Solutions Engineer (Enterprise & Quant Use Cases)

Zerve is an agentic data workspace for decision-grade data work.

Modern organisations rely on analysts, data scientists, and quantitative researchers to reduce uncertainty and recommend actions. But the tools they use today were built for exploration, not operational reasoning.

Most teams still stitch together notebooks, BI tools, orchestration systems, and scripts to move from analysis to production. This fragmentation slows decision cycles and makes reasoning difficult to reproduce, audit, or operationalize.

Zerve enables teams to move from exploratory analysis to repeatable decision systems by combining:

• AI agents that execute data workflows

• Stateful execution environments

• Workflow orchestration and reproducibility

• Deployable outputs such as apps, APIs, and scheduled runs

Our early users include Tier 1 quantitative firms and advanced enterprise data teams using Zerve for research workflows and agentic trading systems.

We’re hiring a Solutions Engineer (Quant / Enterprise) to work closely with some of the most advanced data teams in the world.

You’ll help quantitative researchers and enterprise data teams deploy Zerve in real environments — from research pipelines to operational analytics workflows.

This role sits at the intersection of:

• Engineering

• Data science

• Customer success

• Product development

You’ll help translate complex user needs into working solutions while shaping how the product evolves.

• Work directly with quant researchers and enterprise data teams to implement Zerve in real workflows
• Help design solutions for research pipelines, analytics automation, and decision systems
• Support technical evaluations and proof-of-concept deployments
• Deliver product demos and architecture guidance
• Create reusable workflow templates and technical documentation
• Provide feedback to the product and engineering teams based on real customer usage

Strong candidates typically have experience in one or more of the following areas:

• Data science or machine learning
• Quantitative research or trading systems
• Distributed data infrastructure
• Developer tools or technical platforms

You should be comfortable working with:

• Python
• Data pipelines or analytics workflows
• APIs and cloud environments

You enjoy working closely with users and helping them solve complex technical problems.

• Experience working in hedge funds or quant research environments
• Experience with notebook-based workflows or data science platforms
• Experience deploying ML or analytics pipelines
• Familiarity with AI agents or orchestration systems

You’ll help define how organisations move from analysis to operational decision systems.

The next generation of AI tools will not just generate answers or code. They will power systems that continuously reason over data and drive decisions. xcfaprz

Zerve is building the workspace where those systems are created.