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Machine Learning Professor Jobs (NOW HIRING)

GPTZero

Machine Learning Intern

New York, NY · On-site

What we're looking for At GPTZero, we ensure that machine learning models are created for the ... Karthik Narasimhan (Princeton NLP Professor, co-author of OpenAI's original GPT paper) * Emad ...

Voltai

Machine Learning Engineer

Palo Alto, CA · On-site

We are a team of previous Stanford professors, SAIL researchers, Olympiad medalists (IPhO, IOI, etc.), CTOs of Synopsys & GlobalFoundries, Head of Sales & CRO of Cadence, former US Secretary of ...

Voltai, Inc

Machine Learning Engineer

Palo Alto, CA · On-site

We are a team of previous Stanford professors, SAIL researchers, Olympiad medalists (IPhO, IOI, etc.), CTOs of Synopsys & GlobalFoundries, Head of Sales & CRO of Cadence, former US Secretary of ...

Voltai, Inc

Machine Learning Engineer

Palo Alto, CA

We are a team of previous Stanford professors, SAIL researchers, Olympiad medalists (IPhO, IOI, etc.), CTOs of Synopsys & GlobalFoundries, Head of Sales & CRO of Cadence, former US Secretary of ...

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Machine Learning Professor information

What are the key skills and qualifications needed to thrive as a Machine Learning Professor, and why are they important?

To thrive as a Machine Learning Professor, you need deep expertise in machine learning algorithms, mathematics, and data science, typically supported by a Ph.D. in computer science or a related field. Familiarity with programming languages like Python and R, as well as tools such as TensorFlow, PyTorch, and academic research platforms, is essential. Exceptional communication, mentorship abilities, and enthusiasm for teaching help inspire and guide students and peers. These skills ensure effective research output, high-quality teaching, and the development of future leaders in the field.

How does a Machine Learning Professor typically balance research, teaching, and mentorship responsibilities?

Machine Learning Professors usually split their time between conducting original research, teaching undergraduate and graduate courses, and mentoring students. Balancing these responsibilities can be challenging, particularly when managing multiple research projects and supervising student theses. Professors often collaborate with industry partners and other academic departments, which enhances research opportunities but also adds to their workload. Effective time management and prioritization are key to thriving in this dynamic environment, and universities often provide some flexibility in teaching loads or research support to help maintain this balance.

What does a Machine Learning Professor do?

A Machine Learning Professor teaches university-level courses on machine learning, data science, and related topics. They conduct original research in the field, often publishing their findings in academic journals and presenting at conferences. In addition to lecturing, they mentor students, supervise theses or dissertations, and may collaborate with industry partners on research projects. Their work helps advance the understanding and applications of machine learning.
What cities are hiring for Machine Learning Professor jobs? Cities with the most Machine Learning Professor job openings:
What states have the most Machine Learning Professor jobs? States with the most job openings for Machine Learning Professor jobs include:
Machine Learning Professor jobs near you
Infographic showing various Machine Learning Professor job openings in the United States as of May 2026, with employment types broken down into 1% As Needed, 75% Full Time, and 24% Part Time. Highlights an 98% Physical, 1% Hybrid, and 1% Remote job distribution.

Machine Learning Intern

GPTZero

New York, NY • On-site

Internship

Medical, Dental, PTO

Posted 11 days ago

Job description

GPTZero is on a mission to restore trust and transparency on the internet. As the leading AI detection platform, we empower educators, students, journalists, marketers, and writers to navigate the evolving landscape of AI-generated content. With millions of users and institutions relying on us, we're building a category-defining company at the intersection of AI and information integrity.
Our team comes from high-performing engineering cultures, including Meta, Perplexity, AWS, Affirm, and leading AI research labs, including Princeton, Caltech, and Vector Institute.
What we're looking for
At GPTZero, we ensure that machine learning models are created for the benefit of humanity, not the other way around.
In this role, you'll build the next-gen platform to verify the origin, quality, and factuality of the world's information. The ideal candidate is someone who has a history of ML research, possesses a great product sense, and is also an excellent software engineer. You'll be working on a fast-paced team of passionate builders, creating industry-defining software that has attracted over 2M users globally. Past intern projects have been the focus of demos to VCs and state-level policy leaders.
Responsibilities
Depending on the candidate, the role can be targeted more towards ML engineering or research (publication-focused)
  • Design, train, and fine-tune state-of-the-art large language models
  • Develop AI agents combined with retrieval-augmented language models
  • Build efficient and scalable ML training and inference systems
  • Stay up-to-date with the latest literature and emerging technologies to solve novel problems
  • Work closely with product and design teams to develop intuitive applications that create societal impact
Qualifications
  • Proficiency in Python and PyTorch
  • Experience pushing or implementing the cutting-edge in machine learning
  • Self-starter and hungry learner
  • Highly motivated to make positive societal impact
  • Ability to wear multiple hats
  • Visa for work in Canada or US
  • Bonus:
    • strong open-source portfolio
    • publications at top-tier ML venues
    • experience working in an early-stage startup environment
    • understanding of how machine learning models fail in the wild

Who you'll be joining
Our Team
You will be working directly with
  • Alex (our CTO) R&D at Uber self-driving division and Facebook, 3 patents in ML
  • George (our AI research lead) PhD from University of Toronto and ex-AWS research.
  • Olivia (our head of design) on translating your research into outputs for millions of users.
  • Edward (our CEO, ex-Bellingcat, Microsoft, BBC investigative journalism) to craft the messages we send to our community, and shape the GPTZero brand.

Additionally, you will be working with an experienced (eg. ex-Google, Meta, Microsoft, Bloomberg ML, Uber, Vector, MILA), diverse (eg. an engineering team with both Y-combinator and Obama scholarship recipients, a designer with art featured in the Met), and driven (eg. an operator who has scaled a company to 100M+ revenue and is committed to doing it again) group of individuals, described by one investor as one of the strongest founding teams seen in their career.
Together, we are committed to making a permanent impact on the future of writing, and on humanity.
Our Angels and Advisors
  • Tom Glocer (Legendary Reuters CEO)
  • Mark Thompson (Legendary NYT CEO and current CNN chief executive)
  • Jack Altman (CEO of Lattice, brother of Sam Altman)
  • Karthik Narasimhan (Princeton NLP Professor, co-author of OpenAI's original GPT paper)
  • Emad Mostaque (CEO of Stability AI)
  • Doug Herrington (CEO of Worldwide Amazon Stores)
  • Brad Smith (President of Microsoft)
  • Tripp Jones (Partner at Uncork Capital)
  • Ali Partovi (co-founder of Code.org, early investor in Dropbox and Airbnb)
  • Russ Heddleston (CEO of Docsend)
  • Alex Mashrabov (Snapchat, Director of AI)
  • Faizan Mehdi (Affinity, Director of Demand Generation)
Our Perks
  • Health, dental, and mental health benefits
  • Hybrid work in Toronto and NYC offices
  • Competitive salary
  • Flexible PTO
  • Regular company retreats
  • Mentorship opportunities with our world-class team, advisors, and investors
  • Wellness and learning stipend

At GPTZero, our recruiting team is involved in every step of the hiring process. We use AI-based tools (such as Endorsed.ai and Juicebox.ai) to help us to accelerate candidates at the resume review stage by marking when candidates met certain key criteria. These tools are never the final say in a hiring decision - humans are.

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