Research Scientist, Takeoff Intel

United States Digital Space LLC; San Francisco (CA); Hybrid; USD 350,000 - 850,000; Full time

Job summary

the company is seeking a Research Scientist to advance measurable recursive-self-improvement in large models. You will design evaluations, build models of capability growth, and interpret results to guide R&D decisions.

Senior roles exist and involve hands-on work alongside strategy. Candidates should have hands-on LLM research experience, strong quantitative instincts, and a track record in evaluating AI systems.

Qualifications

• Hands-on research on large language models (pretraining, fine-tuning, RL, evals, or agent systems).
• Strong quantitative instincts and ability to model.
• Experience in forecasting AI capabilities.
• Ability to design an evaluation from a vague question and defend the methodology.
• Clear writing and calibration; state confidence and what would change your conclusion.
• Motivated by impact; comfortable with graded assessments and system-card style output.
• Care about AI safety and implications of rapid capability growth.
• Experience with scaling laws or emergent-capability studies.
• Physics or applied-math background moving into ML.
• Experience supervising and correcting AI-written code.

Responsibilities

• Identify the signals that track AI R&D acceleration and design the evaluations that measure them.
• Build quantitative models of capability growth and self-improvement dynamics, grounded in evaluation and telemetry data.
• Run experiments and evals to test hypotheses about automation and capability.
• Make opinionated research bets and own the outcome.
• Write graded assessments of what our measurements show, for internal decision-makers and public reporting.
• Collaborate with pretraining, RL, economic research, and policy teams.

Skills

LLM research

Quantitative modeling

Forecasting

Evaluation design

Clear communication

AI safety

Impact-focused

Education

Bachelor's degree

Job description

About the company

the company’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.

About the role

We're looking for a Research Scientist who has done hands-on research on large models (pretraining, fine-tuning, RL, evals, or agents scaffolds) and wants to focus on measuring and understanding recursive-self-improvement. You know what the model-development loop looks like from the inside: which signals matter and where the real bottlenecks are. On this team you'll use that judgment to decide what's worth measuring, design the evaluations and models that measure it, and interpret what the results mean for how fast this is moving.

We're hiring at both junior and senior levels. Senior researchers should be comfortable doing hands-on technical work alongside setting research direction.

Responsibilities

• Identify the signals that track AI R&D acceleration and design the evaluations that measure them

• Build quantitative models of capability growth and self-improvement dynamics, grounded in evaluation and telemetry data

• Run experiments and evals to test hypotheses about automation and capability

• Make opinionated research bets and own the outcome

• Write graded assessments of what our measurements show, for internal decision-makers and public reporting

• Collaborate with pretraining, RL, economic research, and policy teams

You may be a good fit if you

• Have done hands-on research on large language models: pretraining, fine-tuning, RL, evals, or agent systems

• Have strong quantitative instincts, are comfortable with quantitative modeling and reasoning

• Have experience in forecasting, may have published AI forecasting scenarios

• Can design an evaluation from a vague question and defend the methodology

• Write clearly and calibrate: state confidence, name what would change your conclusion

• Are motivated by impact: comfortable with work whose output is graded assessments and system-card sections more often than papers

• Care about AI safety and think carefully about where rapid capability growth leads

Strong candidates may also have

• Trained or RL'd frontier models hands-on

• Experience with scaling laws, capability forecasting, or emergent-capability studies

• A physics, applied-math, or similarly quantitative background that moved into ML

• Written a system card section, capability report, or methodology document that others cite

• Experience supervising and correcting AI-written code

Some examples of our work

• the company ECI: our adaptation of Epoch Capabilities Index published in all recent system cards to measure capability acceleration

• AI R&D capability assessments in the Claude system cards

• When AI Builds Itself: all data in the article comes from our team

• The annual compensation range for this role is listed below.

  Annual Salary:

• $350,000 — $850,000 USD

Logistics

Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience

Required field of study:A field relevant to the role as demonstrated through coursework, training, or professional experience

Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position

Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.

Visa sponsorship:We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But 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.

We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team.

How we're different

We believe that the highest-impact AI research will be big science. At the company we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.

The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to the company, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.

Come work with us!

the company is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching,