Staff Product Engineer

What we're building

Arist is an agent-first workforce enablement platform. We deliver learning through the channels employees already use — SMS, Slack, Teams — and we're building toward a future where AI agents autonomously identify skill gaps, generate interventions, and close them before a manager ever has to ask.

The center of that architecture is the employee profile: a living graph built from interaction data, performance signals, and organizational context. Arist’s intelligence layer reads those profiles continuously, surfaces gaps, and routes the right content to the right person at the right moment.

For example: a rep has a discovery call booked in 4 hours. Arist sees it on their calendar, knows from their CRM history that discovery is a weak spot, and sends them a short AI coaching call that morning: targeted practice, right before they need it.

Our customers are some of the most complex enterprises in the world — pharma, industrials, insurance, financial services. The distribution problems we solve are hard.

We're a small, focused team. We're past proving the concept and now building the platform that makes the vision inevitable.

What you'll do

You'll own the parts of the system that make Arist intelligent — the models, pipelines, and products that turn raw behavioral and organizational signals into decisions that reach the right person at the right time. The pieces that determine whether Arist becomes the platform companies can't operate without, or just another tool.

• Design and own the core data models that power Arist's intelligence: learner profiles, engagement signals, performance indicators, content effectiveness. Decide how data is structured, stored, and accessed at enterprise scale
• Ship data products, not dashboards nobody opens. Specifically, surfacing performance gaps to managers or leadership, triggering personalized enablement for individuals via SMS, Slack, or Teams, and making sure the right learning reaches the right person at the right time
• Help lead how our AI agents plan, act, and adapt: how they take on tasks, check their own work, and respond when something changes
• Own the end-to-end health of production data flows. Define quality contracts, build observability, and be the person who knows when something is wrong before a customer does
• Work directly with product, design, engineering, and customer-facing teams to frame problems quantitatively, identify opportunities others miss, and make the case for what to build next

What we're looking for

Must-haves:

• 8+ years of software engineering experience, with meaningful time at Staff level or equivalent scope
• Deep experience building and operating data-intensive systems in production, including modeling, owning pipelines, quality, and reliability end-to-end
• You've built data products that changed how a team or company made decisions, not just ETL jobs that fed a warehouse
• Strong SQL and data modeling instincts, with experience designing schemas that serve both analytical and operational workloads
• You've made consequential technical decisions and been accountable for the results, including those that didn't go as planned
• Comfort with ambiguity and a bias toward action over analysis. You'd rather have something in production learning from real usage than a perfect design document
• Experience with modern data tooling (dbt/SQLMesh, Redshift/BigQuery, Segment, Kafka/Kinesis event streaming) but you care more about the right abstraction than the specific tool

Strong signal:

• Experience building data foundations for AI/ML product systems with a strong understanding of what agents and models actually need from the data layer
• You've worked at a company where data was the product, not a support function
• You've set technical direction for a team and led them through execution, not just handed off a design
• Background in enterprise SaaS with complex integration requirements
• You've been the person who set the technical direction, and led a team to get there, not just executed it
• You've worked at a company that serves enterprise customers and know what it means to build for scale before you're there

What this means in practice

• You'll set the technical direction for your domain. The architecture is yours to define, defend, and deliver
• You'll start from problems, not tickets. You're expected to figure out what to build, make the case, and ship it

Why Arist

We have a genuinely hard problem of impacting individual performance at-scale in large, distributed organizations.

The US Department of Labor ran their Make America AI Ready program entirely on our platform, reaching millions of Americans. Ecolab doubled YoY revenue and saw 5x playbook adherence. Novartis saved over $2M and launched 8x faster. Wolters Kluwer drove 120% more AI usage and saved $1.62M in time.

We're a Series B company with strong customer retention, a sharp point of view on where enterprise AI is going, and a small team that executes well. There's a lot left to build!