Forward Deployed Engineer
Posted 2 days ago
The role in short
Embed with customers to build and ship production-grade integrations and data pipelines on their infrastructure, optimizing AI/ML performance and driving platform improvements.
The Role
We're looking for 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
Prep for this interview
3 questions likely to come up at Rowspace, each matched to a line in this posting.
- 1LLMs, RAG and agents
They wrote: “Familiar with AI/ML concepts — RAG, embeddings, LLMs, retrieval systems. You don't train models, but you debug and optimize them.”
A customer wants a chatbot over their internal documents. How do you build it?
How to answer
Why they ask: RAG is the most common first project. They want the whole path, from messy files to answers people trust.
- Collect and clean the documents; handle permissions per user
- Split them into chunks, embed, and store with source links
- Retrieve, then answer with citations; say “I don't know” when retrieval is weak
- Build a test set of real questions with expected answers before launch
Tip: Spend more time on the data and on testing than on the model.
- 2Discovery and scoping
They wrote: “Strong communicator — you translate customer pain into engineering priorities and explain complex systems to non-technical stakeholders.”
How do you decide what to build first when a customer wants ten things?
How to answer
Why they ask: FDEs have limited weeks per customer. This tests judgment about value, effort and risk.
- Score each request by business value and by effort, with the customer in the room
- Pick one item that proves value fast and unblocks later work
- Write down what's out of scope so it doesn't creep back in
- Revisit the list after the first release, with real usage data
Tip: Show a simple way to rank the list and name what you'd leave out on purpose.
- 3Data and SQL
They wrote: “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).”
Write a query that finds each customer's first and most recent order.
How to answer
Why they ask: A quick check that you're comfortable with grouping, joins and window functions.
- Clarify the tables and what counts as an order (cancelled ones?)
- Use MIN/MAX with GROUP BY, or ROW_NUMBER() if you need the whole row
- Handle ties on the same timestamp, and customers with no orders
- Explain how you'd test it on a few known customers
Tip: Say what you'd check in the data before writing the query: duplicates, time zones, nulls.
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