The "Research" and "Researcher" Job Titles Changes

If you’ve been on the hunt for a research-related job lately, you’ve probably noticed something odd: job boards are flooded with titles like Research Engineer, Research Scientist, and other research variables. These roles sound research-adjacent but often have little to do with user research, insights, or even traditional market research. Instead, they lean heavily toward machine learning (ML), artificial intelligence (AI), and data science. You may have encountered this shift when interacting with people working in AI or large language models (LLMs).

For example, a product manager stakeholder on a call today with a research insights vendor mentioned he "manages researchers". I had to clarify that he meant a different type of researcher—not an insights researcher.

My interest in names and terminology runs deep, and this shift in research job titles is particularly interesting to me. I conceived of the UX Lexicon, a project focused on standardizing language in the research and product design industry. The way we name things has a direct impact on how they are understood, categorized, and valued.

The current shift in 'research' job titles highlights just how much language matters—and how easily confusion can arise when terminology is repurposed or redefined without clear distinctions.

So what’s going on? And what does it mean for those of us in the insights and user research space?

A Shift in the Meaning of "Research"

Over the last 18 months, the term "researcher" has expanded—and in some cases, been co-opted—to describe roles that have little to do with what user researchers and insights professionals do. Companies are hiring more "Research Engineers" and "Research Scientists" than ever, but these positions prioritize coding, algorithm development, and data analysis over qualitative or human-centered research.

Take this job description for a Research Engineer at OpenAI:

"As a research engineer, you will research and develop improvements to our models. Our team works in research areas combining reinforcement learning and products. We're looking for individuals with strong ML engineering skills."

Clearly, this is not a role for someone conducting user interviews or synthesizing insights—it’s for someone optimizing AI models.

Similarly, consider this posting for Research Operations at Anthropic:

"The LLMO/Tokens team collaborates with product and research teams inside Anthropic to incubate groundbreaking research and deploy advanced machine learning algorithms to improve Anthropic products and services."

While the title contains "research," the responsibilities focus on operational efficiencies, partnerships, and deployment—not traditional research.

Another example is the Research Internship at Cohere:

You May Be a Good Fit If You... Are currently pursuing, or in the process of obtaining, a PhD in Machine Learning, NLP, Artificial Intelligence, or a related discipline... Have experience using large-scale distributed training strategies, data annotation and evaluation pipelines, or implementing state of the art ML models.

Again, this role emphasizes technical skills in AI and ML rather than user-centric research methodologies.

Why This Causes Confusion

For practitioners and job seekers in the insights and user research field, this shift can create confusion and other challenges:

Job Search Frustration: Searching for "researcher" or "research" roles now yields a mix of AI-focused and insights-focused positions, making it harder to filter through relevant opportunities.
Misaligned Expectations: Candidates applying to "Research" roles may find themselves in interviews for highly technical positions requiring Python, TensorFlow, or advanced statistical modeling. Vendors interacting with AI clients may misunderstand the the type of research talent on a team.
Diminished Visibility for User and Other Insights-Related Research: As AI-driven research roles take center stage, traditional user and other insights-related research roles may become harder to distinguish and advocate for within organizations.

The (Few) Upsides

While the rebranding of "research" can be frustrating, there are some potential benefits:

New Career Pathways: For researchers interested in AI and ML, there may be an opportunity to pivot into these new research-adjacent roles.
Greater Industry Awareness: The influx of research-focused AI jobs could spark more conversations about the value of insights-driven research in AI development.

What Can We Do About It?

If you're an insights professional navigating this shift, here's some food for thought:

Use More Specific Terms in Communications and Searching – Instead of just "researcher," try "user researcher," "UX researcher," or "market research specialist."
Clarify Roles Before Applying or Responding – Read job descriptions carefully to identify whether a role aligns with your skills. Seek clarity when someone refers to a "researcher."
Advocate for Clearer Titles – Encourage companies to be explicit in their job titles. If a role is engineering-heavy, it should be labeled as such, likewise for insights-related roles. Descriptors, such as staff, marketing, UX before the word "researcher" also help to distinguish the position from more engineering focused roles.
Educate Hiring Teams and Managers – If your company is hiring new talent or vendors, help distinguish between different types of research and ensure roles are properly categorized.

For sure, the research field is evolving, but that doesn’t mean insights professionals need to get lost in the shuffle. By staying aware and adapting to these changes, we can continue to champion the value of user research and the insights industry in a world that seem's to be dominated by AI-driven roles.

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We try to alternate between a theme and UX/UXR jobs, events, classes, articles, and other happenings every few weeks. Thank you for all of the feedback. Feedback is a gift, and we continue to receive very actionable input on how to make Fuel Your Curiosity more meaningful to you.

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PS: Have you seen this new trend in research titles? What do you think about it? Hit reply to share your feedback!

‘Research’ Means Something Different Now—Here’s Why