Forward Deployed Engineer, Post-Sales

Wintermeyer Ventures; San Mateo (CA); On-site; USD 230,000 - 300,000; Full time

Benefits offered by this job

Visa sponsorship

Job summary

Wintermeyer Ventures is seeking a customer-facing AI and ML technical advisor to lead deployments of a data curation platform for enterprise customers. You will help customers build and operate infrastructure to support model training, from onboarding to production rollout.

You will own onboarding, deployment, and optimization, build long-term relationships as the primary technical contact, and design secure, scalable workflows across cloud, on-prem, and hybrid environments.

Qualifications

• 5–10 years in a post-sales role or similar
• Experience owning enterprise post-sales lifecycle (onboarding, deployment, optimization)
• Hands-on enterprise software deployment with distributed systems
• Practical ML/model training knowledge and hands-on ML workflows
• Proficiency in Python and AWS; familiarity with GCP, Azure, Kubernetes, data pipelines, and IaC

Responsibilities

• Own customer onboarding, deployment, and production rollout from planning through optimization
• Build long-term relationships as the primary technical contact across enterprise accounts
• Design secure, scalable workflows across compute, storage, networking, and distributed systems in cloud, on-premises, and hybrid environments
• Translate ambiguous customer needs into concrete architectures and deployment plans
• Partner with sales, engineering, and research teams to relay feedback and shape product direction
• Support deployments through customer-site visits as needed

Skills

Post-sales lifecycle

Python

AWS

Enterprise deployments

Customer-facing communication

ML workflows

Kubernetes

Data pipelines

Education

CS degree

Tools

Kubernetes

Job description

About the Role

Join a customer-facing AI and machine learning team as a trusted technical advisor to enterprise customers. You will lead complex deployments of a data curation platform, helping customers build and operate infrastructure that supports model training.

What You'll Do

• Own customer onboarding, deployment, and production rollout from initial planning through ongoing optimization.
• Build long-term relationships as the primary technical contact across enterprise and strategic accounts.
• Design secure, scalable workflows across compute, storage, networking, and distributed systems in cloud, on-premises, and hybrid environments.
• Translate ambiguous customer needs into concrete technical architectures and deployment plans.
• Partner with sales, engineering, and research teams to relay customer feedback and shape product direction.
• Support critical deployments through customer-site visits as needed.

What We're Looking For

• 5 to 10 years of experience in a post-sales individual contributor role, such as forward deployed engineering, solutions engineering, implementation, or customer-facing machine learning engineering.
• Experience owning the enterprise post-sales lifecycle, including onboarding, deployment, optimization, and establishing processes or playbooks.
• Hands-on experience deploying software within enterprise infrastructure and working with distributed systems, data infrastructure, and on-premises or hybrid environments.
• Practical knowledge of machine learning and model training concepts, with hands-on experience in ML or model training workflows.
• Proficiency in Python and AWS, with familiarity across GCP, Azure, Kubernetes, data pipelines, and infrastructure-as-code.
• Strong customer-facing technical communication skills and experience turning unclear requirements into workable architecture.
• Comfort working in startup or high-ambiguity environments, with recent hands-on individual contributor experience.
• A technical degree or equivalent foundation in computer science or a related field.

Compensation & Benefits

Base salary range: $230,000 to $300,000 annually. Visa sponsorship is available.

Location

On-site in San Mateo, United States. Travel to customer sites may be required.