Senior Machine Learning Performance Engineer (Remote)

• Design, build, and evolve MCP servers, tools, and agent-facing APIs with concise schemas, predictable errors, safe mutations, and clear outcome-oriented contracts.

• Develop accessible, responsive, and performant React and TypeScript experiences that make agent capabilities, MCP tools, and A2A interactions easy to discover, configure, and use.

• Build reusable components, design-system patterns, and frontend architecture that support consistent, scalable user experiences across AI-powered products.

• Integrate GraphQL and REST APIs, SDKs, streaming responses, and real-time data into reliable, user-friendly AI workflows.

• Optimize token and context efficiency through dynamic tool discovery, lazy loading, bounded responses, pagination, selective field retrieval, caching, and reduced tool-call loops.

• Improve end-to-end performance and reliability across front-end clients, gateways, MCP servers, search services, and downstream product systems through observability, tracing, SLOs, and production diagnostics.

• Build semantic retrieval capabilities using embeddings, chunking, vector indexes, hybrid search, metadata and permission filters, ranking, reranking, and freshness strategies.

• Define and operate AI/ML quality programs with JTBD-based evaluations, benchmark datasets, groundedness and relevance metrics, hallucination and bias detection, safety testing, and human feedback.

• Integrate automated evaluations into CI/CD and release gates to detect regressions across model, prompt, tool, and retrieval changes.

• Implement enterprise security and partner cross-functionally to deliver maintainable, well-tested AI integrations, including OAuth 2.1, tenant isolation, audit logging, prompt-injection defenses, and confirmation flows for high-impact actions.

• 7+ years of software engineering experience building and operating enterprise systems, APIs, or cloud-native products.

• Strong proficiency in TypeScript/JavaScript and modern front-end development with React; experience building accessible, responsive, and performant web applications.

• Hands-on experience designing or integrating MCP servers, tools, agent-facing APIs, or related context and agent frameworks.

• Demonstrated ability to apply distributed-systems principles, including concurrency, connection pooling, caching, retries, timeouts, backpressure, autoscaling, and load shedding.

• Experience measuring and improving latency, throughput, saturation, error rates, availability, token consumption, and end-to-end task cost.

• Practical experience with AI/ML evaluation and quality engineering, including benchmark design, groundedness, relevance, safety, hallucination detection, bias analysis, monitoring, and regression prevention.

• Knowledge of semantic search and retrieval systems, including embeddings, vector databases or indexes, hybrid retrieval, ranking, reranking, and permission-aware filtering.

• Experience integrating GraphQL, REST, JSON Schema, streaming APIs, SDKs, and event-driven systems into reliable product experiences.

• Proficiency in at least one additional systems or back-end language such as Python or Go, with strong testing and API design practices.

• Proven ability to lead cross-functional engineering initiatives, communicate clearly with technical and non-technical partners, and mentor other engineers.

Preferred Skills

• Familiarity with MCP architecture, A2A specification, and agent collaboration frameworks (e.g., MCP servers, UI clients, and adapters).

• Experience with observability, vector databases, and secure model-to-model communication.

• Background in enterprise integration patterns, API governance, or developer experience platforms.

• Contributions to open-source AI frameworks or standards development initiatives.

Why Join

• Work at the forefront of AI interoperability and system design.

• Influence emerging standards that define how agents and models communicate.

• • Collaborate with top engineers and research partners building the next layer of enterprise AI infrastructure.