Staff Software Engineer – AI Agent Platform, Runtime

Datasite

. 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 .

Posted 10/27/2026full-timeUnited StatesLead💰 $170,000 - $297,200 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in backend platforms, distributed systems, and asynchronous execution while leading complex technical initiatives. Proficient in Python and modern programming languages, with a strong focus on architecting secure, scalable agent systems and improving task quality and reliability.

Highest-signal resume keywords

Backend Platforms ExpertisePython ProficiencyDistributed Systems KnowledgeAgent Orchestration ExperienceCloud-Native Infrastructure Understanding

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

PythonTypeScriptJavaGoKotlinRustAsynchronous ExecutionData PersistenceEvent-Driven ArchitecturesFault Tolerance

Soft Skills

MentoringTechnical Strategy InfluenceClear Communication

Tools & Technologies

DockerKubernetesMCPMicroVMsSecure Code Execution

Industry Keywords

Enterprise SaaSFinancial ServicesRegulated DataComplex Document-Centric WorkflowsMulti-Tenancy

Tech Stack

Tools & technologies

CloudDistributed SystemsDockerJavaKotlinKubernetesPythonRustTypeScriptGo

About the role

Key responsibilities & impact

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
Implement 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
Establish agent evaluation, tracing, observability, and regression testing
Drive measurable improvements in task quality, correctness, reliability, latency, and cost
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

Requirements

What you’ll need

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
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
Ability to influence technical strategy, mentor senior engineers, and communicate complex tradeoffs clearly to engineering and product leaders
Nice to have: custom agent runtimes or orchestration frameworks; agentic coding systems or autonomous development environments
Nice to have: persistent or hierarchical agent memory, context engineering, retrieval architectures, or long-running agent workflows
Nice to have: secure code execution, sandboxing, Docker, Kubernetes, microVMs, or ephemeral compute
Nice to have: multi-agent coordination, MCP servers, tool registries, capability-based permissions, model routing, or agent evaluation and tracing
Nice to have: enterprise SaaS, M&A, virtual data rooms, regulated data, financial services, or complex document-centric workflows

Benefits

Comp & perks

Health insurance (medical, dental, vision)
Retirement savings plan
Paid time off
Other employee benefits
Potential eligibility for bonuses, commissions, or overtime if applicable