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DataLab

Enterprise Solutions Engineer

SalaryNo pay range posted
LocationNew York Office
Work modeon-site
Typefull-time
SenioritySenior
DepartmentSales
Company size11–50 people
PostedOct 9, 2026 · 3d ago
Verified live13h ago
New York law requires most employers to post a pay range.
At a glanceSummarised by Seekless from the posting.
Must have6
Significantly contributed to closing large (six-figure) opportunities, as an AE selling a technical product or as an SE who carried enterprise deals to signature
Credibility with engineers: can read API docs, write a script, debug a deployment, talk to an ML engineer and a CISO
Presence with executives: can hold own in a room with a C-suite buyer and make the business case
A feel for how enterprise deals move: multiple stakeholders, security reviews, procurement, budget cycles
A drive to win: would rather own the outcome than hand it off
Comfort without a map: early stage, no playbook
Nice to have3
Document AI, OCR, parsing, extraction, or LLM evals
Enterprise sales experience in financial services or insurance, or with AI native companies
On-prem or regulated deployments
Skills
Python
API
ML
Deployments
Document AI
OCR
LLM
About the role
Picture this: an AE walks into the room and introduces you. You do the work to understand the prospect and what it takes to win. You get the deal to a product and technical yes. Then someone else closes it, and the win goes on their record, not yours.
Most solutions engineer roles stop at the demo. This one doesn’t. The win is yours.
At Datalab, solutions engineers are the sales team. The people who know the product best should be the ones asking for the signature and talking price.
You’d be one of the first hires on this team, working directly with our leadership team.
About Datalab
Datalab is an applied research lab in New York. We train our own models to turn documents into data you can verify: scans, dense tables, handwriting, the pages other tools quietly get wrong.
Because we own the models, a customer’s hard problem becomes a roadmap conversation, not a feature request. Our open source work (Marker, Surya, Chandra) has more than 70,000 GitHub stars. Customers include Anthropic, ElevenLabs, Gamma, Reflection AI, and Legora, along with insurers, financial firms, and healthcare companies. We deploy anywhere from our API to fully air-gapped on-prem.
We’re a small, founder-led team. You’ll have real influence on how we sell and what we build.
Day to day
You own the deal. Everything below is yours, with the whole team as backup when you want it.
•
Own opportunities from first conversation to signature.
•
Win the technical evaluation: scope the problem, run the proof of concept and design the deployment, whether API, VPC or on-prem.
•
Make the business case to the customer’s leadership, not just their engineers.
•
Own pricing and commercial terms.
•
Bring what you learn back to product and engineering, where you’ll have real say and sway over what we build.
Ideal candidate
We care about three things: you’re credible with engineers, you can hold your own with a C-suite or other executives, and you can close.
We’re eager to work with someone who has:
•
Significantly contributed to closing large (six-figure) opportunities, as an AE selling a technical product or as an SE who carried enterprise deals to signature, and can give concrete examples of what you did.
•
Credibility with engineers. You can read API docs, write a script, debug a deployment, and talk to an ML engineer and a CISO on the same day.
•
Presence with executives. You can hold your own in a room with a C-suite buyer and make the business case.
•
A feel for how enterprise deals move: multiple stakeholders, security reviews, procurement, budget cycles.
•
A drive to win. You’d rather own the outcome than hand it off.
•
Comfort without a map. Early stage, no playbook. That sounds fun to you.
Bonus points
None of these are required. Each one shortens your ramp.
•
Document AI, OCR, parsing, extraction, or LLM evals.
•
Enterprise sales experience in financial services or insurance, or with AI native companies.
•
On-prem or regulated deployments.
Interview process
1.
Intro call
2.
Deal walkthrough
3.
Take-home project
4.
Conversations with the team
Legal
Datalab is an equal opportunity employer. We do not discriminate on the basis of any characteristic protected by federal, state, or local law.
If you need an accommodation to participate in our hiring process, or if you believe you have experienced discrimination or harassment at any point in it, contact [email protected].
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