Senior Research Scientist
Job description
About the role
This position centers on transforming raw information into actionable decisions through rigorous data science and engineering. You will own the design and execution of research that measures what truly moves the field, prioritizing real-world impact over purely academic publication counts. A core part of your work will involve collaborating across software, hardware, and algorithmic domains to unlock system-wide efficiency gains for intelligent interactions. You will be responsible for measuring real-world impact to directly guide algorithm development, embedding product thinking into the research lifecycle. The role requires a deep obsession with model interaction with the world, pushing boundaries of interface beyond what models merely know. You will define intelligence through its ability to adapt, requiring a constant focus on interface design as much as model capability. Ultimately, you will drive the creation of systems that learn and evolve in real-time as they interact with the world.
Key facts
What you'll do
Testing research ideas through working products measures what moves the field, prioritizing real-world impact over paper count.
Collaborating across software, hardware, and algorithmic domains unlocks system-wide efficiency gains for intelligent interactions.
Measuring real-world impact guides algorithm development, because product thinking is inseparable from research.
Model interaction with the world defines intelligence, requiring deep obsession with interface beyond what models know.
Designing and running live experiments to validate hypotheses about adaptation and efficiency in production environments.
Architecting data pipelines that feed real-time learning systems, ensuring robustness and scalability across the full ML stack.
Translating complex algorithmic optimization problems into concrete system requirements for engineering partners.
Championing model efficiency techniques such as RLHF and finetuning to reduce latency and improve real-world performance.
Owning the end-to-end workflow from data collection and labeling through deployment and monitoring of adaptive models.
Defining and tracking key metrics that quantify the success and impact of intelligent systems in the wild.
Leading technical investigations to diagnose issues in model behavior and proposing data-driven solutions.
Mentoring junior researchers and engineers on best practices for data analysis, experimentation, and model optimization.
Partnering with product teams to identify opportunities where real-time adaptation can create tangible user value.
Evaluating new research directions and tools to maintain a cutting-edge edge in efficiency and intelligent interaction.
Ensuring that all work aligns with the mandate to build efficient intelligence that evolves in real-time.
Requirements
A PhD or equivalent research experience in a computer science field is mandatory.
4-5+ years of industry experience is required for this senior research role.
Publishing at ICML, NeurIPS, ACL, ICLR, or EMNLP is required.
Deep expertise in model efficiency, real-time alignment, or algorithmic optimization is necessary.
Systems thinking spans the full ML stack to understand and optimize end-to-end workflows.
Strong Python programming is required, with experience in deep learning frameworks such as PyTorch, JAX, or TensorFlow.
Knowledge of model optimization techniques including RLHF and finetuning is required.
Experience in an industry lab with computing at scale is required.
A demonstrated track record of turning analysis into decisions and tangible product impact is required.
Comfort operating in a fast-paced environment where priorities shift based on real-world constraints.
Practical notes
The Adaption Passport provides an annual travel stipend to explore a new country.
A weekly lunch stipend covers take-out or grocery delivery.
Typical interview steps
Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study. Candidates may be asked to design a metric, interpret an experiment, or build a small model. Some companies give a take-home analysis. Expect questions about past projects and the business impact of your work. Interviewers often evaluate how you communicate uncertainty and business impact, not only the math. Bringing a clean write-up of a past analysis to the interview is well received.
Good to know
The role emphasizes talent density among builders and creative thinkers who push boundaries of continual adaptation.
Efficiency drives broader access and ensures innovation benefits many rather than few.
Real-time adaptation relies on models that learn and evolve as they interact with the world.
Interface design is as important as model capability for intelligent systems in the wild.
The team values adaptability, bold ideas, and making work feel lighter for great teammates.
Questions to ask
Good questions to ask the employer in the interview: what does success look like in the first six months, how is the team structured, what is the current biggest challenge, and how are decisions made. Asking about growth paths and the review process is also well received. Employers expect questions, and good ones show preparation.
Career growth
Data careers grow toward senior analyst, staff data scientist, or data engineering lead. Many professionals specialize in machine learning, analytics, or infrastructure. Cross-functional work with product and engineering teams becomes more important at senior levels. The field changes quickly, so continuous learning is part of the job. Professionals who can translate numbers into decisions tend to advance fastest.
About the company
We think that's backwards.
About the company
Most AI is frozen in place - it doesn't adapt to the world. We think that's backwards. Our mandate is to build efficient intelligence that evolves in real-time.