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Job market data

Forward deployed engineering is a small but fast-growing category — we track a few hundred postings in the US in any given year, clustered at AI-native product companies selling into the enterprise. These roles are predominantly remote or travel-heavy hybrid, with base compensation generally in a $150K–$230K range plus meaningful variable upside. The strongest forward deployed engineers we place are unusually generalist — equally comfortable with data pipelines, model integration, and a customer call at 4pm on a Friday.

Key responsibilities

Work directly with enterprise customers to design, implement, and deploy AI-enabled solutions on top of the platform
Translate ambiguous customer requirements into concrete technical specifications and ship production integrations rapidly
Serve as the primary technical point of contact throughout onboarding and early use
Pair with customer data, engineering, and product teams in their environments — on-site or remote — to unblock adoption
Capture patterns and gaps from the field and feed them back to product and engineering with signal, not just anecdote
Troubleshoot complex data, integration, and modeling problems that sit outside the standard playbook
Influence product direction based on what actually works (and doesn't) in real customer deployments
Build shared assets — templates, integration patterns, evaluation suites — that accelerate the next rollout

Candidate requirements

5+ years of software, data, or ML engineering experience with strong generalist range
Experience working directly with enterprise customers — solutions engineering, field engineering, or forward-deployed roles
Strong Python and SQL, with comfort across multiple cloud environments
Ability to move fast from ambiguous customer requests to shipped technical solutions
Excellent written and verbal communication with both engineers and business stakeholders
Willingness to travel when needed (typically 10–25%)

Forward Deployed Engineer