ML Research Intern

Samaya AI; Mountain View (CA); On-site; USD 41,328 - 68,880; Full time

Benefits offered by this job

Flexible work hours

Collaborative environment

Research opportunities

Job summary

A leading AI research firm is seeking a highly motivated Machine Learning Research Intern. This role involves conducting innovative research in Natural Language Processing and Machine Learning, aimed at publishing in top-tier conferences. Ideal for a PhD or advanced Master's student, candidates will collaborate with experienced researchers and have the opportunity to influence the product roadmap using their research findings. Strong programming skills in Python and a background in deep learning are essential.

Qualifications

• Pursuing a PhD or Master’s in a relevant field.
• Strong background in deep learning and NLP.
• Proven track record of publications in top AI/NLP conferences.

Responsibilities

• Conduct original research in LLM training and evaluation.
• Develop new models and methodologies for AI applications.
• Collaborate with researchers to design novel methods.
• Present findings through research papers and talks.

Skills

Deep learning

Natural Language Processing

Python

PyTorch

Transformers

Research publications

Education

PhD or Master’s degree in Computer Science or related field

Tools

Deep learning frameworks

Job description

Mountain View, California, United States

Role

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 delivers value to one of the most high‑stake environments of AI applications - helping financial professionals make knowledge‑informed decisions via key techniques such as RAG, RL and AI agents with advanced tool calling and long‑horizon reasoning, etc. 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.

Responsibilities

• Conduct original research in the broad areas of LLM training and evaluation, RAG, RL and AI agents, leading to publications in top ML/NLP conferences.
• Develop and experiment with new models, algorithms, and methodologies to improve AI agents performance in real‑world tasks.
• Collaborate with in‑house researchers to design, implement, and evaluate novel methods in alignment with the company’s mission.
• Present findings internally and externally through research papers and technical talks.

Qualifications

• Currently pursuing a PhD or Master’s degree in Computer Science, Machine Learning, NLP, or a related field.
• Strong background in deep learning, large language models, and NLP techniques. Deep domain knowledge with RAG, RL and AI agent training and evaluation is a plus.
• A strong track record of first‑author publications in top AI/NLP conferences (e.g., NeurIPS, ICML, ACL, EMNLP).
• Proficiency in Python and deep learning frameworks such as PyTorch or Transformers, and strong coding skills.

Inclusive Hiring

Interview Accommodations: 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.

Equal Opportunity Employer: 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 factor.

Visa Sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. If we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.

About Samaya

Samaya builds Expert AI Agents that turn information from the global financial market into investment conviction.

The global financial market is the largest and most valuable information ecosystem in the world, connecting billions of people, influencing every type of productive human activity, and driving tens of trillions of dollars of value. At its core is investment decision‑making: identifying areas of productive activity, allocating resources, carried out by millions of people across the globe.

But that process is at a breaking point. The past two decades have brought an exponential increase in market complexity: more information sources, more asset types, more disruptive themes like AI reshaping every corner of the market. For investors, this means exponentially more depth, breadth, and speed required on every decision.

The response is a forced tradeoff: zoom in on a sector or basket of companies and manage the flood, but lose sight of adjacent dynamics that move markets. Or zoom out to track broad themes, but lose the needle‑in‑a‑haystack details that drive precise decisions. No market sector evolves in isolation, and this lack of a simultaneously zoomed‑in and zoomed‑out picture costs hundreds of billions in missed or suboptimal investment decisions every year.

Samaya was founded to reimagine investment decision‑making across the global financial market. General‑purpose AI can’t reason about cause and effect across complex economic systems, embed firm‑specific context, or execute reliably over long‑horizon workflows. We built something different: a purpose‑built AI system combining proprietary financial reasoning models, a long‑horizon execution engine with persistent memory, and full auditability. Built by a team from Google DeepMind, Meta, Microsoft, and Stanford with 100+ papers and 50k+ citations, it achieves 98% accuracy on financial reasoning tasks where generic LLMs reach 53%. The result is AI that learns how each investor thinks and seamlessly takes them from information to conviction.

Our user base has scaled to 10,000+, with partnerships spanning top financial institutions worldwide, including Morgan Stanley. We’re backed by $43.5M in Series A funding led by NEA, with investors including Eric Schmidt, NVIDIA, Databricks, Yann LeCun, Jeff Dean, Marty Chavez, and Mark Cuban.

• Put Users first. 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 users place in us, we keep their needs as our top priority.
• Win as a collective. 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.
• Focus and iterate quickly. 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.
• Innovate Relentlessly. 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.
• Prioritize Outcomes over Egos. We are committed not to a person, an idea, or an opinion but to continuously making progress to 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, our understanding is built through rigorous experimentation, research, and observation. For our personal goals, we embrace candid feedback and collaborative learning to guide our progress.

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