HiddenLayer

• Manage ongoing communication across your portfolio: regular check-ins, status updates, and account emails. • Join customer calls, take notes, keep things on track, and make sure every action item is captured and assigned. • Own all follow-up after calls; track who owns what internally and make sure things actually get done. • Handle scheduling coordination so the rest of the team doesn't have to. • Keep Jira tickets current, correctly labeled, and moving. • Maintain accurate health notes and scores so the team always has a real view of account status. • Proactively keep the VP of Customer Success informed on account health, risks, and anything that needs visibility or escalation. • Work alongside the Solution Architect team during onboarding. The SA handles technical setup; you own the customer relationship and make sure nothing gets lost in the transition from Sales. • Partner with Technical Support Engineering on issues. Triage what comes in, route it to the right TSE, and own customer communication while it's being resolved. • Keep Account Directors informed ahead of renewals, flag at-risk accounts early, and surface upsell opportunities for them to run with. Be the source of truth on day-to-day account context. • Help new customers see value quickly. Onboarding doesn't end when the contract is signed. • Monitor product adoption and flag low engagement before it becomes a problem. • Own customer communication during escalations.

• Own and resolve technically complex customer issues from initial report through root cause • Troubleshoot across APIs, integrations, logs, and infrastructure (not just UI-level issues) • Reproduce issues, isolate variables, and identify whether problems stem from configuration, usage, or product defects • Partner closely with Engineering to escalate issues with clear, actionable context and debugging data • Work with Product and Research to validate expected vs. actual system behavior • Translate technical findings into clear, concise updates for customers • Contribute to and improve internal runbooks, troubleshooting guides, and external documentation • Identify patterns across tickets and proactively suggest product or process improvements • Help maintain a customer-first culture through proactive communication and solution ownership

• Customer Onboarding & Deployment: Partner with customers to design and implement secure deployments of our AI security solutions, ensuring seamless integration into their environments (cloud, on-prem, or hybrid). • Technical Guidance & Iteration: Provide expert technical consultation on infrastructure, containerization (Docker/Kubernetes), and cloud networking. Help customers rapidly iterate and adopt best practices for secure AI operations. • Customer Success Ownership: Act as the primary technical point of contact post-sale, ensuring customers derive ongoing value, achieve their security objectives, and successfully scale their deployments. • Troubleshooting & Optimization: Diagnose and resolve customer issues, optimize configurations, and proactively identify opportunities for improvement. • Enablement & Training: Deliver hands-on workshops, training sessions, and technical documentation to empower customer teams to be self-sufficient with our products. • Feedback Loop: Capture customer feedback and collaborate with product management, engineering, and research teams to inform roadmap priorities and product enhancements.

• You're looking for a Data Scientist to join our Data Sciences and ML Engineering team. You'll be building, shipping, and improving the models and LLM-powered systems that sit at the core of our security products. • This is a hands-on role on a small, focused team. You'll have real ownership over the models and pipelines you build, close collaboration with engineering and product, and the runway to go deep on the hard problems. • Your work will span a few areas: • Model development and research. Building classifiers, detectors, and scoring models on messy, high-stakes security data. Designing experiments, evaluating trade-offs, and iterating on architectures — not just hyperparameters. • LLM agent systems. Shaping the prompts, context, tool-use patterns, and supporting content that drive our LLM agents. • Production delivery. Shipping models behind real traffic, monitoring them, and improving them over time. • Evaluation and iteration. Building the evaluation harnesses and feedback loops that let us know whether a change is actually an improvement — often the hardest part of the work. Our models only improve for customers when our evaluations highlight what really matters.

• Monthly close. You'll own close end-to-end — journal entries through published financials — with a target cadence of 10 business days monthly. • Audit readiness and external audit. You'll stand up documentation and controls for external audit, partner with the CFO on firm selection, and manage fieldwork through to a clean opinion. • The books. Full GL, AP, AR, treasury, payroll accounting, and revenue recognition. You'll have CFO partnership on technical accounting matters and external support available for heavier lifts (ASC 718, 340-40, SAFE conversion) as needed. • Finance systems. You'll own the finance systems roadmap — GL, AP, expense, and billing tooling — and lead evaluations and migrations as the business scales. We want someone who has opinions here. • Revenue recognition policy. SaaS subscription, some services, occasional non-standard deals, a reseller arrangement. You'll own the policy, review non-standard contracts before they close, and partner with RevOps on ARR tie-out.

• Conduct end to end penetration testing on AI systems, with a focus on predictive and generative AI models. • Develop and execute adversarial attacks (e.g., evasion, poisoning, and inference attacks) to identify weaknesses in predictive models. • Develop and execute adversarial attacks (e.g., jailbreak, hallucination, context leakage, etc.) to identify weaknesses in generative AI models and applications built on top of them. • Collaborate with data scientists, engineering, and research teams to design and implement novel attacks and relate them back to actionable recommendations. • Stay current with the latest AI security research, trends, and adversarial tactics. • Produce detailed reports outlining vulnerabilities, risks, and actionable recommendations. • Contribute to the development of internal tools and frameworks for AI red teaming.

• Increase deployment confidence and velocity by embedding automation and quality signals into everyday engineering work. • Improve reliability of low-latency, distributed services running in Kubernetes by catching regressions early and validating failure modes. • Eliminate “QA phases” by building rapid, team-embedded validation that runs continuously in CI/CD. • Establish clear quality metrics across our microservice catalog so teams can see, own, and improve service health over time. • Build and maintain automation (unit/integration/e2e) that enables continuous deployment with high confidence. • Create test harnesses and frameworks to accelerate critical-path, upgrade, and failure-mode testing across microservices. • Validate installation and upgrade workflows for Kubernetes deployments (Helm, manifests, customer-like configuration). • Run environment and compatibility testing across cloud and customer setups (AWS/Azure, networking/storage/security variability). • Track, publish, and operationalize quality metrics (coverage, flake rate, defect escapes, regression trends) for each service. • Partner closely with Engineering, Product, and Security—communicate early, document risks, and seek alignment on release readiness. • Use AI-assisted tools to accelerate test development, triage, and analysis while applying strong engineering judgment.

• Ship end-to-end platform features (UI → API → cloud services) that customers rely on daily. • Improve performance and reliability of low-latency, distributed services running in Kubernetes. • Increase deployment confidence and velocity through strong automated test coverage and CI/CD. • Deliver integrations that connect HiddenLayer into the broader cloud, AI, and security ecosystems. • Design and implement microservices in Go and/or Python, with clear API contracts and interoperability. • Build and operate cloud-native services on AWS or Azure, deployed as containerized workloads on Kubernetes. • Build and evolve customer-facing product experiences using TypeScript + React. • Own performance: profile, troubleshoot, and optimize p95/p99 latency, throughput, and resource efficiency. • Strengthen delivery: maintain test strategies (unit/integration/e2e) that enable continuous deployment. • Improve operational excellence: logging/metrics/tracing, alerting, on-call readiness, runbooks, and incident follow-ups. • Partner closely with Product, Security, and Engineering peers, communicate early, document decisions, and seek alignment. • Use AI-assisted development tools to accelerate delivery, while applying strong engineering judgment and security hygiene.

• Collaborate with the sales team to engage with clients and prospects, conducting technical discussions to understand their security challenges, objectives, and infrastructure requirements. • Conduct product demonstrations and presentations, showcasing the features, functionalities, and benefits of our AI security solutions to prospective clients. • Architect customized security solutions tailored to client needs, leveraging your understanding of cybersecurity principles, AI technologies, and industry best practices. • Lead the development and execution of proof of concept (PoC) projects, working closely with clients to validate the effectiveness and feasibility of our solutions in their environment. • Provide pre-sales and post-sales technical support to clients, addressing inquiries, troubleshooting issues, and ensuring a smooth deployment and integration process. • Conduct training sessions and workshops for clients and internal stakeholders, empowering them with the knowledge and skills to effectively utilize our AI security products. • Collaborate with cross-functional teams including product management, engineering, and research, to provide feedback from the field and contribute to product enhancements and roadmap. • Stay informed about market trends, emerging technologies, and competitive landscape in the AI security space, continuously enhancing your knowledge and expertise.

• Collaborate with the sales team to engage with clients and prospects, conducting technical discussions to understand their security challenges, objectives, and infrastructure requirements. • Conduct product demonstrations and presentations, showcasing the features, functionalities, and benefits of our AI security solutions to prospective clients. • Architect customized security solutions tailored to client needs, leveraging your understanding of cybersecurity principles, AI technologies, and industry best practices. • Lead the development and execution of proof of concept (PoC) projects, working closely with clients to validate the effectiveness and feasibility of our solutions in their environment. • Provide pre-sales and post-sales technical support to clients, addressing inquiries, troubleshooting issues, and ensuring a smooth deployment and integration process. • Conduct training sessions and workshops for clients and internal stakeholders, empowering them with the knowledge and skills to effectively utilize our AI security products. • Collaborate with cross-functional teams including product management, engineering, and research, to provide feedback from the field and contribute to product enhancements and roadmap. • Stay informed about market trends, emerging technologies, and competitive landscape in the AI security space, continuously enhancing your knowledge and expertise.