Forward Deployed Engineer

Do you want to play a critical customer facing role in a well-backed AI company deploying cutting-edge media authenticity technology in the real world?

Location: Mountain View, Bay Area, California

Compensation: $180k - $200k base equity

Employment Type: Full time

Model: Hybrid. Monday to Thursday in office, Friday optional remote

The Company

This is a leading AI company operating at the intersection of generative AI, media authenticity, and real-world deployment. The team builds advanced deepfake detection technology across audio, image, and video that is actively used by governments and large enterprises worldwide.

Alongside detection, the company also develops industry-leading text-to-speech models, giving it a unique end-to-end perspective on both generative media creation and verification. With $15M raised to date and positioned between Series A and Series B, the company is experiencing strong global demand and entering a new phase of growth. Engineering is central to the mission, with a tight-knit team of just over 30 people.

The Opportunity:

This Forward Deployed Engineer role is a highly technical, customer facing position focused on real world deployment and integration. You will own the technical success of enterprise and government customers, taking them from early proof of concept through to full production rollout and ongoing expansion.

You will work directly inside customer environments, often on-prem, embedding with their engineering teams to integrate detection systems into complex workflows. This role sits at the centre of customers, sales, product, and core engineering, acting as the technical bridge that turns product capability into real operational impact.

This is an ideal role for an engineer who enjoys hands-on problem solving, thrives in ambiguity, and wants direct exposure to how advanced AI systems are deployed in high-stakes environments.

What You Will Do:

• Own technical success for enterprise and government integrations from POC to production and beyond
• Deploy software directly into customer infrastructure, including on-prem and restricted environments
• Work extensively with Linux systems, Docker, Kubernetes, and modern deployment patterns
• Embed with customer engineering teams to integrate detection into APIs, batch pipelines, real-time streams, call center tooling, and security workflows
• Debug issues in real-world environments, including logs, networking constraints, certificates, proxies, SSO, and enterprise security requirements
• Use GenAI tools daily to accelerate integration work, including sample code, SDK snippets, configs, runbooks, troubleshooting guides, and customer-facing documentation
• Build repeatable integration assets such as reference architectures, deployment templates, Helm charts, sample applications, and automated test harnesses
• Support late-stage sales conversations by answering technical questions around infrastructure, networking, and deployment
• Act as the feedback loop between customers and internal engineering teams, translating friction into high-impact product improvements

What We’re Looking For:

• Mid to senior level technical background as a software engineer, SRE, solutions engineer, or implementation engineer
• Strong Python experience with comfort writing scripts, integration glue, and small services
• Deep familiarity with Linux environments and systems-level debugging
• Hands on experience with Docker and Kubernetes. On-prem deployment experience is a major plus
• Experience deploying software into customer, enterprise, or regulated environments
• Strong GenAI product fluency. You use GenAI tools daily to move faster across coding, debugging, and documentation while validating correctness and protecting sensitive data
• Comfortable working directly with customers and earning trust with technical stakeholders
• Ability to operate with ambiguity and drive outcomes without perfect requirements
• Minimum of 3 years of relevant industry experience

Nice to Haves:

• Prior experience shipping enterprise integrations in security, fraud, trust and safety, or telco domains
• Background as a core software engineer transitioning into a customer-facing role
• Experience working with APIs, distributed systems, and real-time data pipelines
• Experience building developer enablement assets such as documentation, reference implementations, or workshops

What Great Looks Like:

• Successfully drives customers from proof of concept to production deployment
• Strong debugging ability across Linux, networking, and containerized environments
• Fluent with containers, Kubernetes, and on-prem enterprise constraints
• Clear, structured communicator who writes high-quality documentation and runbooks
• Proactive, calm under pressure, and highly collaborative across teams

Why This Role:

• Highly technical, high-ownership role with direct customer impact
• Exposure to real-world deployments with government and enterprise customers
• Opportunity to shape how advanced AI detection systems are deployed globally
• Strong growth and learning curve across infrastructure, AI products, and customer engagement
• Close collaboration with senior engineering leadership and the CTO

Interview Process:

• 30 minute initial screen with the CTO
• Technical challenge focused on Linux, Docker, Kubernetes, and deployment workflows
• Final 30-minute interview with a senior engineering leader