Job description

THE ROLE We're looking for founding Forward Deployed Engineers who thrive at the intersection of deep technical work and customer impact. You'll own the technical customer relationship post-sale, embedding in their workflows, building production-grade, deeply integrated solutions on their data, and driving real, meaningful outcomes. You'll see how investors actually work up close — their messy data environments, their internal systems, their workarounds — and translate what you learn and build into platform improvements that benefit every customer. You'll have real agency and impact, working directly alongside our founders and engineers to iterate fast and transform how the world’s most sophisticated investors work. WHAT YOU'LL DO - Embed with customers — build custom integrations and pipelines on their data, in their environment, meet regularly to understand evolving needs and identify new opportunities, and scope, redesign, and ship new workflows from the ground up. - Troubleshoot and optimize — debug retrieval quality, LLM performance, and data ingestion in live production environments. - Partner with AI Deployment Managers to drive adoption and unlock new use cases. - Act as the voice of the customer — surface platform gaps and themes before anyone else, and work with engineering and product to identify what should be productized. Often you'll build the fix yourself. - Turn what you build for one customer into something that benefits everyone — ship the best integrations and patterns into the platform, and develop playbooks that scale every future engagement. WHAT YOU HAVE - 2–5+ years as a software engineer or in a technical customer-facing role (FDE, solutions engineer/architect, implementation engineer) — ideally at a company where you shipped directly to customers. - Strong technical foundation — proficient in Python and SQL, comfortable working across the stack and in messy real-world data environments (PDFs, Excel models, inconsistent formats). - Familiar with AI/ML concepts — RAG, embeddings, LLMs, retrieval systems. You don't train models, but you debug and optimize them. - Customer and outcome-obsessed — you're genuinely curious about how customers' systems work, and you measure success by what changes for them, not by what you shipped. - High agency and a builder's mindset — something breaks on-site with a customer, you fix it. You enjoy building solutions and figure things out without waiting to be told what to do. - Strong communicator — you translate customer pain into engineering priorities and explain complex systems to non-technical stakeholders. - Familiarity with financial services is a plus — you understand how investors use data, what due diligence looks like, and why data accuracy matters. PERKS & BENEFITS - Health & Wellness: Comprehensive medical, dental, and vision insurance - Future Planning: 401(k) plan including employer match on contributions made while employed by Rowspace - Food & Fuel: Monthly commuter stipend, plus free daily lunch and a fully stocked snack bar in the office - Team Culture: Regular team offsites to bond and strategize away from the desk

Requirements

Must-haves

Troubleshoot and optimize — debug retrieval quality, LLM performance, and data ingestion in live production environments.
Partner with AI Deployment Managers to drive adoption and unlock new use cases.
Act as the voice of the customer — surface platform gaps and themes before anyone else, and work with engineering and product to identify what should be productized. Often you'll build the fix yourself.
Turn what you build for one customer into something that benefits everyone — ship the best integrations and patterns into the platform, and develop playbooks that scale every future engagement.
2–5+ years as a software engineer or in a technical customer-facing role (FDE, solutions engineer/architect, implementation engineer) — ideally at a company where you shipped directly to customers.
Strong technical foundation — proficient in Python and SQL, comfortable working across the stack and in messy real-world data environments (PDFs, Excel models, inconsistent formats).
Familiar with AI/ML concepts — RAG, embeddings, LLMs, retrieval systems. You don't train models, but you debug and optimize them.
Customer and outcome-obsessed — you're genuinely curious about how customers' systems work, and you measure success by what changes for them, not by what you shipped.
High agency and a builder's mindset — something breaks on-site with a customer, you fix it. You enjoy building solutions and figure things out without waiting to be told what to do.
Strong communicator — you translate customer pain into engineering priorities and explain complex systems to non-technical stakeholders.

Nice-to-haves

Familiarity with financial services is a plus — you understand how investors use data, what due diligence looks like, and why data accuracy matters.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

llmaipythonsqlml

Platform: ashby
Effort: quick

Benefits

Benefits detected in the listing.

Dental

Vision

401(K)

Wellness