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About This Role
Company
At Actian we believe data should be a competitive advantage. Through the deployment
of data technology, underpinned by a relentless and trusted service commitment, we
help business critical systems transact and integrate at their very best. As a trusted
leader in data management, integration, and analytics, our mission is to help businesses unlock the full potential of their data to drive better decision\-making and innovation wherever it resides — in the cloud, on\-premises, or hybrid environments.
With a global team of experts and a culture of innovation, we’re dedicated to helping
our customers solve their most complex data challenges.
Internship Overview
We are looking for interns to join us for our 2026 Summer Internship Program! This 12\-
week program is set to begin June 8 th , so if you are looking for an incredible opportunity to partner with the best and brightest minds in the industry, apply today. This program has been designed with our interns in mind and includes structured learning plans, a dedicated buddy, and a focused capstone project that you will have the opportunity to present in our Internship Showcase!
What It’s Like Interning with Us!
- Intern Events— just because the internship is remote, doesn’t mean we don’t
have time for fun! Regular intern events will be hosted throughout your 12\-
weeks with us!
- Time with Executives— Interns all get a chance to connect with our executive
team through panel discussions, 1:1s, Q\&A meetings, and events
- Workshops — Interns participate in workshops geared towards helping new
professionals
- Opportunity to travel – we will fly you out for onsite orientation at our Austin,
Texas office location!
Position Overview
In today’s fast\-paced environment, business stakeholders in Sales, Marketing, and
Product often face decision\-making delays due to a reliance on manual data requests
and complex reporting. The Revenue Operations team at Actian is building a RevOps AI
Analyst by leveraging the Actian AI Analyst platform—a cutting\-edge Generative AI
(GenAI) conversational interface powered by a robust Semantic Layer—to enable self\-
service analytics and empower leadership to make faster, smarter decisions.
The Technology: Actian AI Analyst
The Actian AI Analyst is designed to bridge the gap between raw data and actionable insights. By utilizing Natural Language Processing (NLP), it allows non\-technical users to ask complex questions—such as inquiries regarding deal progression, account distribution, and revenue attainment—and receive instant, data\-driven answers without ever having to write a line of SQL. With its state\-of\-the art Semantic Layer, this revolutionary product aims to move organizations away from "Steward\-heavy" manual workflows and toward Self\-Service Analytics without compromising on the reliability and accuracy of their reports, thus providing true Business intelligence.
The Capstone Project
As the RevOps AI Analyst Intern, you will work on the RevOps team to deliver a RevOps AI Analyst Agent to our Sales and Product team. With the central aim to democratize data, your mission will be to support the design, deployment, and evaluation of the pilot, ensuring the AI model delivers accurate, hallucination\-free insights by refining the Semantic Model and Data Architecture.
This project is not just about testing a tool; it is about building a scalable framework for Augmented Analytics that will empower leadership to make faster, smarter decisions.
### Responsibilities:
This is a unique opportunity to work at the intersection of Revenue Strategy and AI Implementation with high visibility across the organization.
Product Ownership \& User Discovery
- Stakeholder Engagement: Act as a junior Product Owner by conducting discovery interviews with cross\-functional stakeholders (Sales, Marketing, and Product) to identify critical analytics "pain points" and high\-value data gaps.
- Requirements Synthesis: Translate qualitative user feedback into technical requirements for the AI Analyst, ensuring the conversational interface addresses real\-world business logic.
- Strategic Roadmap: Develop a forward\-looking strategic roadmap for future AI iterations based on pilot insights, highlighting opportunities for scalability across the organization.
Data Architecture \& Semantic Modeling
- Semantic Layer Definition: Support the documentation of the Semantic Layer by building a standardized Metrics Definitions Glossary, ensuring the AI provides "one version of the truth."
- Prompt Engineering: Design and curate a Prompt Library to optimize the AI’s Natural Language Processing (NLP) capabilities and improve the precision of generated insights.
- Data Integrity \& Validation: Utilize SQL and Salesforce (SFDC) reporting to audit AI\-generated outputs, performing root\-cause analysis on query failures to ensure data accuracy.
Pilot Management \& Performance Analytics
- Operational Execution: Manage the daily lifecycle of the Pilot program, serving as the primary lead for the "Steward Inbox" to monitor query failures and user friction points.
- Success Benchmarking: Track and analyze Adoption Metrics—including Active Users, Question Success Rates, and Time\-to\-Insight—to quantify the pilot’s ROI and business impact.
- Capstone Presentation: Synthesize complex data findings into a compelling narrative for the Internship Showcase, presenting your final recommendations to the Executive leadership team.
### Nice to Haves:
We are looking for candidates who are passionate about the intersection of AI and business impact.
Educational Focus: Pursuing a degree (undergrad/masters) in Business Analytics, Data Science, Information Systems, or Business Administration.
Technical Skills:
- AI \& Knowledge Management: Experience using Generative AI (GenAI) tools and research assistants (e.g., NotebookLM, ChatGPT, or Claude) to synthesize complex documentation, extract insights from unstructured data, and build grounded knowledge bases.
- SQL Fundamentals: Proficiency in writing queries to validate data and audit AI outputs.
- Data Modeling: Understanding of relational databases, schema design, and Semantic Layer definitions.
- BI \& Analytics: Familiarity with Business Intelligence tools (e.g., Tableau, Power BI, or Looker) and data visualization principles.
Soft Skills \& Domain Knowledge:
- Stakeholder Management: Ability to translate technical concepts for non\-technical business users.
- RevOps Familiarity: Experience with Salesforce (SFDC) architecture or revenue lifecycle concepts is preferred although not required
- Problem\-Solving: A product\-owner mindset with the ability to synthesize user feedback into actionable technical insights.
### Requirements:
- Must be actively enrolled in a college degree program
- Must be legally authorized to work in the United States
We value diversity at our company. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, or any other applicable legally protected characteristics in the location in which the candidate is applying.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
Salary Context
This $41K-$62K range is below the median for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Actian Corporation, 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 $166,983 based on 13,781 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $76,880. This role's midpoint ($52K) sits 69% below the category median. Disclosed range: $41K to $62K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Actian Corporation AI Hiring
Actian Corporation has 8 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $62K - $62K.
Remote Work Context
Remote AI roles pay a median of $156,000 across 1,221 positions. About 7% 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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