Forward Deployed Engineer, AI Training Data (Redwood City, CA)

Lavendo•Redwood City, CA

4d•$230,000 - $300,000•Hybrid

About The Position

This role is with one of our clients; we’ll share full details about the company and interview process as we get to know you and confirm mutual fit. Our client is a fast-growing AI infrastructure company that has built a state-of-the-art data curation platform used to train some of the most demanding deep learning models in the world. Their technology automatically curates and optimizes petabytes of training data — algorithms that are modality-agnostic and require no labels — to make model training dramatically faster and more efficient. Backed by $57.5M raised across Seed and Series A, with investors including Microsoft, Amazon, Felicis, and AI luminaries like Geoff Hinton, Yann LeCun, and Jeff Dean, our client has built a lean team of ~60 people. This is a company already proving out results with real enterprise customers. Foundational models are only as good as the data they're trained on. Our client exists to close the gap between raw data and truly optimized training data — without requiring labels, without adding compute cost, and without forcing customers to compromise on model quality. Every strategic account this team lands is a chance to prove that better data curation, not just bigger models, is the unlock the industry has been missing. As Forward Deployed Engineer (Post-Sales), this is a rare role at a company small enough that your fingerprints will be on every major account, and well-funded enough that the roadmap, the compensation, and the customer roster are all already real. You'll be one of the founding members of the post-sales technical org, working directly with strategic enterprise accounts to take them from signed contract to production deployment — and shaping the playbooks that the next wave of hires will use. If you've been looking for a role that lets you stay hands-on with distributed systems and model training while also owning the high-stakes customer relationships, this is that role.

Requirements

5–10 years of experience in a post-sales individual-contributor capacity, owning customer deployments as a Forward Deployed Engineer, Post-Sales Solutions Engineer/Architect, Implementation Engineer, Machine Learning Engineer, or customer-facing engineer
Hands-on machine learning and model training experience — you understand pre-training, mid-training, and post-training concepts, not just how to call an API
Real experience deploying software into enterprise customer infrastructure
Strong Python proficiency (required) and AWS proficiency (required)
A track record of owning the full post-sales enterprise lifecycle — onboarding, optimization, and everything in between
Experience building post-sales processes or playbooks from scratch
Comfort operating in ambiguous, high-stakes, and fast-moving environments — startup or high-ambiguity experience is a plus
A technical degree (CS or equivalent)
A demonstrated ability to translate messy, ambiguous customer requirements into clean, concrete architecture
Willingness to travel to customer sites as needed (roughly 15%)

Nice To Haves

Exposure to multi-cloud environments (GCP, Azure), on-prem or hybrid Kubernetes, or broader ML tooling — not required, but a strong differentiator

Responsibilities

Own end-to-end onboarding, deployment, and production rollout of the platform for strategic enterprise accounts — from first technical conversation through stabilization and beyond
Serve as the primary technical point of contact for your accounts, building long-term relationships and driving adoption across complex on-prem and hybrid environments
Design scalable, secure workflows spanning compute, storage, networking, and distributed systems across AWS, GCP, Azure, and on-prem Kubernetes
Translate ambiguous, real-world customer requirements into concrete technical architecture — there's no pre-written spec waiting for you; you help define the problem
Build the processes and playbooks for post-sales deployment from 0→1, knowing that what you build now becomes the foundation for the team scaling behind you
Partner cross-functionally with Sales, Engineering, and Research, relaying field learnings that directly shape the product roadmap
Travel to customer sites as needed (roughly 15%) to support critical deployments and high-stakes engagements