Actively AI is looking to solve the problem of increasing productivity per rep for Enterprise GTM organizations by building a superintelligent machine that powers the day-to-day for outbound teams, generating hundreds of millions of dollars in revenue for customers.
Requirements
- Experienced systems builder. You’ve designed and operated large-scale, event-driven backend systems — modeling complex, long-lived workflows into clear, maintainable, and reliable software.
- Strong backend engineer. Deep Python expertise and solid fundamentals in concurrency, transactions, and performance tuning.
- Architectural thinker. You reason about trade-offs between consistency, latency, fault-tolerance, and cost — building for correctness and adaptability.
- Fluent in modern infrastructure. Familiar with streaming and workflow technologies (Kafka, Pub/Sub, Temporal-like), and data stores across relational, in-memory, and vector paradigms.
- Built or contributed to LLM-powered or agent-driven systems — such as real-time inference pipelines, context and retrieval systems, or human-in-the-loop orchestration.
- Experience scaling mission-critical systems with high reliability requirements (payments, messaging, logistics, orchestration).
Responsibilities
- Design and build the agents that power Actively’s platform. Model complex customer and business workflows into clear, reliable agent logic that reasons over streaming data, maintaining state, decides what to do next, and acts consistently across systems — continuously and at massive scale.
- Develop orchestration and workflow primitives for agent and human collaboration. Translate nuanced, long-running real-world scenarios — multi-step workflows, customer-specific rules, and human-in-the-loop actions — into well-structured agent behaviors that are predictable, maintainable, and easy to extend.
- Ensure reliability through good modeling. Design agents with clear state transitions, strong data contracts, and explicit guarantees — embedding observability and fault tolerance into their design so they behave correctly over millions of events and long-lived sessions.
- Continuously evolve agent design. Simplify complex behavior through better abstractions, documentation, and testing — ensuring agents remain reliable and comprehensible as capabilities, data volume, and concurrency grow.
Other
- Bachelor's, Master's, or Ph.D. degree in Computer Science or related field.
- Travel requirements: not specified
- Visa requirements: not specified
- Collaborative and pragmatic. You value iteration, clear communication, and helping others move faster without sacrificing quality.
- Curious about AI systems. You’re excited by how LLMs, retrieval systems, and agents can transform enterprise software — and you want to build the infrastructure that makes it real.
