Sequen
VP, Field Engineering
$270K–$300K
United States · Remote · Director · Full-time · Posted Sep 9, 2026
- Experience
- 8–12+ years in pre-sales / sales engineering / solutions engineering; at least 4 years leading Field Engineering or Sales Engineering teams.
- Education
- Bachelor's degree or comparable experience, ideally in a technical field.
About the job
Build and lead Sequen’s Field Engineering function, team, and technical pre-sales playbook, while guiding strategic customer evaluations and connecting field insights to product and go-to-market decisions.
What you'll do
- Hire, coach, and scale a team of 4–5 Field Engineers; establish its hiring process and operating rhythm.
- Define the demo, POC, proof-of-value, technical qualification, and collateral frameworks for technical pre-sales.
- Lead complex strategic evaluations and translate customer requirements and evaluation learnings into feedback for product, ML, and forward deployed engineering.
What they ask for
- 8–12+ years of pre-sales, sales engineering, or solutions engineering experience, including at least 4 years leading Field Engineering or Sales Engineering teams.
- Built or substantially scaled a technical pre-sales function and led technical sales for technical products.
- Track record of driving technical wins on seven- and eight-figure enterprise deals.
- Strong customer-facing communication skills; bachelor's degree or comparable experience, ideally in a technical field.
Nice to have
- Pre-sales experience in retail, e-commerce, marketplaces, or travel; hands-on depth in recommendation, ranking, search, or personalization systems.
- Experience scaling teams rapidly, building POC or proof-of-value motions tied to specified commercial models, or working with forward deployed engineering or professional services.
Pay
$270K – $300K (base salary)
Tools
infrastructure, ML/AI, data platforms, search, recommendation, ranking, personalization
AI focus
Leads technical pre-sales for a platform combining frontier ranking models and production infrastructure; supports customer evaluations and feeds learnings back to product and ML.
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