Interested in this AI/ML Engineer role at Actian Corporation?
Apply Now →Skills & Technologies
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 helping 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 8th, 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
This Conversational AI \& Agentic Support Automation Intern, Salesforce focuses on enhancing customer support infrastructure using AI within Salesforce Service Cloud and Salesforce Experience Cloud. The intern will implement Agentforce as an intelligence layer behind the existing chatbot, which already leverages generative AI from Coveo, to enable more advanced, action\-oriented customer support capabilities. Customers will be able to manage cases directly within chat, including viewing cases, checking status, escalating cases (if needed) and adding comments. Develop AI\-driven capabilities such as case summarization, suggested replies, and knowledge article recommendations, while creating dashboards to analyze case trends, identify recurring issues, and flag high\-risk accounts. The goal is to improve efficiency, reduce manual effort, and deliver a more intuitive support experience.
Capstone Overview
The capstone project involves integrating Agentforce into the Customer Community to transform the existing chatbot into a more intelligent support system. Instead of replacing the chatbot, Agentforce will empower it by enabling customers to interact using natural language and perform real actions such as viewing cases, checking updates, escalating cases and posting comments. The project will also introduce AI\-driven capabilities like case summarization, suggested replies, and knowledge recommendations for support engineers and customers. This will demonstrate how AI can evolve customer support from simple Q\&A into a fully interactive, self\-service experience.
### Responsibilities:
- Integrate Agentforce with the existing Coveo chatbot as an intelligence layer (conversational AI) (without replacing it).
- Build AI support agents including Case Viewer, Case Status, Escalate Case and Case Comment agents to enable end\-to\-end case management in chat.
- Connect agents to Salesforce using Flows/APIs to retrieve cases, fetch updates, and post comments.
- Develop AI features such as case summarization, suggested replies, and recommended knowledge articles.
- Create dashboards to analyze case trends, identify recurring issues, and flag high\-risk accounts.
Lead the capstone project to design and deliver an AI\-powered support experience showcasing real business impact.
- Explore indexing video transcripts so academy videos are surfaced based on their actual content, not just course descriptions.
### Nice to Haves:
- Exposure to AI/ML concepts such as generative AI, chatbots, or natural language processing.
- Strong communication, analytical and problem\-solving skills with an interest in improving customer experience through technology.
- Strong understanding of Salesforce (Service Cloud/Experience Cloud) or CRM platforms; familiarity with Coveo or search/AI tools is a plus.
- Pursuing a degree in Computer Science, Information Systems, Data Science, or a related field.
- Experience or coursework in APIs, data integration, or automation (e.g., REST APIs, workflows, or similar concepts).
### 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.