About the role
Blackpoint Cyber is the leading provider of world-class cybersecurity threat hunting, detection and remediation technology. Founded by former National Security Agency (NSA) cyber operations experts who applied their learnings to bring national security-grade technology solutions to commercial customers around the world, Blackpoint Cyber is in hyper-growth mode, fueled by a recent $190m series C round. SUMMARY We're building a new engineering team under the SVP, Engineering & AI/ML to accelerate Blackpoint Cyber's ML/AI capability. As a Senior Software Engineer on this team, you'll own back-end systems and data/ML pipelines end-to-end, work cross-functionally across engineering, and be mentored directly by the SVP in applied ML and Generative AI.
RESPONSIBILITIES Lead and drive back-end projects across the stack, including Python and Node.js/Go services hosted on Kubernetes, owning delivery end-to-end. Build Generative AI workflows and deployments Build, optimize, and maintain data pipelines powering AI/ML workflows and model deployments, working independently and devising solutions based on established engineering patterns. Design and consume APIs (REST, GraphQL, or gRPC), balancing performance, scale, and resiliency. Contribute to architecture and design decisions for AI/ML-enabled features, partnering with the SVP, Engineering & AI/ML and other engineering leads. Mentor other engineers and lead them through complex, cross-functional projects. Communicate technical solutions and progress directly to leadership, aligning them to business objectives.
REQUIREMENTS 5-7 years of back-end software engineering experience, with a track record delivering scalable, high-performance systems in fast-paced, enterprise environments. Python required. Familiarity with TypeScript and/or Java. Strong experience with microservices architecture on Kubernetes. Strong database design/performance tuning across SQL (PostgreSQL, MySQL) and NoSQL (MongoDB). Master's degree in Computer Science, Machine Learning, or a related field, or demonstrated equivalent applied ML experience. Familiarity with ML/AI concepts and workflows (MLOps, model deployment/monitoring) — we're not requiring deep ML specialization; strong engineers with the right foundation and appetite to grow into this space are encouraged to apply. Deep understanding of back-end security best practices: securing APIs, handling sensitive data, authentication/authorization (OAuth, JWT). Comfortable in an Agile environment; able to break down large initiatives independently.
