We are seeking a highly motivated Machine Learning Research Intern to work on cutting-edge research in the fields of Natural Language Processing (NLP) and Machine Learning (ML).
At Samaya, our product helps improve the productivity of professional workers via key techniques such as retrieval-augmented generation, LLM agents with advanced reasoning capabilities, etc. Therefore, the primary focus of this internship is to conduct novel research that advances these fields, with the goal of publishing in top-tier ML and NLP conferences.
The intern will collaborate closely with our team of experienced researchers on topics aligned with the company’s mission.
This role is ideal for a PhD or advanced Master’s student passionate about fundamental research and real-world AI applications. The intern will also have the opportunity to reshape Samaya’s key product roadmap using their research.
We are committed to ensuring an equitable selection process for everyone and welcome applicants from varied backgrounds to enrich our team. If you require accommodations or adjustments during our recruitment process, please inform us.
We do not discriminate on the basis of race, color, religion, sex (including pregnancy and gender identity), national origin, political affiliation, sexual orientation, marital status, disability, genetic information, age, membership in an employee organization, retaliation, parental status, military service, or other non-merit factors.
Samaya is building the first Human-AI Knowledge Network – an information ecosystem to transform expert knowledge work.
Expert knowledge work drives trillions of dollars of economic activity. Teams of human experts painstakingly synthesize insights and drive decisions from vast volumes of noisy, real-time information.
For example, in financial services, expert analysts hone in on key economic insights to inform high-stakes investment decisions. Although the volume of information continues to grow, past technology has played only a passive role, and today's human experts struggle with information overload.
We believe AI should take an active role in complex knowledge work, becoming an equal collaborator to human experts.
However, such an AI will not "simply emerge" through scaling or the development of general-purpose LLMs.
At Samaya, we are developing an AI system purpose-built for "expert intelligence" – for reasoning and interacting with real-world information networks. Our AI is designed to consume dense, noisy real-time information, distill key insights, form connections, contextualize findings, and make expert predictions.
We are building a future where our expert intelligence AI can transform global knowledge work for the better.
Our users rely on us to do their jobs. We exist because our users trust us to help them achieve their goals. In return for this trust, we keep their needs as our top priority.
We are high achievers with a drive to succeed. We build strong bonds over this shared drive. We dive in to help when one of us needs it.
We’re kind to each other and boost each other to succeed and grow professionally and personally. We build trust with each other by making commitments and consistently delivering on them.
This trust means we genuinely support each other, embracing feedback as a tool for growth and improvement.
We win by operating this way, as one team.
Bias for action makes us build and learn quickly. Iterating fast requires clarity on what outcomes we are targeting and why.
Prioritizing the important things, taking full ownership and initiative, making fast initial progress, and rapid iterations lead to the best outcomes.
We pursue novel insights, challenging the status quo and reimagining how things are done.
We aren’t attached to the past when improving our product and how we work in the future.
We actively invest time in innovation, thinking “outside the box” to consistently raise our standards.
We are committed not to a person, an idea, or an opinion but to continuously making progress toward our goals.
Sometimes, our goals are ambiguous; in those moments, we iterate, learn, and move on to the next inquiry.
We ask the tough questions with kindness, dropping our egos in our pursuit of evidence.
For our business goals, we learn from our users.
For our scientific goals, we build understanding through rigorous experimentation, research, and observation.
For our personal goals, we embrace candid feedback and collaborative learning to guide our progress.