Staff Software Engineer, AI Agent Platform & Runtime

Datasite • New York City, NY

1d • Hybrid

About The Position

Datasite is hiring a hands-on Staff Software Engineer to shape the foundational infrastructure for enterprise AI agents. The role will evolve Datasite’s custom agent harness, build layered memory and secure isolated execution, and make sophisticated agent workflows dependable at production scale. It focuses on distributed systems, applied AI, security, and platform engineering, with responsibility for defining how agents reason, plan, remember, use tools, delegate work, and recover from failures across complex business workflows.

The engineer will own architecture decisions and collaborate with Engineering, Product, Machine Learning, Security, and platform teams to deliver production-grade agent experiences for diligence, research, and deal workflows. The role also involves mentoring engineers while remaining hands-on in critical implementation.

Datasite is hiring in New York City and Minneapolis. The company uses a hybrid work model, with in-office collaboration on Mondays, Tuesdays, and Thursdays.

Requirements

• Experience building and operating production software, with a track record of leading complex technical initiatives across teams.
• Deep expertise in backend platforms, distributed systems, asynchronous execution, APIs, fault tolerance, and stateful services.
• Strong hands-on Python proficiency and experience with a modern language such as TypeScript, Java, Go, Kotlin, or Rust.
• Experience developing production LLM applications involving tool calling, multi-step workflows, retrieval, agent orchestration, or context management.
• Practical understanding of containers, cloud-native infrastructure, workload isolation, and secure execution patterns.
• Strong foundations in data persistence, caching, event-driven architectures, permissions, multi-tenancy, and secrets management.
• Excellent product and architectural judgment, including the ability to evaluate emerging techniques, balance speed with reliability, and turn ambiguous problems into maintainable platforms.
• Ability to influence technical strategy, mentor senior engineers, and communicate complex tradeoffs clearly to engineering and product leaders.

Nice To Haves

• Custom agent runtimes or orchestration frameworks; agentic coding systems or autonomous development environments.
• Persistent or hierarchical agent memory, context engineering, retrieval architectures, or long-running agent workflows.
• Secure code execution, sandboxing, Docker, Kubernetes, microVMs, or ephemeral compute.
• Multi-agent coordination, MCP servers, tool registries, capability-based permissions, model routing, or agent evaluation and tracing.
• Enterprise SaaS, M&A, virtual data rooms, regulated data, financial services, or complex document-centric workflows.

Responsibilities

• Lead the architecture and evolution of Datasite’s custom agent harness, including reasoning and execution loops, planning, tool selection, state transitions, structured outputs, and runtime controls.
• Design layered agent memory spanning working context, episodic history, semantic knowledge, and durable workflow state; build retrieval, consolidation, compaction, provenance, retention, and access controls.
• Architect isolated, containerized agent execution environments for code, files, and tools, including ephemeral workloads, tenant boundaries, resource quotas, restricted networking, and secure credential handling.
• Build resilient agent orchestration with asynchronous tasks, specialized subagents, delegation, checkpoints, resumability, retries, failure recovery, and human approvals.
• Create secure, extensible tool integrations using MCP and related interfaces, with authentication, authorization, least-privilege permissions, and safeguards against prompt injection and unsafe actions.
• Establish agent evaluation, tracing, observability, and regression testing to measure task quality, correctness, reliability, latency, and cost, and drive measurable improvements.
• Make high-impact technical tradeoffs between probabilistic model behavior and deterministic application logic, validation, or human review.
• Partner with Product, Machine Learning, Security, and platform teams to turn diligence, research, and deal workflows into production-grade agent experiences.
• Mentor engineers while remaining hands-on in critical implementation.

Benefits

• Health insurance, including medical, dental, and vision coverage.
• Retirement savings plan.
• Paid time off.
• Other employee benefits, with details provided during the interview process.
• The position may be eligible for bonuses, commissions, or overtime if applicable.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed