Interested in this AI/ML Engineer role at Datasite?
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About This Role
Datasite and its associated businesses are the global center for facilitating economic value creation for companies across the globe. From data rooms to AI deal sourcing
and more. Here you’ll find the finest technological pioneers: Datasite, Blueflame AI, Grata, and Sherpany. They all, collectively, define the future for business growth.
Apply for one position or as many as you like. Talent doesn’t always just go in one direction or fit in a single box. We’re happy to see whatever your superpower is and find the best place for it to flourish.
Get started now, we look forward to meeting you..
Job Description:
This is a 10–12 week, full\-time internship (40 hours per week) based in our Minneapolis office. We are seeking recent college graduates who completed a degree in Computer Science, Mathematics, Engineering or Data Science preferrably between May and August 2026\. High\-performing interns may be considered for future full\-time opportunities within Datasite's AI Transformation organization.
Please note: This position is not eligible for employment visa sponsorship. Therfore, applicants must be legally authorized to work in the US without requiring current or future employer\-sponsored support.
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Datasite's Transformation Office leads enterprise AI adoption, operational transformation, and strategic change across the business. Our Forward Deployed Engineers (FDEs) partner directly with business teams to identify opportunities, rapidly prototype AI solutions, and help bring those solutions into production.
As an AI Transformation / FDE Intern, you'll work alongside experienced FDEs on real business initiatives. You'll gain hands\-on experience applying AI, automation, and modern engineering tools to solve operational challenges while learning how technology creates measurable business value.
This internship is designed for someone with strong technical fundamentals, intellectual curiosity, and a builder's mindset. Enterprise experience isn't expected—we're looking for someone eager to learn, contribute, and grow.
WHAT YOU'LL LEARN \& CONTRIBUTE
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- Learn the Discovery Process: Partner with FDEs during stakeholder meetings to understand business workflows, identify pain points, and learn how AI opportunities are identified and prioritized.
- Build AI Solutions: Develop rapid prototypes using AI models, APIs, Python, workflow automation platforms, and other modern tools. Iterate quickly based on business feedback.
- Explore Process Improvements: Analyze workflows across business functions to identify opportunities where AI and automation can improve productivity, quality, and customer experience.
- Support Delivery: Help document use cases, participate in testing, prepare demonstrations, and support successful deployment of AI solutions.
- Communicate Business Impact: Learn to explain technical work in business terms by focusing on measurable outcomes such as time savings, quality improvements, and efficiency gains.
HOW YOU'LL WORK
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You'll be paired with experienced FDEs who will mentor you through active AI initiatives. You'll participate in team standups, collaborate with cross\-functional stakeholders, contribute to prototype development, and present your work throughout the internship.
This is a collaborative, fast\-moving environment where curiosity, initiative, and continuous learning are valued as much as technical ability.
WHAT WE'RE LOOKING FOR
Required
- Recently completed a degree in Computer Science, Mathematics, Engineering or Data Science.
- Experience with Python (strongly preferred) or another modern programming language.
- Familiarity with APIs, SQL, databases, or workflow automation tools.
- Strong analytical and problem\-solving skills.
Preferred
- Experience with AI/ML, LLMs, or AI\-powered development tools.
- Academic, personal, or internship projects demonstrating automation or AI applications.
- Experience with Git, cloud platforms, or workflow tools such as n8n.
- Interest in business process improvement and enterprise AI.
WE'RE ESPECIALLY INTERESTED IN CANDIDATES WHO...
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- Enjoy solving ambiguous problems.
- Build things outside of class.
- Ask thoughtful questions before jumping to solutions.
- Learn new technologies quickly.
- Communicate clearly with technical and non\-technical audiences.
- Take ownership and follow through.
WHAT SUCCESS LOOKS LIKE
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By the end of the internship, you will have:
- Participated in real AI Transformation initiatives.
- Built and demonstrated working AI prototypes.
- Worked alongside business leaders and technical teams.
- Learned how enterprise AI projects move from idea to implementation.
- Developed the skills needed to succeed as a Forward Deployed Engineer.
- Positioned yourself for consideration for a future full\-time FDE opportunity.
The base salary range represents the estimated low and high end for this position based on a good faith assessment of the role and market data at the time of posting. Consistent with applicable law, each candidate’s compensation offer may vary and will be determined based on but not limited to, your geographic region, skills, qualifications, and experience along with the requirements of the position. This position may be eligible for bonuses, commissions, or overtime if applicable. Benefits include health insurance (medical, dental, vision), a retirement savings plan, paid time off, and other employee benefits. Specific details will be provided during the interview process. Datasite reserves the right to modify this pay range at any time.
$34,700\.00 \- $38,900\.00
Our company is committed to fostering a diverse and inclusive workforce where all individuals are respected and valued. We are an equal opportunity employer and make all employment decisions without regard to race, color, religion, sex, gender identity, sexual orientation, age, national origin, disability, protected veteran status, or any other protected characteristic. We encourage applications from candidates of all backgrounds and are dedicated to building teams that reflect the diversity of our communities.
Salary Context
This $34K-$38K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Datasite, 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. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($36K) sits 83% below the category median. Disclosed range: $34K to $38K.
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.
Datasite AI Hiring
Datasite has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span New York, NY, US, Minneapolis, MN, US. Compensation range: $38K - $169K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 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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