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About This Role
Job Description:
DataRobot delivers AI that maximizes impact and minimizes business risk. Our platform and applications integrate into core business processes so teams can develop, deliver, and govern AI at scale. DataRobot empowers practitioners to deliver predictive and generative AI, and enables leaders to secure their AI assets. Organizations worldwide rely on DataRobot for AI that makes sense for their business — today and in the future.
Our interns are not observers — they are contributors. Each AI Native Intern is embedded in a real team, working on real problems, and is expected to deliver meaningful output over the course of the program. This is how we identify and develop the next generation of AI practitioners who will carry DataRobot's mission forward.
As an AI Native Intern, you will be placed on one of our engineering or product teams tackling high\-impact, process\-heavy work that is ripe for AI automation. Current focus areas include CVE resolution workflows (triage, impact assessment, dependency upgrades, and verification) and support ticket lifecycle management (categorization, diagnosis, routing, status updates, and resolution documentation). You will design and build agentic AI solutions that reduce manual toil and free engineers to focus on higher\-order problems. This is a fully remote position. The program runs approximately 6 months.
WHAT YOU'LL DO:
- Build and deploy agentic AI workflows that automate repeatable, high\-volume engineering processes such as CVE triage and support ticket management
- Use the DataRobot platform — including AutoML, GenAI tooling, and MLOps capabilities — to design, evaluate, and ship solutions
- Translate ambiguous team pain points into well\-scoped AI/ML problems with defined success criteria
- Work autonomously on defined project deliverables while staying aligned with your mentor and team
- Communicate progress through structured weekly updates and a final Intern Showcase presentation to DataRobot leadership
- Collaborate across engineering, product, and go\-to\-market teams to understand context and deliver work that sticks
- Document your work thoroughly so findings and solutions can be handed off and extended after the program
WHAT YOU'LL LEARN:
Agentic AI \& Autonomous Systems
- Design and implementation of multi\-agent systems and autonomous reasoning loops
- Agent evaluation frameworks, tool\-use reliability, and safety guardrails for autonomous agents
- Advanced orchestration using frameworks like LangGraph, CrewAI, or AutoGen for enterprise automation
Platform \& Product
- End\-to\-end proficiency on the DataRobot platform: GUI, Python/R clients, and API integrations
- How to automate the ML lifecycle from data ingestion through deployment and monitoring
Business Acumen \& Use Case Development
- How to identify and size AI opportunities within real engineering workflows
- How to communicate technical findings to both technical and non\-technical stakeholders
Communication \& Collaboration
- Async\-first collaboration using Slack, Confluence, and structured project updates
- How to present technical work clearly to senior leaders at a live Intern Showcase
PROGRAM STRUCTURE:
Phase I — Month 1: Onboarding \& Foundation
Get oriented to DataRobot's culture, tools, and platform. Complete structured training, co\-create your Individual Learning Plan (ILP) with your mentor, and begin shadowing team members on real work.
Phase II — Months 2–3: Core Project Execution
Take ownership of a defined project scoped by your team. Work moves from guided to increasingly independent as you demonstrate competency. Expect weekly feedback from your mentor and regular touchpoints with your Buddy.
Phase III — Months 4–6: Synthesis \& Showcase
Finalize your deliverables, document your findings, and prepare for the Intern Showcase — a live presentation to DataRobot leaders and peers. Optional rotations or cross\-functional exposure may be available based on performance and team need.
WHAT WE'RE LOOKING FOR:
Required:
- Currently pursuing or recently completed a Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Engineering, or a related quantitative field
- Working proficiency in Python (pandas, numpy) and comfort with data manipulation and scripting
- Foundational understanding of machine learning concepts — supervised learning, model evaluation, feature engineering
- Deep passion for autonomous agents — you have experience building systems that "do" rather than just "chat"
- Strong written and verbal communication skills; ability to explain technical work clearly to mixed audiences
- Ability to work independently, manage ambiguity, and escalate blockers appropriately
Nice to Have:
- Prior hands\-on experience with LLMs, prompt engineering, or agentic frameworks (LangGraph, CrewAI, LlamaIndex)
- Familiarity with the DataRobot platform or other MLOps/AutoML tools
- Experience with API development (FastAPI, Flask) or containerization (Docker)
- Prior internship, research, or project experience applying ML to real\-world problems
- Experience with or interest in DevSecOps concepts, CVE triage, or support engineering workflows
The talent and dedication of our employees are at the core of DataRobot’s journey to be an iconic company. We strive to attract and retain the best talent by providing competitive pay and benefits with our employees’ well\-being at the core. Here’s what your benefits package may include depending on your location and local legal requirements: Medical, Dental \& Vision Insurance, Flexible Time Off Program, Paid Holidays, Paid Parental Leave, Global Employee Assistance Program (EAP) and more!
DataRobot Operating Principles:
- Wow Our Customers
- Set High Standards
- Be Better Than Yesterday
- Be Rigorous
- Assume Positive Intent
- Have the Tough Conversations
- Be Better Together
- Debate, Decide, Commit
- Deliver Results
- Overcommunicate
Research shows that many women only apply to jobs when they meet 100% of the qualifications while many men apply to jobs when they meet 60%. At DataRobot we encourage ALL candidates, especially women, people of color, LGBTQ\+ identifying people, differently abled, and other people from marginalized groups to apply to our jobs, even if you do not check every box. We’d love to have a conversation with you and see if you might be a great fit.
DataRobot is proud to be an Equal Employment Opportunity and Affirmative Action employer. We do not discriminate based upon race, religion, color, national origin, gender (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics. DataRobot is committed to working with and providing reasonable accommodations to applicants with physical and mental disabilities. Please see the United States Department of Labor’s EEO poster and EEO poster supplement for additional information.
Use of Artificial Intelligence in Our Hiring Process
DataRobot uses approved AI\-powered tools to support the hiring process in selected regions. These tools may assist in writing job descriptions, reviewing applications, assessing qualifications, and evaluating candidate materials. All decisions regarding applications are made by members of the DataRobot team.
All applicant data submitted is handled in accordance with our Applicant Privacy Policy.
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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At DataRobot, 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 $218,750 based on 3,817 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $120,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
DataRobot AI Hiring
DataRobot has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Boston, MA, US.
Location Context
AI roles in Boston pay a median of $210,000 across 97 tracked positions. That's 3% below the national 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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