Staff Engineer, AI Platform

About the role

The platform secures data across SaaS, GenAI, email, and endpoints while enabling autonomous risk response. Security teams automate enforcement and coaching to prevent data loss without slowing innovation. The role delivers scalable backend infrastructure that keeps detection and remediation always on.

Software engineers turn product ideas into working code. Engineers work in small teams, review each other's work, and ship in small batches. Most teams follow agile practices such as sprints and daily standups. Engineers also write tests, fix bugs, and improve performance. The field values clear communication as much as technical skill. Engineers spend part of every week on planning, code review, and debugging, not just writing new code. The ability to explain a technical decision in plain words separates strong engineers from the rest.

Key facts

What you'll do

Design and operate authentication and API services that remain highly available and secure for enterprise customers.

Maintain and evolve mission-critical internal databases and services that underpin data protection workflows.

Ensure downstream AI models can scale for real-time inferencing with low latency and high reliability.

Instrument streaming data services to expose per-customer utilization metrics for observability and control.

Write and maintain documentation for internal and public services so teams can integrate and operate safely.

Requirements

Demonstrate expertise in one or more systems or high-level programming languages such as Go, Java, Python, or C++, with willingness to learn additional languages.

Operate scalable systems at thousands of requests per second while maintaining three nines of reliability in production.

Deliver complex software at scale, handling substantial data volumes or millions of users with mature deployment, monitoring, and reliability practices.

Build Agentic AI systems at scale, including semantic ingestion, summarization, and querying, plus orchestration of hundreds of parallel agents.

Manage large-scale distributed storage and databases, including SQL and NoSQL systems such as Postgres and Cassandra.

Decompose complex business problems and lead a team to design and execute solutions end to end.

Process and manage large scale or real-time complex data pipelines using tools such as Kafka, Flink, Snowflake, and Databricks.

Bring demonstrable technical leadership in scalable architectures with 8 or more years of hands-on experience.

Practical notes

Workplace is Hybrid, and employment type is FullTime within the R&D department.

Typical interview steps

Hiring for engineering roles usually starts with a recruiter screen, followed by one or two technical rounds. Candidates often solve a coding problem, discuss past projects, and answer system design questions. Some loops include a take-home task. Final rounds typically cover team fit and give candidates a chance to ask questions. Interviewers look for how you break down an unfamiliar problem, not just whether you reach the answer. Practicing a few problems aloud and reviewing your own past projects are the best preparation.

Good to know

Roles in AI Platform often combine backend engineering with data infrastructure and reliability responsibilities.

Streaming systems and agentic AI workflows rely on event-driven architectures and distributed processing patterns.

Scalable data platforms commonly use SQL and NoSQL stores alongside stream processors for real-time insights.

Security and privacy domains require strong isolation, auditability, and automated response mechanisms across services.

Engineering leadership in large-scale systems requires clear interfaces, observability, and cross-team collaboration.

Career growth

Engineering careers usually progress from individual contributor to senior, staff, and principal levels. Some engineers move into management and lead teams of five to twenty people. Others stay on the technical track. Growth follows demonstrated impact, not tenure alone. A typical engineering ladder has clear levels with defined expectations for scope, quality, and mentorship. Moving up usually requires owning outcomes end to end rather than completing assigned tickets.

About the company

Nightfall is the agentic, all-in-one data loss prevention (DLP) & AI data security platform that prevents data leaks, gets visibility into data flows, and stops data exfiltration across SaaS, gen AI apps, endpoints, and more.