Forward Deployed Engineer, AI Training Data (San Mateo, CA)

About the role

Lavendo partners with startups and high-growth companies to help them hire top-tier sales, GTM, and technical talent. 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.

The Mission

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.

The Opportunity

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.

What You'll Do

• 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

What You Bring

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

Additional information

Note: This role is built for people who bring both deep post-sales, customer-facing deployment experience and hands-on ML/model training expertise with measurable results. If you're strong in only one of these two areas, this likely isn't the right fit - but we'd still encourage a conversation if you're close.

Interviewing Process

• Initial Screen - Assesses background, interest, culture fit, comp expectations, and logistics
• Hiring Manager Technical Screen (30 min) - Evaluates distributed systems experience, cloud infrastructure (AWS/GCP/Azure), Kubernetes, and post-sales/customer-facing mindset
• Coding Screen (1 hour) - Live coding in Python with end-to-end debugging
• On-site (3.5 hours) - System design/architecture discussion, technical deep dives, behavioral/stakeholder scenarios, and cross-functional interviews with Sales, Engineering, and Research