Datawizz is building the agent workforce. We're a seed-stage team backed by Human Capital (SpaceX, Snowflake, Anduril) building a platform that helps enterprises build and deploy agents — with the right permissions, guardrails, and activity logging to actually ship within companies. Early customers are already automating several hours of their workday.
We're hiring people who want to build, not manage. If you want to work on hard problems with a small team that ships, this is the place.
The RoleAs Head of Research, you’ll own Datawizz’s research agenda and build an applied research organization focused on making agents radically more reliable and capable. You’ll define the roadmap across agent planning, tool orchestration, evaluation, and safety; lead hands-on experimentation; and partner closely with engineering to productionize breakthroughs that drive measurable quality and reliability wins for customers.
You will:Set the research strategy and roadmap for agent planning, tool orchestration, multi-agent coordination, and evaluation.
Build, lead, and mentor a high-caliber applied research team.
Design and run rigorous experiments (ablations, offline/online A/Bs), defining clear metrics for reliability, accuracy, and performance.
Own our evaluation stack: datasets, benchmarks, human-in-the-loop reviews, and reliability/safety assessments.
Develop novel methods (e.g., agent planning algorithms, tool selection strategies, multi-agent coordination, safety/alignment techniques) and ship reference implementations.
Collaborate with engineering to transfer research into production and measure real-world impact.
This role is in-office, 5 days/week, based in San Francisco.
Leading applied ML/NLP research teams and shipping work into production at a startup or high-growth company.
LLM and agent internals: prompting strategies, tool use, planning/reasoning architectures, multi-agent systems, and evaluation methodology.
Building evaluation frameworks (task suites, synthetic data, human eval pipelines) and tying metrics to product outcomes.
Large-scale agent systems: orchestration frameworks, tool APIs, distributed execution, observability, and logging infrastructure.
Strong coding skills in Python and a bias toward hands-on experimentation and rapid iteration.
Data curation and labeling workflows, with attention to privacy, safety, and robustness.
Communicating research clearly and partnering cross-functionally with engineering and product.
(Nice to have) Publications or notable open-source contributions; patents; early-stage 0→1 experience.
Competitive salary, based on experience level (Annual compensation range: $50,000-$500,000)
Meaningful equity
Opportunity to be a founding member of a growing company
Skills Required
- Set research strategy and roadmap for agent planning, tool orchestration, multi-agent coordination, and evaluation.
- Build, lead, and mentor an applied research team.
- Design and run rigorous experiments (ablations, offline/online A/B tests) with clear reliability and performance metrics.
- Own evaluation stack: datasets, benchmarks, human-in-the-loop reviews, and reliability/safety assessments.
- Develop novel methods (agent planning algorithms, tool selection strategies, multi-agent coordination, safety/alignment techniques) and ship reference implementations.
- Collaborate with engineering to transfer research into production and measure real-world impact.
- Experience leading applied ML/NLP research teams and shipping work into production at a startup or high-growth company.
- Deep knowledge of LLM and agent internals: prompting, tool use, planning/reasoning architectures, multi-agent systems, and evaluation methodology.
- Experience building evaluation frameworks, human eval pipelines, and tying metrics to product outcomes.
- Experience with large-scale agent systems: orchestration frameworks, tool APIs, distributed execution, observability, and logging infrastructure.
- Strong coding skills in Python and bias toward hands-on experimentation and rapid iteration.
- Experience with data curation and labeling workflows, with attention to privacy, safety, and robustness.
- On-site work requirement: in-office 5 days/week in San Francisco.
- Publications, notable open-source contributions, patents, or early-stage 0->1 experience.
What We Do
Gamut is an artificial-intelligence software platform for building and running personal and enterprise AI agents in secure environments. Its agents operate autonomously in browsers and connected tools, handling research, scheduling, repetitive preparation, and other complete workflows rather than isolated tasks. The company provides controls such as permissions, guardrails, activity logging, and integrations so teams can deploy agents productively within their organizations.
