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About This Role
RESULTS. INNOVATION. VALUES. ACCOUNTABILITY.
That's RIVA.
We're a mission-driven IT services company and systems integrator supporting digital transformation and modernization for federal government agencies. Since 2009, we've partnered with our customers to solve complex challenges through smart, practical innovation to deliver real outcomes where they matter most. Our teams are made up of industry-leading experts who are passionate about doing great work and making a difference. We don't just develop solutions, we support efforts that strengthen communities and serve the public good.
RIVA's culture is built on four core values: Results, Innovation, Values, and Accountability. They guide how we work, how we collaborate, and how we measure success. Our employee-first approach is rooted in trust, ownership, and meaningful work. By investing in our people and fostering a flexible, supportive environment, employees have the opportunity to grow their skills, contribute ideas, and make an impact from day one. all while supporting missions that matter.
POSITION OVERVIEW
RIVA Solutions is seeking an Artificial Intelligence and Machine Learning Engineer to support the USPTO P-TACTS program, the Patent Trial and Appeal Board case tracking and electronic filing system. This role designs, builds, and deploys production AI and ML solutions, with an emphasis on large language models, retrieval augmented generation, and scalable cloud deployments including on Databricks and within the program AWS Cloud environment. The engineer works directly with government stakeholders and technical teams to turn mission needs into working, maintainable applications
MINIMUM QUALIFICATIONS
- Bachelor's degree in Computer Science, Engineering, or a related field.
- 5+ years of applied ML and AI engineering experience.
- Strong Python skills and hands-on experience with ML frameworks such as TensorFlow, PyTorch, scikit-learn, and Keras.
- Practical experience with LLMs, RAG architectures, and vector databases.
- Cloud platform experience with [AWS or GCP — confirm USPTO environment], including deployment of ML workloads.
- Experience across supervised, unsupervised, and reinforcement learning approaches.
- Eligibility to obtain a USPTO Public Trust and authorization to work in the U.S.
- Databricks Certified Machine Learning Engineer, AWS Certified Machine Learning Engineer or an equivalent cloud certification.
PREFERRED QUALIFICATIONS
- Experience with generative and multimodal AI applied to documents and images, such as OCR, layout analysis, and information extraction from scanned filings.
- Databricks Certified Machine Learning Engineer, AWS Certified Machine Learning Engineer or an equivalent cloud certification.
- MLOps experience with pipeline orchestration.
- Full-stack development experience integrating ML services with web interfaces.
- Databricks Certified Machine Learning Engineer, AWS Certified Machine Learning Engineer or an equivalent cloud certification.
- Prior experience delivering on federal government contracts.
- Experience with USPTO, PTAB, or patent and intellectual property data.
Experience supporting a FISMA Moderate system through security authorization and continuous monitoring. *
EQUAL EMPLOYMENT OPPORTUNITY & ACCOMMODATION
We believe great teams are built from different backgrounds, perspectives, and lived experiences, and we mean that beyond the buzzwords.
RIVA is an equal opportunity employer. We welcome applicants of every race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, veteran status, and any other characteristic protected by law.
If you need an accommodation at any point in our process, send an email to [email protected], we'll be happy to help.
VETERAN SUPPORT & ACCESSIBILITY
As a federal contractor, RIVA follows the requirements of the Vietnam Era Veterans' Readjustment Assistance Act (VEVRAA) and Section 503 of the Rehabilitation Act, supporting the employment and advancement of protected veterans and individuals with disabilities. If you're a veteran, we encourage you to self-identify during the application process, it helps us track our hiring commitments and, in some cases, may support priority referral for open roles.
RIVA also invites applicants to voluntarily self-identify as having a disability during the application process, not because it affects your chances of being hired, but because it helps us measure how well we're living up to our commitments and identify where we can do better. Your response is confidential and entirely optional.
Salary Context
This $180K-$190K range is above the median for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Riva Solutions Inc, 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 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($185K) sits 13% below the category median. Disclosed range: $180K to $190K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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: $272,275; VP: $235,000.
Riva Solutions Inc AI Hiring
Riva Solutions Inc has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $190K - $190K.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% of all AI roles offer remote work.
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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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