Interested in this AI/ML Engineer role at Maximus?
Apply Now →Skills & Technologies
About This Role
Essential Duties and Responsibilities:
- Lead, develop, collaborate, and advance the applied and responsible use of AI, ML, mathematical, and data science solutions throughout the enterprise by finding the right fit of tools, technologies, methodologies, processes, and automation to enable effective and efficient solutions for each unique situation. Lead the use of applied mathematical analyses to provide solutions.
- Lead efforts across the enterprise to support the creation of solutions and real mission outcomes, emphasizing and teaching the ability to flex and demonstrate initiative when dealing with ambiguous and fast-paced situations.
- Act as technical translator and role model for effectively articulating and translating technical needs, solutions, outputs, and impacts to all levels, regardless of technical proficiency, in a respectful, collaborative, and situationally appropriate manner.
- Maintain deep, current knowledge of the AI technology landscape and emerging developments, evaluating their applicability for use in production/operational environments.
- Lead the creation, curation, and promotion of playbooks, best practices, lessons learned, and firm intellectual capital.
Job-Specific Essential Duties and Responsibilities:
- Design, build, and improve production AI solutions.
- Own difficult technical problems from initial exploration through implementation, deployment, troubleshooting, and continuous improvement.
- Write high-quality software and contribute directly to custom enterprise capabilities.
- Build AI-enabled applications that integrate models, enterprise data, cloud services, APIs, and existing systems.
- Improve AI solutions by incorporating holistic algorithmic improvements.
- Evaluate emerging AI technologies through hands-on experimentation and determine where they can provide practical value in production.
- Build reusable components, libraries, patterns, and reference implementations that enable other teams to deliver AI-based capabilities.
- Mentor other staff through solutioning of custom algorithms and solution enhancements.
Minimum Requirements
- Bachelor's degree in relevant field of study and 7+ years of relevant professional experience required, or equivalent combination of education and experience.
Job-Specific Minimum Requirements:
- Bachelor's degree in a relevant field of study and 10+ years of relevant professional experience required.
- 7+ years of professional advanced algorithm building and data science experience, including substantial experience building and supporting production algorithms on systems processing real data.
- 7+ years of professional Data Science or advanced algorithm building experience.
- 5+ years of experience designing or building solutions in cloud environments.
- 3+ years of experience with artificial intelligence, machine learning, or related technologies.
- Demonstrated experience developing algorithms including AI and machine learning algorithms.
- Strong understanding of the application of mathematics, AI, and signal processing in production algorithms.
- Experience designing and implementing algorithms on AWS, Azure, GCP, or similar cloud platforms.
- Experience with deploying algorithms on production systems outside of notebooks (e.g. outside sagemaker, jupyter).
- Experience building or integrating AI/ML capabilities into software systems.
- Ability to communicate technical decisions, tradeoffs, and risks clearly with technical and non-technical colleagues.
- US Citizen and able to obtain a Public Trust.
Preferred Key Skills Qualification:
- Deep expertise in Python, demonstrated through substantial production software rather than scripts, notebooks, or prototypes alone.
- Proficiency in at least one systems programming language, such as Rust, Go, C++, or C, with experience applying it to real-world software engineering problems.
- Experience building and operating distributed or cloud-native production systems.
- Experience building algorithms with classical signal processing.
- Experience building AI models from scratch
- A track record of taking algorithms from initial development through production and ongoing operation.
- Experience diagnosing complex production problems across application, infrastructure, data, and integration boundaries.
- Practical experience building algorithmic solutions leveraging large language models, machine learning models, or managed AI services.
- Experience working in environments where security, privacy, reliability, or regulatory requirements influence engineering decisions.
- Experience improving existing systems where prior decisions limit your options.
- Experience mentoring staff through collaboration, design reviews, and technical problem solving.
#techjobs #Veteranspage #LI-Remote
EEO Statement
Maximus is an equal opportunity employer. We evaluate qualified applicants without regard to race, color, religion, sex, age, national origin, disability, veteran status, genetic information and other legally protected characteristics.
Pay Transparency
Maximus compensation is based on various factors including but not limited to job location, a candidate's education, training, experience, expected quality and quantity of work, required travel (if any), external market and internal value analysis including seniority and merit systems, as well as internal pay alignment. Annual salary is just one component of Maximus's total compensation package. Other rewards may include short- and long-term incentives as well as program-specific awards. Additionally, Maximus provides a variety of benefits to employees, including health insurance coverage, life and disability insurance, a retirement savings plan, paid holidays and paid time off. Compensation ranges may differ based on contract value but will be commensurate with job duties and relevant work experience. An applicant's salary history will not be used in determining compensation. Maximus will comply with regulatory minimum wage rates and exempt salary thresholds in all instances.
Accommodations
Maximus provides reasonable accommodations to individuals requiring assistance during any phase of the employment process due to a disability, medical condition, or physical or mental impairment. If you require assistance at any stage of the employment process-including accessing job postings, completing assessments, or participating in interviews,-please contact People Operations at [email protected] .
Minimum Salary
$180,000.00
Maximum Salary
$200,000.00
Salary Context
This $180K-$200K range is above the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Maximus, 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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($190K) sits 10% below the category median. Disclosed range: $180K to $200K.
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.
Maximus AI Hiring
Maximus has 5 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span Tysons, VA, US, Remote, US. Compensation range: $129K - $200K.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.