Tech Lead of Software Engineering

• As part of the Platform Engineering team, you will help build DataVisor’s next-generation machine learning platform, combining our proprietary unsupervised machine learning technology with supervised machine learning algorithms to power real-time fraud detection at scale
• As fraud attacks become increasingly sophisticated, real-time detection is more critical than ever
• Our platform team is responsible for designing and developing the architecture that makes scalable, real-time detection possible, including streaming systems, storage layers, and training pipelines that support our core detection capabilities
• We are looking for a creative, hands-on engineering leader to help expand our streaming and database systems, improve our core detection algorithms, and automate the end-to-end training process
• This role is ideal for someone who enjoys solving complex distributed systems problems, mentoring engineers, and driving technical execution in a high-impact environment
• Join us as we continue to push the boundaries of fraud detection, machine learning infrastructure, and large-scale data processing
• Own the technical direction of the real-time detection platform, including streaming, storage, and the training pipelines that support it
• Translate product and engineering roadmaps into clear technical plans, milestones, and execution priorities
• Proactively identify technical risks, dependencies, and trade-offs before they impact delivery
• Lead design and architecture reviews, make technical trade-off decisions, and document the reasoning behind key decisions
• Stay hands-on by coding, reviewing code, debugging issues, and supporting the team during production incidents
• Mentor engineers and raise the bar for system design, code quality, operational excellence, and technical execution
• Own the operational health of the platform, including alert quality, on-call load, incident follow-through, and root-cause prevention
• Partner directly with Product, TAM, and customer-facing teams on customer-impacting issues, ensuring clear impact assessment, prioritization, ownership, and next steps
• Define how the team uses AI agents and AI-assisted tools in engineering workflows, including verification standards and safe usage practices
• Build, evaluate, and improve LLM- and agent-assisted tools for engineering and operations use cases, such as triage, root-cause analysis, alert summarization, and evaluation harnesses

Benefits

• Stock Options
• Commuter Passes
• Paid Time Off
• Catered Lunch and Dinner
• Team and Company Outings

• Demonstrated use of AI coding tools such as Claude Code, Cursor, GitHub Copilot, or similar tools in real production work
• Strong knowledge of computer systems, relational databases, and SQL
• Ability to set team-level standards for AI-assisted engineering, including how tools are used, how outputs are verified, and when AI-generated suggestions should be rejected
• Bachelor’s degree in Computer Science or a related field is required
• Experience with the Spring Framework
• Experience designing, building, shipping, and operating distributed real-time systems at scale
• Proven ability to lead technical outcomes across a team, including work you did not personally implement
• Experience building and optimizing multithreaded and concurrent applications
• 2+ years of technical leadership experience as a tech lead, staff engineer, engineering manager, or similar role
• Hands-on experience with Cassandra, Yugabyte, Flink, Spark, or Kafka
• Strong verification discipline, with the ability to validate model outputs against source code, logs, documentation, and production behavior
• 8+ years of software development experience
• Deep production experience with Java, along with working proficiency in Python and Shell scripting
• Experience in fraud, risk, payments, financial services, or another domain where false negatives carry significant business or customer impact
• Experience owning ML platforms or large-scale training pipelines
• Experience with Kubernetes
• Experience writing evaluations or regression tests for non-deterministic systems
• Experience building with LLM APIs, agent frameworks, tool calling, RAG, or MCP
• Experience hiring, managing, or mentoring engineers
• Experience with test-driven development