivSoft technologies AI Hiring
VivSoft technologies has 3 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Positions span Remote, US, Springfield, VA, US. Compensation range: $145K - $145K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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