AI Engineer

About Emergence AI

Emergence AI is building AI agents that don't just generate answers—they investigate, reason, and help organizations make better decisions. Our platform enables enterprises to deploy intelligent agents that understand complex data, collaborate across business systems, and automate high-value work with the transparency, traceability, and reliability required for production environments. Founded by veterans of IBM Research, Google, Microsoft, and Amazon, we're a small team of researchers, engineers, and builders working at the intersection of frontier AI research and real-world customer challenges. Everyone owns meaningful work, collaborates directly with customers, and helps shape the future of our platform.

About the Role

As an AI Engineer, you'll build customer-facing capabilities across Emergence's Enterprise Agent Platform, working directly with Product Management, Customer Success, and enterprise customers to turn complex business problems into production-grade AI systems. You'll operate as an AI-native engineer, using AI development tools to accelerate delivery while owning architecture, engineering quality, and customer outcomes. We care more about engineering judgment, ownership, and the ability to learn than expertise in any specific language or framework.

Responsibilities

• Build the core Enterprise Agent Platform that lets customers create, configure, deploy, and operate AI agents securely at scale, including agent orchestration, workflow execution, context management, memory, governance, and enterprise integrations.
• Partner with customers in semiconductor manufacturing, life sciences, and other data-intensive industries to build production-ready AI workflows, then turn those customer-specific solutions into reusable platform capabilities that benefit every customer.
• Design the systems that make enterprise AI trustworthy, including evaluation frameworks, verification pipelines, policy enforcement, audit trails, observability, provenance, and human-in-the-loop workflows.
• Build the data and knowledge capabilities that connect to enterprise data sources, discover schemas and semantic relationships, and give AI agents the context needed to reason accurately over complex business data.
• Develop the runtime that powers long-running AI workflows, managing execution state, retries, approvals, checkpoints, memory, and multi-agent coordination while ensuring reliability and transparency.
• Work directly with Product Management, Customer Success, and enterprise customers in workshops, technical discussions, and production deployments to rapidly understand problems and ship solutions.
• Own features from concept through production, making thoughtful engineering decisions around APIs, architecture, scalability, security, and performance.
• Raise the engineering bar by establishing reusable patterns and best practices, evaluating emerging AI tools for what meaningfully improves engineering productivity, and continuously simplifying how we build.

Requirements

• 3+ years of professional software engineering experience building production software.
• AI-native engineer who uses Claude Code, Cursor, Codex, or similar AI development tools as part of your daily engineering workflow, not just a tool you occasionally reach for.
• Strong software engineering fundamentals — production backend services, APIs, distributed systems, cloud applications, or developer platforms — and comfortable working across the stack, picking up new languages and frameworks quickly.
• Experience building and shipping software used by real customers in production environments.
• Strong problem-solving skills, sound engineering judgment, and excellent communication with customers, product managers, designers, and engineers.
• Self-driven, curious, and comfortable taking ownership in a fast-moving startup environment.

Skills

Bonus Points

• Experience building AI agents, agentic workflows, or enterprise AI applications.
• Experience with LLMs, tool calling, RAG, context engineering, memory systems, or multi-agent orchestration.
• Experience integrating enterprise data sources such as databases, data warehouses, SaaS applications, APIs, or document repositories.
• Familiarity with Rust, Go, TypeScript, Python, or other modern programming languages.
• Experience with cloud-native platforms, Kubernetes, observability, distributed tracing, or production operations.
• Demonstrated curiosity, rapid learning, and a passion for solving difficult engineering problems.