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Internship Applied Scientist Machine Learning Jobs in Arizona

Relativity

Senior Manager, Applied Science

Phoenix, AZ · On-site

The Applied Science team serves this mission by building bold and ambitious AI systems. We are ... Machine-learning rigor, grounded in data understanding: careful evaluation, error analysis, and the ...

New

Relativity

Senior Manager, Applied Science

Phoenix, AZ · On-site

The Applied Science team serves this mission by building bold and ambitious AI systems. We are ... Machine-learning rigor, grounded in data understanding: careful evaluation, error analysis, and the ...

New

Globe Telecom, Inc.

Machine Learning Operations Expert

Globe, AZ · On-site

$50.25 - $68.75/hr

Collaboration - collaborate with data scientists, data engineers, insighters and other stakeholders ... Experience in machine learning, data science, or software engineering roles. * Experience in MLOps ...

Lifekind Health

Data Scientist

Scottsdale, AZ · On-site

$80K - $120K/yr

... science, machine learning, or applied analytics * Strong Python + advanced SQL skills for data manipulation, modeling, and EDA * Experience developing and evaluating ML models in real-world ...

Prosum Inc.

Sr. Machine Learning Engineer

Phoenix, AZ · On-site

$150K - $198K/yr

Partner with machine learning scientists, computer vision engineers, hardware engineers, and software developers to deliver integrated AI solutions. Please view our Privacy Policy

New

Quora

$139K - $168K/yr

D. in Computer Science, Engineering or a related technical field * Strong understanding of ... Previous software engineering experience via an internship, work experience, or coding competition

Intel

Sr. Machine Learning Engineer

Phoenix, AZ · On-site

$103K - $142K/yr

Machine Learning Engineer / Data Scientist** to join our team, working on agent harness research ... internship experiences and or schoolwork/classes/research. Benefits at Intel Our total rewards ...

Unitek Learning

People Data Scientist

Tempe, AZ · On-site

This role leverages predictive modeling, experimentation, machine learning, and workforce analytics ... Minimum of 5 years of experience in data science, people analytics, workforce analytics, applied ...

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Internship Applied Scientist Machine Learning information

What types of projects do internship applied scientists in machine learning typically work on, and how do they contribute to the team's goals?

Internship Applied Scientists in Machine Learning often collaborate with multidisciplinary teams to tackle real-world problems using data-driven approaches. Typical projects might include developing and fine-tuning machine learning models, conducting experiments to validate hypotheses, or assisting in the deployment of algorithms into production systems. Interns are expected to contribute fresh perspectives, help with data preprocessing, and perform thorough model evaluations. Through these projects, interns gain hands-on experience while directly supporting the team's research and product development objectives.

What is the difference between Internship Applied Scientist Machine Learning vs Internship Data Scientist?

AspectInternship Applied Scientist Machine LearningInternship Data Scientist
Required CredentialsRelevant degrees in Computer Science, Data Science, or related fields; knowledge of ML frameworksDegrees in Statistics, Data Science, or related fields; strong analytical skills
Work EnvironmentResearch and development teams, focus on ML model developmentBusiness teams, focus on data analysis and insights
Employer & Industry UsageTech companies, AI-focused organizationsVarious industries including tech, finance, healthcare
Comparison Search IntentUnderstanding roles in ML research and developmentUnderstanding data analysis and business insights roles

Internship Applied Scientist Machine Learning roles focus on developing and applying machine learning models, often in research settings. In contrast, Internship Data Scientist positions emphasize analyzing data to generate insights for business decisions. Both roles require strong analytical skills and relevant educational backgrounds, but they differ in their primary focus and work environment.

What are the key skills and qualifications needed to thrive as an internship applied scientist in machine learning, and why are they important?

To thrive as an Internship Applied Scientist in Machine Learning, you need a solid background in mathematics, statistics, and computer science, often supported by coursework or research experience in machine learning and data analysis. Familiarity with tools such as Python, TensorFlow, PyTorch, and experience working with large datasets are highly valued, along with knowledge of version control systems like Git. Strong problem-solving skills, curiosity, and the ability to communicate complex concepts clearly set top candidates apart. These competencies are crucial for effectively designing, implementing, and presenting machine learning solutions that address real-world challenges.

What does an internship applied scientist in machine learning do?

An Internship Applied Scientist in Machine Learning works on real-world projects involving the design, development, and evaluation of machine learning models and algorithms. Their responsibilities typically include data analysis, building predictive models, experimenting with new techniques, and collaborating with engineers and researchers to solve complex problems. Interns gain hands-on experience with tools like Python, TensorFlow, or PyTorch, and contribute to advancing the company's AI capabilities. The role requires a strong foundation in mathematics, statistics, and computer science, as well as the ability to communicate findings to both technical and non-technical stakeholders.
What are the most commonly searched types of Applied Scientist Machine Learning jobs in Arizona? The most popular types of Applied Scientist Machine Learning jobs in Arizona are:
What are popular job titles related to Internship Applied Scientist Machine Learning jobs in Arizona? For Internship Applied Scientist Machine Learning jobs in Arizona, the most frequently searched job titles are:
What job categories do people searching Internship Applied Scientist Machine Learning jobs in Arizona look for? The top searched job categories for Internship Applied Scientist Machine Learning jobs in Arizona are:
What cities in Arizona are hiring for Internship Applied Scientist Machine Learning jobs? Cities in Arizona with the most Internship Applied Scientist Machine Learning job openings:
Internship Applied Scientist Machine Learning jobs near you

Senior Manager, Applied Science

Relativity

Phoenix, AZ • On-site

Other

Posted yesterday

New

Job description

Posting Type

Remote/Hybrid

Job Overview

The Work
Every legal matter is its own experiment. An attorney arrives with a theory of the case; the evidence arrives as hundreds of thousands of documents, sometimes millions, that no one has read and no model has seen. Somewhere in the cross product of the two are the answers that decide lawsuits, investigations, and livelihoods. Finding them quickly and defensibly, with the integrity and credibility attorneys can rely on, is the problem we own. We solve it creatively and rigorously.
Relativity is a data-centered, AI-native legal technology company, and Applied Science builds the AI inside Relativity aiR. We launched aiR in 2023 and have now run commercial generative AI in the legal domain for more than three years, powering work that includes the largest investigations in the world. Our systems are distinguished by the data they operate over (more than 93 petabytes) and the work they have done: over 190 million AI review decisions, backed by more than 1 billion generative sub-analyses in 2026 alone. The team is as distinctive as the data: legal experts, all former litigators, work directly inside Applied Science.
At Relativity, our mission is to Organize data. Discover the truth. Act on it. The Applied Science team serves this mission by building bold and ambitious AI systems. We are curious, dedicated, and humble. We understand complexity, uphold rigor, and measure relentlessly. We build and ship with pace. Above all, we are interdisciplinary collaborators and team players.
We're looking for a Senior Manager, Applied Science to lead a team expanding the aiR agentic harness for greater capability and reliability

Job Description and Requirements

Capable and Reliable

Two requests can look nearly identical and be worlds apart. "See if you can find me an example of this" needs a capable system: it finds the example or it doesn't. "Conduct a reasonable search for any and all documents responsive to this request" is a different kind of promise. Its answer spans a corpus no one will ever read end-to-end. So the system's process, as much as its output, has to earn the trust of the professionals who rely on it.

That property is reliability. It decomposes into consistency, robustness, calibration, and safety: systems that behave tomorrow the way they did today, degrade predictably under stress, know how confident they should be, and check their own work. Before aiR returns an analysis, it validates its citations and runs internal consistency checks; when a check fails, it refuses to answer. It has refused more than a million times so far in 2026, and we count every one as a success: an error caught before it reached a user.

Your team will build for both, and you'll define the standard for how.

What You'll Do

  • Lead and grow a team of applied scientists: hire, coach, set direction, and develop people toward their best work.
  • Set the technical and scientific bar. The work stays hands-on: you'll shape architectures, review designs and evaluations, and dig into hard problems alongside your team, close enough to the science to lead by example.
  • Own AI system readiness end-to-end, from problem framing through evaluation, error analysis, efficacy studies, and production monitoring, so that what ships is dependable and defensible.
  • Choose the right problems. Current examples range from agentic assistants that extend what a legal professional can do, to large-scale review and analysis that must stay reliable across hundreds of thousands of documents per matter. You'll help decide where we invest.
  • Partner with product, engineering, design, customer-facing teams, and the legal experts on the team to take ideas from proof-of-concept to production at scale.
  • Communicate with precision to your team, to leadership, and to customers: translate technical nuance into decisions people can act on, and carry the customer's voice back into the work.
  • Represent Relativity at industry conferences, events, and with customers.

What You Bring

  • A master's or PhD in computer science or another quantitative discipline (or equivalent professional experience), and at least 6 years in applied AI/ML, including at least 1 year as a people leader.
  • Deep applied AI/ML and deployment engineering experience: you've built production-ready AI systems and owned them through their production lifecycle, partnering with engineering teams to keep them running reliably.
  • Fluency with modern generative AI as a component of larger systems, and sound judgment about what it can and cannot do reliably.
  • Machine-learning rigor, grounded in data understanding: careful evaluation, error analysis, and the statistical thinking to draw only the conclusions your data supports.
  • Strong software-engineering judgment and programming skill.
  • An ownership mindset that extends beyond your immediate team.

Nice to Have

  • An interest in legal technology and the justice system.
  • Experience hiring and growing a team.
  • Experience developing information retrieval systems or agentic harnesses.
  • An interest in building reliable AI systems at scale.

Why Relativity Applied Science

This is the place where your curiosity, dedication, and talent will build products that power the pursuit of justice around the world.

Relativity is committed to competitive, fair, and equitable compensation practices.

This position is eligible for total compensation which includes a competitive base salary, an annual performance bonus, and long-term incentives.

The expected salary range for this role is between following values:

$208,000 and $312,000

The final offered salary will be based on several factors, including but not limited to the candidate's depth of experience, skill set, qualifications, and internal pay equity. Hiring at the top end of the range would not be typical, to allow for future meaningful salary growth in this position.

Required Skills:

Artificial Intelligence (AI), Business Intelligence (BI), Data Analysis, Database Management, Data Governance, Data Intelligence, Data Visualization, Information Management, Machine Learning (ML), Strategic Planning

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