Member of Technical Staff, Data

Role Overview

• Are you a skilled developer excited to build at the frontier of AI-native software development? We're hiring an experienced developer to shape the future of our AI engine for impact intelligence — full-stack by default, but we care more about analytic skills, good judgment, and AI-fluency than about a specific stack. You'll work across the platform, from data pipelines, APIs, and ML/LLM systems to the product surfaces our customers use, and you'll be expected to pick up whatever the problem requires.
• At Upright, you get to work on a product that actually matters: the world's largest open-access database on company impact, used by 1,000+ institutional investors and corporations to make real capital allocation decisions. We quantify companies' impact from the ground up, based on peer-reviewed science and what companies actually produce and sell. The raw material is their product data, which we collect and classify automatically at scale.
• What makes the role unusual is how we build today. Over the past six months, we have rebuilt a large part of our development workflow around AI agents, including our in-house Slack-native agent "Upbot", which now autonomously handles a meaningful share of bug fixes, feature development, dev-environment management, data QA, data refreshes, and other engineering chores that used to require a human. As a developer at Upright, you spend much less time on repetitive plumbing and much more time designing systems, writing the hard parts, and teaching agents to do the rest well. We're betting heavily on AI-native engineering, and you'd be joining a small, senior team where your work on both the product and the agentic tooling around it is visible from day one.
• Your specific responsibilities will be tailored during the recruitment process to your background, skill level, and interests. If you're ready to grow your career while building a platform that matters and to do it in an AI-forward engineering environment, we'd love to hear from you!

Requirements

• At least 2 years of professional experience in data analysis, data science, research, consulting, or a closely related analytical field.
• Strong analytical and quantitative thinking. You reach for the right tool (a pivot table, a SQL query, a notebook, a Bayesian model, an LLM, a back-of-the-envelope sanity check) instead of defaulting to one.
• Stubborn about correctness: you notice when a number looks slightly off, and you keep pulling on the thread until you understand why.
• Comfortable working with large, messy datasets, SQL fluency is a must, Python (pandas / notebooks) or similar is a strong plus.
• Comfortable collaborating closely with non-engineering domain experts (sustainability researchers, analysts, customers) and turning expert judgment into structured, defensible analyses.
• Comfortable working with LLM-assisted features, using AI tools to speed up your own analysis, evaluating LLM-generated outputs, designing checks that catch model regressions, even if you don't consider yourself an ML or coding specialist. Curiosity about agent-assisted workflows matters more than prior experience with them.
• Strong output orientation and common sense thinking to enable solving hard-to-define problems.
• Ability to communicate clearly both verbally and in writing, especially turning a complicated analysis into a clean explanation a non-expert can act on.
• Solid track record of internal passion for excellence: you have gotten things done clearly better than what was required, because you enjoy doing things well.

Tech Stack

• Pandas
• Python
• SQL

Benefits

• A chance to join a quickly growing and highly ambitious impact SaaS company with a mission that matters — real capital allocation decisions at 1,000+ institutional investors and corporations rest on the data we build.
• A team of exceptional people who are kind, direct, and care deeply about doing the work well.
• An unusually AI-forward environment — first-class tooling, in-house agents, and the freedom to keep pushing what "AI-native development" actually means in practice. You'll be shaping the workflow, not inheriting it.
• Substantial autonomy and ownership from day one, with lots of room to grow.
• Competitive compensation, including stock options and a comprehensive healthcare package.