Solutions Engineer (San Francisco, California)

Lakera, a Check Point Company, is seeking a Solutions Engineer to accelerate sales velocity, increase customer success and shape the product experience as we scale. In this customer-facing role, you’ll be the primary technical point of contact through the sales process and a resource for customer success. You’ll translate customer needs into solutions, architecting with Check Point's AI Defense Plane to help enterprises secure their AI stacks and unlock cutting-edge use cases.

You’ll work closely with Sales, Customer Success, Product, and Engineering to ensure we deliver on our mission. With your blend of technical expertise, communication skills, and customer empathy, you’ll be at the forefront of AI security innovation.

About Lakera, a Check Point Company.

Lakera, a Check Point Company, is on a mission to ensure AI does what we want it to do. We are heading towards a future where AI agents run our businesses and personal lives. We're not just dreaming about the future; we're building the security foundation for it. We empower security teams and builders so that their businesses can adopt AI technologies and unleash the next phase of intelligent computing.

We work with Fortune 500 companies, startups, and foundation model providers to protect them and their users from adversarial misalignment. We are also the company behind Gandalf, the world’s most popular AI security game.

We move fast and work with urgency. We act as one team but expect everyone to take substantial ownership and accountability. We prioritize transparency at every level and are committed to always raising the bar in everything we do. We promote diversity of thought as we believe that creates the best outcomes.

What You’ll Do:

• Act as the primary technical point of contact for prospects evaluating our product. Understand their needs and design solutions that align with their infrastructure using our platform.

• Partner with Customer Success to ensure customers onboard quickly and realize value from the platform.

• Develop and maintain technical documentation and guides to streamline pre-sales and guide post-sales workflows.

• Support product configuration and deliver minor technical changes required for prospect and customer implementations.

• Feed prospect feedback directly into Product and Engineering, helping shape the roadmap and ensuring the platform evolves with real-world needs.

• Lay the foundation for a scalable Solutions Engineering function by building playbooks and repeatable best practices.

What You’ll Bring:

• 5+ years of experience as a Solutions Engineer, Sales Engineer, Solutions Architect, or similar role.

• A track record of managing relationships with large, complex organizations, preferably with a deeply technical enterprise product.

• Proficiency coding in the Python and Javascript ecosystems.

• A track record of enabling large scale implementations in at least one of the hyperscalers (AWS,GCP, Azure). Preferred experience with Docker, Kubernetes, and Terraform.

• An understanding of and experience navigating security, compliance, and IT processes and requirements when implementing B2B products at large enterprises.

• Strong written and verbal communication and interpersonal skills with a comfort presenting in front of customers 1:1, in small groups, and at large conference venues..

• A track record of working directly with customers of varying sizes, including large enterprises.

• An organized, self-starter approach with an eye for finding creative ways to get projects to the finish line.

• A passion for what AI can do for the good of humanity and a desire to ensure that it is implemented in a secure and ethical way.

• Comfortable in a small and fast moving startup environment.

Location: San Francisco, California.

Logistics: Hybrid. We’re excited about candidates who want to contribute to our in-office culture.

Compensation and Benefits: Total on-target earnings (OTE) are expected to range from $190,000 to $210,000, based on an 80/20 compensation split and determined by years of experience, depth of relevant domain knowledge, and interview performance.

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