The Software Engineer (Java) operates at a senior engineer level, driving technical direction across core product domains
The role combines deep hands‑on Java engineering with architectural ownership — from high‑level system design and technology selection to leading delivery of critical, cross‑cutting platform capabilities
At this level, engineering excellence extends beyond implementation: the engineer determines the right approach to complex problems, communicates technical decisions across functions, participates in hiring, and is accountable for the long‑term health of the systems they own
This level demands sound judgment on technology and business trade‑offs and the ability to act as a technical lead
A defining expectation is mastery of AI‑assisted engineering — leveraging agentic AI tools as force multipliers while retaining full ownership of architecture, quality, and technical outcomes
Architecture and technology leadership: Lead high‑level design for complex, cross‑service features. Evaluate and select appropriate technologies, frameworks, and architectural patterns before delegating implementation. Produce and maintain architecture documentation: design docs, ADRs, tech specs, and wiki pages
Advanced Java feature implementation: Own and implement critical product components — including prototyping, architecture validation, and production‑grade code. Ensure correctness, performance, and long‑term maintainability with comprehensive test coverage (unit, integration, contract, component)
Technical roadmap contribution: Drive the engineering agenda for assigned product areas. Proactively identify gaps in requirements, architectural limitations, and technical risks. Contribute to product roadmap planning and delivery estimation. Participate in hiring processes
Cross‑functional technical communication: Drive technical communication across engineering, product, DevOps, and ML teams. Communicate technical decisions clearly to non‑technical stakeholders. Produce design documents and participate in tech talks and knowledge‑sharing sessions
AI‑augmented engineering: Direct agentic AI tools (Claude Code, Codex, or equivalent) across the full engineering workflow — code generation, testing, refactoring, debugging, and documentation. Demonstrated ability to apply advanced prompt engineering, manage AI context limitations, compose multi‑agent orchestration workflows, and critically evaluate AI‑generated outputs for correctness, security, and quality. Ability to establish guardrails and improve agent configurations to raise the quality bar. AI proficiency amplifies — it does not replace — deep engineering judgment and technical accountability
Benefits
Health insurance for the whole family, free of charge
World‑class relocation program with visa and citizenship sponsorship
Great office culture
OKR‑based management
Equity options
Software architecture and design patterns: Expertise in monolith and microservices architectural styles — including migration patterns and domain‑driven decomposition. Inter‑process communication design (REST, gRPC, messaging), transaction management in distributed systems (Sagas, 2PC), CQRS, Event Sourcing, and external API design focused on scalability, security, and documentationCloud‑native platforms and infrastructure: Experience designing high‑availability and high‑load systems on GCP (preferred), AWS, and Azure. Cloud security best practices: IAM, VPC, data encryption, JWT/JWS/JWE. Infrastructure as Code (Terraform or equivalent) and Twelve‑Factor App methodologyExpert Java engineering: Deep understanding of Java internals — GC tuning, Collections Framework, advanced concurrency (java.util.concurrent, multithreading), NIO/NIO2, performance profiling, and heap‑dump analysis. Mastery of Spring Framework (IoC/DI, bean lifecycle, Spring Boot). SOLID principles, Clean Code practices, and GoF design patternsObservability, reliability and deployment: Implementing full observability stacks: structured logging, distributed tracing, metrics, and alerting. SLI/SLO/SLA frameworks. Deployment strategies: Rolling Updates, Blue/Green Deployments, Canary ReleasesAI‑assisted engineering: Practitioner‑level command of agentic AI tools applied to software engineering — encompassing prompt engineering techniques, AI context management and its limitations, sub‑agents, skills and plugins, multi‑agent orchestration, and team‑of‑agents architectures. Experience with Claude Code (Anthropic), Codex (OpenAI), or equivalent is mandatory
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The role combines deep hands‑on Java engineering with architectural ownership — from high‑level system design and technology selection to leading delivery of critical, cross‑cutting platform capabilities
At this level, engineering excellence extends beyond implementation: the engineer determines the right approach to complex problems, communicates technical decisions across functions, participates in hiring, and is accountable for the long‑term health of the systems they own
This level demands sound judgment on technology and business trade‑offs and the ability to act as a technical lead
A defining expectation is mastery of AI‑assisted engineering — leveraging agentic AI tools as force multipliers while retaining full ownership of architecture, quality, and technical outcomes
Architecture and technology leadership: Lead high‑level design for complex, cross‑service features. Evaluate and select appropriate technologies, frameworks, and architectural patterns before delegating implementation. Produce and maintain architecture documentation: design docs, ADRs, tech specs, and wiki pages
Advanced Java feature implementation: Own and implement critical product components — including prototyping, architecture validation, and production‑grade code. Ensure correctness, performance, and long‑term maintainability with comprehensive test coverage (unit, integration, contract, component)
Technical roadmap contribution: Drive the engineering agenda for assigned product areas. Proactively identify gaps in requirements, architectural limitations, and technical risks. Contribute to product roadmap planning and delivery estimation. Participate in hiring processes
Cross‑functional technical communication: Drive technical communication across engineering, product, DevOps, and ML teams. Communicate technical decisions clearly to non‑technical stakeholders. Produce design documents and participate in tech talks and knowledge‑sharing sessions
AI‑augmented engineering: Direct agentic AI tools (Claude Code, Codex, or equivalent) across the full engineering workflow — code generation, testing, refactoring, debugging, and documentation. Demonstrated ability to apply advanced prompt engineering, manage AI context limitations, compose multi‑agent orchestration workflows, and critically evaluate AI‑generated outputs for correctness, security, and quality. Ability to establish guardrails and improve agent configurations to raise the quality bar. AI proficiency amplifies — it does not replace — deep engineering judgment and technical accountability
Benefits
Health insurance for the whole family, free of charge
World‑class relocation program with visa and citizenship sponsorship
Great office culture
OKR‑based management
Equity options
Software architecture and design patterns: Expertise in monolith and microservices architectural styles — including migration patterns and domain‑driven decomposition. Inter‑process communication design (REST, gRPC, messaging), transaction management in distributed systems (Sagas, 2PC), CQRS, Event Sourcing, and external API design focused on scalability, security, and documentationCloud‑native platforms and infrastructure: Experience designing high‑availability and high‑load systems on GCP (preferred), AWS, and Azure. Cloud security best practices: IAM, VPC, data encryption, JWT/JWS/JWE. Infrastructure as Code (Terraform or equivalent) and Twelve‑Factor App methodologyExpert Java engineering: Deep understanding of Java internals — GC tuning, Collections Framework, advanced concurrency (java.util.concurrent, multithreading), NIO/NIO2, performance profiling, and heap‑dump analysis. Mastery of Spring Framework (IoC/DI, bean lifecycle, Spring Boot). SOLID principles, Clean Code practices, and GoF design patternsObservability, reliability and deployment: Implementing full observability stacks: structured logging, distributed tracing, metrics, and alerting. SLI/SLO/SLA frameworks. Deployment strategies: Rolling Updates, Blue/Green Deployments, Canary ReleasesAI‑assisted engineering: Practitioner‑level command of agentic AI tools applied to software engineering — encompassing prompt engineering techniques, AI context management and its limitations, sub‑agents, skills and plugins, multi‑agent orchestration, and team‑of‑agents architectures. Experience with Claude Code (Anthropic), Codex (OpenAI), or equivalent is mandatory
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