Applied Scientist (ML)
$190k - $275k USD
Role
As an Applied Scientist (ML) at Samaya, you will collaborate closely with our product and engineering teams, and use cutting-edge ML research to transform how users interact with Samaya in their daily workflows. You'll drive impact across the entire ML lifecycle: from problem formulation and system analysis to data collection, benchmark development, model training, and production deployment. You’ll also have opportunities to publish your work and deliver impact to the ML community. Your expertise will advance our capabilities in these critical technical domains:
- Retrieval, ranking and RAG
- LLM post-training and reinforcement learning
- AI agents for knowledge workflows
- ML benchmarks
Your work has the potential to transform the following key Samaya products and deliver impacts to tens of thousands of professional users:
Instant QA: Our custom-built Question Answer system using state of the art in house models, seamlessly trained to work together to enable instant expert intelligence.
Agents: You will enable expert-level agentic workflows to automate comprehensive knowledge work and enable AI tools that work with experts to gain new insights.
You can read some of our previous ML work at: https://samaya.ai/blog/ and https://samaya.ai/research/.
Responsibilities
- Formulate an ML problem from product requirements.
- Analyze existing ML systems for their limitations, and propose and validate novel methodologies to improve upon existing systems.
- Create and productionize cutting-edge research prototypes for knowledge work at scale.
- Build novel ML evaluation datasets that serve as crucial criteria for production feature rollouts.
- [Optionally] Mentor ML interns and publish your research findings with the community.
Experience
Required
- 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.
- 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.
Preferred
- 2+ years of experience in an industry applied ML research environment
- Familiarity with retrieval-augmented generation, reasoning, LLM training and reinforcement learning techniques.
Compensation
The cash compensation range for this role is $190,000 - $275,000.
Final offer amounts are determined by multiple factors, including experience and expertise, and may vary from the amounts listed above.
In addition to the base salary, we may consider equity as part of our total compensation package.
Benefits
Health:Access comprehensive health insurance, including medical, dental, vision, flexible spending account (FSA), and short-term disability.
Wealth: Support for your long-term financial wellbeing with a 401(k) and pre-tax benefits (e.g. commuting).
Rest: Enjoy flexibility to rest and recharge as needed, with unlimited PTO (Paid Time Off).
Flexibility:Work flexibly with a hybrid setup - typically team members spend a minimum of three days in the office per week.
Travel:Grow and connect with a travel budget that encourages conference attendance, customer visits, and team gatherings.
Equipment: Create your ideal workspace with an office Equipment allowance to set up what works best for you.
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.
Our Operating Principles
- 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.