Forward Deployed Engineer (Post-Sales)

Forward Deployed Engineer (Post-Sales)

Location: Redwood City, CA

Work model: Hybrid — 4 days/week in-office, plus travel to customer sites

Salary: $230K–$300K base + very competitive equity

I'm hiring a Forward Deployed Engineer for a well-funded AI company whose platform makes model training data dramatically more efficient — cutting training time and cost while improving model performance, and letting smaller models outperform much larger ones.

You'd be the trusted technical advisor for the company's most strategic customers, owning their deployments end to end. Equally comfortable in a customer boardroom and deep in infrastructure configs.

The problem you'd help solve:

A large share of training compute is wasted on data that a model has already learned, doesn't need, or is actively harmed by. The platform fixes that at petabyte scale — but the customers who need it most are the ones with the hardest environments: on-prem clusters, hybrid architectures, strict security boundaries, and data they can't move.

That's where deployments get hard. The research is solved. Getting it running reliably inside a customer's own infrastructure, across compute, storage, networking and IAM, is not.

You'd own that: end-to-end onboarding, deployment and production rollout for strategic accounts. You'd be the primary technical point of contact, building long-term relationships and driving adoption across complex on-prem and hybrid environments. You'd design scalable, secure workflows spanning AWS, GCP, Azure and on-prem Kubernetes — and you'd work with Sales, Engineering and Research to turn customer requirements into technical strategy and feed real deployment learnings back into the roadmap.

You'll likely be a fit if you have:

• 5+ years in a post-sales technical role — solutions engineering, customer engineering, or technical implementation
• Owned customer deployments as an individual contributor: FDE, Post-Sales Solutions Engineer/Architect, Implementation Engineer, or similar customer-facing engineering role
• Strong hands-on experience with distributed systems, data infrastructure, and on-prem or hybrid compute environments
• Deep multi-cloud expertise across AWS, GCP and Azure — compute, storage, networking, IAM
• Experience with ML/AI workflows and deploying systems involving Kubernetes, data pipelines, or large-scale backend infrastructure

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• Python or SQL proficiency, with the ability to debug and hold a technical conversation end to end
• Genuine comfort with ambiguity — and an appetite for distributed systems problems

Nice to have:

• On-prem Kubernetes at real scale, in security-constrained environments
• Infrastructure-as-code
• Petabyte-scale data pipelines or ML training infrastructure
• Experience as the first or one of the first deployment engineers at a technical startup

What you won't find here:

This is a small, deeply technical team where the deployment engineer is the customer's main technical relationship — there's no implementation layer to escalate into. It won't suit you if you want tightly defined scope, clean environments, or someone else to own the customer when things go wrong. Expect real ambiguity and messy on-prem reality.

Note this is a post-sales role: you own deployment and adoption, not quota.

The package:

• $230K–$300K base salary
• Very competitive equity
• 4 days per week in-office in Redwood City, CA
• Travel to customer sites as needed for critical deployments
• Open to visa transfers and relocation