The Software Engineer III will report to the Technical Director.

Responsibilities:

• Operate with a sense of ownership.
• Design, develop, and run a fast, scalable, highly available game service all the way from conception to delivery to live service operations
• Work with designers, client engineering, and production teams to achieve gameplay goals
• Implement security best practices and original techniques to keep user data secure and prevent cheating
• Create and run automated testing, readiness testing, and deployment plans
• Monitor the performance and costs of the server infrastructure to improve our game
• Design and implement data transformation layers
• Collaborate with game server and web frontend teams to define API contracts
• Manage Release Ops / Live Ops of web services

Qualifications:

We encourage you to apply if you can meet most of the requirements and are comfortable opening a dialogue to be considered.

• 8+ years development of scalable back-end services
• BS degree in Computer Science or equivalent work experience
• Proficiency in Java required
• Proficiency in using frameworks such as Spring Boot
• Knowledge of paradigms like Domain Drive Development
• Experience with Cloud services like Amazon Web Services or Google Cloud Platform
• Experience with Redis
• Experience with Database Design and usage of large datasets in both relational (MySQL, Postgres) and NoSQL (Couchbase, DynamoDB) environments
• Experience defining API contracts and collaborating with cross-functional teams

Bonus:

• 3+ years of experience developing games using cloud services like AWS, Azure, Google Cloud Platform, or similar
• Proficient in technical planning, solution research, proposal, and implementation
• Background using metrics and analytics to determine the quality or priority
• Comfortable working across client and server codebases
• Familiar with profiling, optimising, and debugging scalable data systems
• Passion for making and playing games

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The Server Engineer will report to the Technical Director.

Responsibilities:

• Design, develop, and run a fast, scalable, highly available game service all the way from conception to delivery to live service operations
• Work with designers, client engineering, and production teams to achieve gameplay goals
• Implement security best practices and original techniques to keep user data secure and prevent cheating
• Create and run automated testing, readiness testing, and deployment plans
• Monitor the performance and costs of the server infrastructure to improve our game
• Design and implement data transformation layers
• Collaborate with game server and web frontend teams to define API contracts
• Manage Release Ops / Live Ops of web services

Qualifications:

We encourage you to apply if you can meet most of the requirements and are comfortable opening a dialogue to be considered.

• 6+ years development of scalable back-end services
• BS degree in Computer Science or equivalent work experience
• Proficiency in Java required
• Proficiency in using frameworks such as Spring Boot
• Knowledge of paradigms like Domain Drive Development
• Experience with Cloud services like Amazon Web Services or Google Cloud Platform
• Experience with Redis
• Experience with Database Design and usage of large datasets in both relational (MySQL, Postgres) and NoSQL (Couchbase, DynamoDB) environments
• Experience defining API contracts and collaborating with cross-functional teams

Bonus:

• 3+ years of experience developing games using cloud services like AWS, Azure, Google Cloud Platform, or similar
• Proficient in technical planning, solution research, proposal, and implementation
• Background using metrics and analytics to determine the quality or priority
• Comfortable working across client and server codebases
• Familiar with profiling, optimising, and debugging scalable data systems
• Passion for making and playing games

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As a senior member of the team, you will champion modern, AI-enhanced quality practices and help build a culture where continuous improvement, automation-first thinking, and data-driven decisions are embedded at every stage of product development. This is a hybrid position in Mountain View (Headquarters) and will require in-office work 2 days a week.

The base salary range for this full-time position is $210,000 to $257,000, plus equity and benefits. Our salary ranges are determined by role, level, and location. EarnIn provides excellent benefits for our employees, including healthcare, internet/cell phone reimbursement, and a learning and development stipend.

Quality Engineering & Test

Own end-to-end quality across iOS and Android applications and their supporting backend services, ensuring high confidence in weekly (or faster) releases. Design and implement comprehensive test strategies covering:

• Native mobile applications (iOS & Android)
• Mobile-to-backend integrations (REST APIs, auth flows, event-driven systems)
• Microservices and distributed systems
• Critical web workflows that intersect with mobile journeys
• Device, OS, browser, and network variability
• App lifecycle events, offline behavior, retries, and edge cases

Ensure critical user journeys are validated across mobile UI → API → backend → web touchpoints, preventing production escapes in high-impact flows. Partner with engineering teams to embed quality gates into the mobile release lifecycle, including pre-merge validation, release candidate verification, and post-deploy smoke testing.

Drive improvements in testability by introducing better logging, API contracts, observability hooks, feature flags, and deterministic state management. Establish meaningful quality metrics (crash analytics, defect trends, flaky tests, API reliability, release risk scoring) and surface actionable insights to engineering stakeholders.

Champion shift-left quality by influencing design reviews, API schema discussions, and acceptance criteria early in development.

AI-Driven Quality and Automation

Leverage AI to enhance mobile, backend, and web testing effectiveness, including:

• AI-assisted test case and test data generation
• Intelligent regression suite prioritization based on code changes
• Predictive defect detection and risk-based testing
• Flaky test detection and automated stabilization insights

Integrate AI-powered log intelligence, crash clustering, and anomaly detection into quality workflows. Continuously evaluate and experiment with AI-driven QA tools to increase coverage, reduce maintenance overhead, and accelerate release cycles.

Contribute to building an AI-augmented quality ecosystem that improves speed without compromising reliability.

Automation Excellence

Design, build, and scale robust automation frameworks using:

• XCUITest, Espresso, Appium (mobile automation)
• Playwright (web and mobile web validation)
• REST Assured or similar tools for API and service validation

Ensure frameworks are modular, maintainable, and optimized for scale across multiple teams. Integrate automated validation into CI/CD pipelines (Jenkins, GitHub Actions, etc.) to enable:

• Pre-merge quality gates
• Parallelized execution
• Environment-aware test runs
• Post-deployment smoke and regression coverage

Build developer-friendly tooling that enables:

• Self-service test execution
• Real-time reporting and dashboards
• Faster debugging and failure triage
• Scalable test data and environment management

Continuously reduce flakiness, improve signal quality, and optimize execution time across mobile and backend suites.

Performance, Scalability & Reliability

Design and execute performance validation across:

• Mobile app startup time and responsiveness
• API latency, throughput, and reliability
• Backend load and stress conditions
• Web performance for critical flows

Partner with engineering teams to analyze production logs, crash reports, browser telemetry, and service metrics. Lead root-cause analysis of complex cross-layer defects spanning mobile UI, APIs, backend services, and web surfaces.

Ensure reliability validation is embedded directly into release workflows.

Cross-Functional Collaboration and Leadership

Collaborate closely with mobile engineers, backend developers, web engineers, product managers, DevOps teams, and release managers to define clear, testable requirements and release criteria. Actively participate in sprint grooming, planning, stand-ups, and retrospectives.

Influence best practices around mobile-first design, API contracts, and release readiness. Support mobile app release activities, including release candidate validation, go/no-go recommendations, and post-release monitoring.

Mentor junior QA engineers and contribute to raising the technical bar in automation and cross-platform validation. Work effectively with globally distributed teams to coordinate testing across time zones.

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LLM & AI Pipeline Engineering - Design, build, and maintain production-grade LLM integration pipelines , including retrieval-augmented generation (RAG), prompt engineering, output parsing, and chain orchestration.

Develop and operate AI features within Jeeves's core financial products: spend categorization, document extraction, anomaly detection, financial Q&A, and automated reconciliation.

Implement structured output validation, fallback handling, and confidence scoring to ensure AI decisions meet reliability standards for financial use cases.

Evaluate and integrate AI frameworks and tools (LangChain, LlamaIndex, OpenAI API, Anthropic API, HuggingFace, vector databases) and advocate for the right tool for the job.

Establish prompt versioning and evaluation practices to ensure AI outputs remain accurate and consistent as models and data evolve.

Retrieval & Vector Search - Design and maintain vector search pipelines using databases such as Pinecone, Weaviate, or pgvector to power semantic search and RAG-based features.

Build document ingestion and chunking pipelines for Jeeves's financial data , processing invoices, receipts, policy documents, and transaction records.

Optimize retrieval quality through embedding model selection, chunk strategy, metadata filtering, and re-ranking techniques.

ML Model Serving & Operations - Collaborate with data scientists to take trained ML models from experimental notebooks to production serving infrastructure.

Build and maintain model serving endpoints with appropriate latency SLOs, input validation, and output monitoring.

Implement model performance monitoring and data drift detection to ensure production models remain accurate over time.

Support model retraining workflows by designing clean data pipelines and feature engineering that can be continuously updated.

Backend Integration & Reliability - Integrate AI services cleanly with Jeeves's backend microservices , designing clear API contracts, circuit breakers, and graceful degradation patterns.

Write high-quality, testable backend code in Python or Go/Node.js to power AI-integrated features.

Instrument AI components with structured logging, distributed tracing, latency dashboards, and alerting to ensure operational visibility.

Collaboration & Growth - Partner with Product, Backend Engineering, and Data Science to define the AI roadmap and translate requirements into reliable systems.

Contribute to a culture of quality by writing design docs, reviewing peers' AI system designs, and sharing learnings openly.

Help grow the AI engineering practice at Jeeves by establishing patterns, tooling, and best practices that the broader team can build on.

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LLM & AI Pipeline Engineering - Design, build, and maintain production-grade LLM integration pipelines , including retrieval-augmented generation (RAG), prompt engineering, output parsing, and chain orchestration.

Develop and operate AI features within Jeeves's core financial products: spend categorization, document extraction, anomaly detection, financial Q&A, and automated reconciliation.

Implement structured output validation, fallback handling, and confidence scoring to ensure AI decisions meet reliability standards for financial use cases.

Evaluate and integrate AI frameworks and tools (LangChain, LlamaIndex, OpenAI API, Anthropic API, HuggingFace, vector databases) and advocate for the right tool for the job.

Establish prompt versioning and evaluation practices to ensure AI outputs remain accurate and consistent as models and data evolve.

Retrieval & Vector Search - Design and maintain vector search pipelines using databases such as Pinecone, Weaviate, or pgvector to power semantic search and RAG-based features.

Build document ingestion and chunking pipelines for Jeeves's financial data , processing invoices, receipts, policy documents, and transaction records.

Optimize retrieval quality through embedding model selection, chunk strategy, metadata filtering, and re-ranking techniques.

ML Model Serving & Operations - Collaborate with data scientists to take trained ML models from experimental notebooks to production serving infrastructure.

Build and maintain model serving endpoints with appropriate latency SLOs, input validation, and output monitoring.

Implement model performance monitoring and data drift detection to ensure production models remain accurate over time.

Support model retraining workflows by designing clean data pipelines and feature engineering that can be continuously updated.

Backend Integration & Reliability - Integrate AI services cleanly with Jeeves's backend microservices , designing clear API contracts, circuit breakers, and graceful degradation patterns.

Write high-quality, testable backend code in Python or Go/Node.js to power AI-integrated features.

Instrument AI components with structured logging, distributed tracing, latency dashboards, and alerting to ensure operational visibility.

Collaboration & Growth - Partner with Product, Backend Engineering, and Data Science to define the AI roadmap and translate requirements into reliable systems.

Contribute to a culture of quality by writing design docs, reviewing peers' AI system designs, and sharing learnings openly.

Help grow the AI engineering practice at Jeeves by establishing patterns, tooling, and best practices that the broader team can build on.

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