Senior Software Engineer - Orchestration Language Team
Apollo GraphQL2 weeks ago
Location
United States or Canada (remote)
Workplace
Remote
Type
Full Time
Salary
USD 165,000 – 195,000
Level
Senior
Role
Senior Software Engineer
Posted
Jul 7, 2026
Full TimeRemoteSenior
The role
Summary
Join Apollo GraphQL's Orchestration Language team as a Senior Software Engineer to architect and maintain the MCP Server, MCP Apps, and GraphOS MCP Tools—critical infrastructure enabling AI agents to discover and interact with federated GraphQL graphs. This is a build-and-define role where you'll shape the emerging MCP/Skills ecosystem for production AI workflows, balancing ambitious new capabilities with operational excellence, working primarily in Rust alongside Federation and platform engineering teams.
What you'll do
Design and develop MCP Server and GraphOS MCP Tools: Architect, implement, and maintain Apollo's Model Context Protocol (MCP) server components in idiomatic Rust, ensuring full spec compliance and compatibility with evolving MCP standards, including breaking changes. Drive technical decisions around performance, reliability, and extensibility that enable AI agents to reliably interact with federated GraphQL platforms.
Deliver rapid customer-driven iterations: Ship new MCP capabilities on a near-weekly basis in direct response to customer feedback and market demands. Partner closely with product and customer teams to prioritize features, debug real-world issues, and translate use cases into technical roadmap items that accelerate agent adoption.
Build and productionize Skills for AI agent workflows: Design, develop, and package reusable Skills that make AI agent interactions with GraphQL platforms operationally useful and reliable in production environments. Establish patterns and tooling for Skills distribution across Apollo's product ecosystem, enabling standardized, discoverable, composable agent capabilities.
Optimize tool discovery and introspection systems: Improve AI agent visibility into available GraphQL capabilities through enhanced tool discovery mechanisms, intelligent partitioning strategies, and context-size optimization. Reduce friction in how agents find and select the correct tools for complex federated graph queries and mutations.
Expand GraphOS platform capabilities as MCP tools: Systematically surface GraphOS features—schema validation, composition orchestration, health metrics, deployment tools—as discoverable MCP tools for AI agents. Enable agents to perform platform operations autonomously while maintaining security and reliability guardrails.
Own projects end-to-end with operational focus: Lead projects from design document through production delivery, including post-launch maintenance, debugging, and codebase health improvements. Treat operational reliability, bug resolution, and system resilience as first-class priorities, not secondary maintenance tasks. Participate in a lightweight on-call rotation to ensure system stability.
Cross-functional collaboration and federation integration: Work fluidly across Orchestration Language, Federation, Connectors, and Client engineering teams to ensure MCP/Skills work integrates seamlessly with core GraphQL composition and query planning infrastructure. Contribute to federation-level improvements where MCP capabilities intersect with distributed graph architecture.
Engage with customers and community: Participate in direct customer conversations, community forums, and debugging sessions to understand real-world MCP use cases, pain points, and emerging needs. Translate customer insights into concrete roadmap recommendations and help shape how the agent ecosystem evolves around GraphQL.
What we look for
Technical
Advanced Rust systems programmingProduction-grade expertise writing performant, maintainable Rust code. Demonstrated ability to design and implement fault-tolerant, event-driven systems with attention to memory safety, concurrency patterns, and performance optimization. Candidates with deep low-level C/C++ systems background actively ramping into Rust are also strong candidates.
Distributed systems and protocol implementationStrong foundation in stateless/fault-tolerant architectures, event-driven patterns, distributed computing paradigms, and protocol design/implementation. Understanding of how to implement emerging specifications like MCP with evolving standards and breaking changes.
AI developer tooling proficiencyActive, hands-on experience using modern AI developer tools (e.g., Claude, Cursor, Copilot). Can articulate concrete use cases, limitations, and what 'good' looks like from both user and platform perspectives. This signal matters significantly, even without direct MCP experience.
Systems debugging and operational excellenceStrong debugging instincts and genuine enthusiasm for hands-on operational work, production incident response, and reliability engineering. Comfortable navigating complex system behaviors, tracing issues across distributed components, and implementing preventative measures.
Education
Bachelor's degree in Computer Science, Engineering, or equivalentFormal education or demonstrated equivalent practical experience in computer science, software engineering, or closely related discipline. Strong foundational knowledge of algorithms, data structures, systems design, and networking concepts.
Experience
Senior-level systems engineering background3+ years building, scaling, or maintaining production systems at a senior level, with demonstrated ownership of complex technical initiatives. Experience should span full lifecycle from architecture through operational support.
Modern protocol and API ecosystem developmentTrack record working with emerging or evolving protocols, standardized APIs, or specification-driven development. Experience adapting to breaking changes in standards while maintaining backward compatibility where needed.
Hands-on operational and on-call experienceComfort with on-call rotations, production incident response, and reliability-focused engineering. Proven ability to balance feature development with maintenance, debugging, and codebase health initiatives.
Skills
Required skills
RustAdvanced proficiency in Rust for systems programming. Deep understanding of ownership model, trait systems, error handling, async/await patterns, and performance optimization.
Systems architecture and designAbility to architect scalable, fault-tolerant distributed systems with emphasis on reliability, performance, and operational maintainability.
Protocol design and implementationExperience designing, implementing, or maintaining network protocols, message formats, or standardized communication interfaces. Comfort working with specification-driven development.
Debugging and troubleshootingExceptional diagnostic skills for complex systems. Proficiency with observability tools, profiling, and root-cause analysis in production environments.
Cross-team collaborationDemonstrated ability to work effectively across engineering teams with different specializations, communicate technical complexity clearly, and align on shared outcomes.
Nice to have
Model Context Protocol (MCP) implementation experienceDirect hands-on experience implementing MCP servers, reading the specification, or building tools and integrations against MCP-compatible systems.
AI Skills and agent workflow developmentExperience building, maintaining, or packaging reusable capabilities for AI agent workflows, including tool design for agent discoverability and execution.
GraphQL platform experienceFamiliarity with GraphQL ecosystem, federated graph architecture, query planning, composition, or large-scale GraphQL runtime systems.
Large-scale runtime systemsExperience architecting or maintaining high-performance runtime systems serving millions of requests or managing complex distributed state.
High-escalation incident responseDemonstrated ability to navigate, debug, and resolve critical incidents in high-stakes, high-visibility environments with rigorous postmortem and prevention processes.
C/C++ systems programming backgroundDeep systems-level programming experience in C or C++ that translates well to Rust's systems programming model and performance constraints.
Compensation & benefits
Salary
USD 165,000 – 195,000 (annual)
Stock options
Available
Benefits
Equity compensation
Stock options reflecting your role in building Apollo's strategically critical AI infrastructure. Potential for meaningful long-term wealth creation as the MCP/agent ecosystem matures.
Comprehensive health coverage
Competitive medical, dental, and vision insurance with Apollo covering a significant portion of premiums for you and your family.
Retirement planning
401(k) plan with employer matching contributions to support long-term financial security.
Unlimited PTO and flexible work
Remote-first culture with flexible work arrangements. Unlimited paid time off to support work-life balance and personal wellbeing.
Professional development
Learning budget, conference attendance, and internal technical mentorship to stay current with Rust, systems engineering, and AI/agent landscape evolution.
Lightweight on-call rotation
Unlike many infrastructure roles, the Orchestration Language team maintains a notably light on-call load, minimizing disruption to personal time while maintaining system reliability.
Remote-first, globally distributed team
Work alongside world-class systems engineers across multiple time zones. Async-friendly communication patterns and flexible meeting scheduling to accommodate distributed teams.
Interview process
- 1Initial screening call — 30-minute conversation with a recruiter to discuss your background in systems engineering, Rust experience, and interest in the MCP/agent space. Come prepared to discuss concrete examples of AI tools you actively use and limitations you've observed.
- 2Technical deep-dive interview — 60-75 minute conversation with one or more team engineers covering systems design, protocol implementation, Rust proficiency, and operational thinking. Expect questions about distributed systems tradeoffs, debugging strategies, and how you'd approach designing MCP server components for reliability and performance.
- 3Systems design and architecture discussion — Collaborative session exploring how you'd design key MCP infrastructure components or Skills systems. This may involve whiteboarding/diagramming, discussing tradeoffs between different architectural approaches, and reasoning about scalability and reliability constraints.
- 4Customer and collaboration scenarios — Discussion of how you've handled cross-team collaboration, customer engagement, and translating real-world needs into technical solutions. May include scenario-based questions about handling conflicting requirements or navigating ambiguous product needs.
- 5Final round with team leadership — Conversation with the team lead or manager to explore team fit, your growth aspirations, and how this role aligns with your career trajectory. Opportunity to ask detailed questions about roadmap, technical direction, and team dynamics.
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Apollo GraphQL develops open-source GraphQL tools and a leading GraphQL implementation platform, empowering teams to build, query, and manage APIs efficiently.
Remote, USAFounded 2015apollographql.com
Tech Stack
Languages
RustTypeScript/JavaScript
Frameworks
TokioTonic/gRPC (potential)
Databases
PostgreSQL or similar
Tools
Docker and KubernetesGit and GitHubObservability stack (Datadog, Prometheus, etc.)VS Code, JetBrains IDEs
Other
Model Context Protocol (MCP) specificationGraphQL and federated graph architectureAI agent platforms and LLMsAPI design and tooling standards