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We are hiring a Sr. software engineer for the Telemetry Data Platform team, which carries product usage telemetry & performance telemetry for the entire platform on a stack built with Kubernetes, Kafka, and ClickHouse. We are distributed across Israel, India, and the Americas. 
This is a full stack role with its center of gravity firmly on telemetry development, data pipelines, Kubernetes and DevOps work, supporting Billions of records per day. You will be equally comfortable designing a distributed system and owning it in production. If you like following a system from the event that fires to the chart that renders - and you want the pager for it - you will feel at home. 
You will lead design and delivery of complex, multi-service features, own technical decisions within a domain, and are fully self-directed - escalating only genuine ambiguities and driving decisions to closure. Of the ten critical skills at this level, incident response management is the only one set at Expert; the rest are Experienced. 
What youll do:
Telemetry data pipelines:
Design, build, and own backend services for distributed data streaming and processing that handle high-volume, high-cardinality event data with predictable latency and no silent data loss. 
Build and maintain Kafka-based streaming pipelines and the pipeline components that feed ClickHouse. 
Own data modeling and query performance in ClickHouse - partitioning, sort keys, materialized views, retention, and the cost curve that comes with all of it. 
Partner with product and platform teams to shape requirements for telemetry ingestion and processing, then drive the solutions to production. 
Kubernetes, DevOps, and production ownership:
Deploy, scale, and operate services in production Kubernetes environments, including Helm-based deployments and CI/CD pipelines that make releases repeatable and safe to roll back.
Own observability for what you build - meaningful metrics, useful logs, real tracing, and alerts that fire on customer impact rather than on noise.
Debug and resolve production incidents independently, participate in on-call, run root-cause analysis, and operate against defined service level objectives.
Full stack engineering and technical leadership:
Own the full development lifecycle for your work, and build the backend services and APIs that expose telemetry to internal consumers, product surfaces, and AI agents - including API contracts, data models, and schema migrations.
Contribute to the analytics front end - dashboards, funnels, and exploration tools - with an eye on performance against large result sets, and improve the web and mobile capture SDKs.
Lead the design of complex, multi-service features across team boundaries, drive decisions to closure, and write the design docs and postmortems that outlive the conversation.
Raise the bar through code review, test strategy, and automation coverage, and mentor engineers on the team.

We're looking for a Staff AI Security Specialist who sits at the intersection of AI and Security and is happiest building. This is an applied role: you'll spend your time reasoning about how agentic systems break and then writing the code that stops it, from prototype to shipped control in collaboration with Security Engineers.
What you get to do in this role:
Analyze the security of AI agent systems - how the harness assembles context and drives the tool-calling loop, how agents plan and delegate, what they persist to memory, and how all of it responds to untrusted input - and find where an adversary can subvert them
Design and build security controls for agentic systems: static analysis of agent and tool configurations, runtime policy enforcement, behavioral detection, and mitigations for tool and goal hijacking
Build working prototypes end to end, then partner with engineering to harden the ones that prove out
Threat model new AI architectures and features before they ship, and turn findings into concrete controls rather than a list of risks
Red-team our own agents and our own defenses: indirect prompt injection, tool abuse, privilege escalation across agent boundaries, data exfiltration through model and tool channels
Build repeatable evaluations and benchmarks so we can measure whether a control actually holds, and prove it as models and products change underneath us
Track offensive and defensive AI security research and judge quickly what's real, what's noise, and what should change our roadmap
Advise product and platform teams building agentic features on secure design, and raise the team's collective depth in AI security
Publish what's worth publishing - internal research, patents, and external talks