Roboflow — Forward Deployed Engineer

Type: Full-time | Remote (US) with ~40–50% travel | Chicago, IL (Midwest strongly preferred)Compensation: $144K–$200K base ($180K–$250K OTE, uncapped variable) + competitive equityHiring count: 3–5Visa sponsorship: No — not open to any visas (US citizens / Green Card holders only)Reports to:Nick Herrig, Head of Field Engineering

About Roboflow

Roboflow builds software-as-a-service products that make building with computer vision easy — a "physical AI" company spanning image data management, annotation/labeling, preprocessing, one-click model training, and deployment via API or to the edge. Over 1,000,000 developers use the platform, and it works with 65% of the Fortune 100, primarily in global manufacturing.

Founded: 2019 | Team size: 70 | Total funding: $63.6M (see data conflict — "Why Join" cites $99M+ raised / $77.5M Series B)Industry: AI, Software Development, DevtoolsWebsite: roboflow.comOffice: Flatiron, NYC (+1 hub)

Why Candidates Should Join

Real traction at scale: 1M+ developers and 65% of the Fortune 100 are already customers — not a pre-revenue bet.
Well-capitalized and well-backed: $99M+ raised, $77.5M Series B led by Google Ventures and Y Combinator; investors include Sam Altman and Greg Brockman.
Strong comp + benefits: $180K–$250K OTE (uncapped variable) plus equity, 100% health insurance for you and family, $4K/yr travel stipend, $350/mo productivity stipend, $350/mo AI tools budget.
Remote-first across the US: optional NYC/SF hubs with relocation bonus (3x/week hybrid at office locations), flexible async-friendly schedule, co-working stipend.

Intake Call Summary

Company: Physical AI / computer vision company; works with 65% of the Fortune 100, primarily global manufacturing.
Role: Hiring FDEs to support strategic customers, drive platform adoption, and accelerate deployment and product development alongside customers.
Requirements: 2–8 years experience; competencies over strict year counts. Customer-facing experience and execution rigor are critical.
Comp/logistics: $180K–$250K incl. OTE + equity. Remote with preference for candidates near major cities; relocation bonus available.
Timeline/urgency: Plan to hire 3–5 FDEs within the next month due to high customer demand and rapid deployment/expansion needs.
Pain points: Large companies lack internal physical-AI skills, driving high FDE demand; need field insight to support product evolution and adoption.
Ideal profile: Manufacturing, IoT, or robotics backgrounds with strong software skills; open to solution architect / pre-sales transitions.
Interview process: Under calibration; technical screening may be added by role.
Location: Preference for Chicago, Indianapolis, or areas with major airports due to travel needs.
Visa: Avoiding sponsorship at this stage; may revisit based on pipeline.

HM update (Nick Herrig, Aug 20, 2026): Added a Must-Have — "Highly motivated, coachable, and low-ego — someone who's eager to learn, takes feedback well, and is a team player" — based on initial first-round feedback. Candidates should show excitement for the role, have stories highlighting team skills, and present as coachable.

The Role

Take computer vision deployments from proof-of-concept to production at some of the world's largest manufacturing, logistics, and industrial companies. A hands-on 0-to-1 builder who thrives in ambiguous, real-world environments — embedding with customer teams and shipping production-grade systems that hold up on the factory floor. Roboflow's goal: for "FDE at Roboflow" to carry the resume weight of Palantir or FAANG.

What You'll Be Doing

Embed with customers (on-site 25–50% of the time) to take validated POCs to first production deployment — the critical 0-to-1 phase.
Build and configure data pipelines, edge devices, and CV models in real-world physical environments (factories, warehouses, construction sites).
Serve as Roboflow's eyes and ears in the field — surfacing the gap between what customers say vs. need, feeding insight back to Product and Engineering.
Write production-grade Python and handle the messy realities of real-world CV: lighting variability, camera calibration, model drift, edge hardware constraints.
Document deployment architectures, create runbooks, and hand off to Implementation Engineers once the customer runs independently.

Tech stack: Python, Docker, K

Roboflow — Forward Deployed Engineer at David Joseph & Company