Tech Lead - Software Engineering

DataVisor•Mountain View, CA

23h•$170,000 - $220,000

About The Position

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.

Requirements

8+ years of software development experience.
2+ years of technical leadership experience as a tech lead, staff engineer, engineering manager, or similar role.
Proven ability to lead technical outcomes across a team, including work you did not personally implement.
Deep production experience with Java, along with working proficiency in Python and Shell scripting.
Experience designing, building, shipping, and operating distributed real-time systems at scale.
Strong knowledge of computer systems, relational databases, and SQL.
Experience building and optimizing multithreaded and concurrent applications.
Hands-on experience with Cassandra, Yugabyte, Flink, Spark, or Kafka.
Experience with the Spring Framework.
Demonstrated use of AI coding tools such as Claude Code, Cursor, GitHub Copilot, or similar tools in real production work.
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.
Strong verification discipline, with the ability to validate model outputs against source code, logs, documentation, and production behavior.
Bachelor’s degree in Computer Science or a related field is required.

Nice To Haves

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 building with LLM APIs, agent frameworks, tool calling, RAG, or MCP.
Experience writing evaluations or regression tests for non-deterministic systems.
Experience hiring, managing, or mentoring engineers.
Experience with test-driven development.

Responsibilities

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.