Sr. Software Engineer

6sense • San Francisco, CA

7d • $179,400 - $248,120 • Hybrid

About The Position

6sense is hiring a Senior AI Engineer, Agentic Platform to advance its production agents and MCP server. The role builds more capable agents, strengthens APIs and tools, and expands an ecosystem that external agents, clients, and partners can connect to. The work spans the path from a customer's agent to 6sense data and actions, including APIs, tools, MCP, orchestration, context, evaluation, and security.

The engineer will partner with the Contextual Intelligence team to make contextual intelligence available to customers, partners, and AI tools. The position involves daily production Python development, ownership of multi-month projects, support for internal teams and customers, and keeping the platform current as agent tooling evolves. This is a hands-on engineering role working on systems already in production, not an architect-only or greenfield role.

The position is hybrid in the San Francisco Bay Area or Austin, Texas. The anticipated base salary range is $179,400 to $248,120. Other compensation may include a bonus program or commission plan and stock options if approved. Benefits include health coverage, life and disability insurance, a 401(k) employer match, paid holidays, self-care days, paid time off, paid parental leave, and learning and wellness initiatives.

Requirements

• At least 5 years of experience building and operating production software systems, with strong distributed systems fundamentals including idempotency, fault tolerance, backpressure, and failure recovery.
• At least 2 years of experience building production LLM-powered systems, including customer-facing agents.
• Strong Python development skills and production experience with LangGraph or a comparable orchestration framework.
• Experience designing APIs and tool interfaces for LLM consumers, including schemas, descriptions, error handling, and tool-selection optimization.
• Experience scaling customer- or partner-facing platforms, including authentication, rate limiting, versioning, and backward compatibility.
• Hands-on experience with retrieval, grounding, and LLM evaluation frameworks.
• Experience building multi-tenant, security-sensitive cloud systems, including authorization boundaries and data leakage prevention; AWS is preferred.
• Proven ability to debug production issues across distributed services and support customers directly or through support and solutions teams.
• Demonstrated success leading multi-month, cross-functional initiatives with minimal oversight and communicating effectively with technical and non-technical audiences.
• Bachelor's degree in Computer Science, Engineering, or equivalent practical experience.

Nice To Haves

• Direct MCP experience is strongly preferred.
• Experience building or operating MCP servers in production or contributing to MCP open-source projects.
• Experience serving context to agents from signals, entity data, knowledge graphs, or enterprise data platforms.
• Experience integrating with external agent platforms, AI assistants, or agent interoperability protocols.
• Familiarity with B2B go-to-market platforms, RevTech, CRM systems, or intent-data platforms.
• Experience with model routing, caching, cost optimization, and latency optimization.
• Experience with Kafka, Snowflake, Databricks, Spark, or similar data platforms.

Responsibilities

• Extend LangGraph orchestrators with typed state, conditional routing, subgraphs, checkpointing, durable execution, and human-in-the-loop workflows.
• Select and implement agent patterns such as routing, orchestrator-worker, plan-and-execute, reflection, and multi-agent handoffs to meet business needs.
• Run agents headlessly through API-first and MCP-first architectures across in-app, chat, workflow, and partner surfaces using a shared core.
• Build and maintain reusable components for state management, memory, tool calling, and agent-to-agent delegation with scoped permissions.
• Evolve APIs and tools that agents use to access 6sense data and take actions for both human and LLM consumers.
• Optimize tool design, including naming, descriptions, schemas, response formats, error handling, and token efficiency.
• Redesign and scale the MCP layer for production workloads, including concurrency, per-tenant quotas, latency budgets, caching, graceful degradation, backward compatibility, and emerging MCP capabilities.
• Onboard external MCP clients, AI assistants, agent platforms, and partners; create integration guides and reference clients; and define trust boundaries for third-party MCP server integrations.
• Strengthen platform reliability through idempotency, retries, rate limits, permissions, spend controls, and standards for tool contracts, versioning, and discovery.
• Partner with the Contextual Intelligence team to expose context through MCP, APIs, and tools across customer touchpoints.
• Define and enforce contracts for returned data, freshness, provenance, and tenant- and user-level access controls.
• Improve permission-aware retrieval and grounding through chunking, hybrid search, reranking, citations, context assembly, and memory management within token and latency constraints.
• Expand evaluation frameworks using golden datasets, tool-selection and tool-call accuracy, end-to-end task success metrics, regression testing, release gates, and context quality measurements.
• Enhance tracing and usage analytics and report on task success rates, tool-call precision, groundedness, latency, and cost efficiency.
• Make security a first-class consideration across tools, APIs, agents, and MCP changes, and review work for vulnerabilities and security risks.
• Enforce tenant isolation and user-level authorization while maintaining short-lived, scoped credentials.
• Protect against prompt injection, tool poisoning, excessive agent autonomy, and data leakage through outputs, traces, and logs.
• Establish runtime guardrails around steps, tokens, execution time, and spend.
• Investigate and resolve customer-reported issues across agents, tools, models, and downstream systems.
• Participate in customer and partner technical discussions and support enablement teams with guidance, demonstrations, and documentation.
• Write design documents, evaluate trade-offs, conduct code reviews, mentor engineers, and participate in on-call and incident response rotations.
• Stay current with MCP, agent frameworks, and model capabilities; prototype promising technologies and share evidence-based recommendations.

Benefits

• Health coverage and generous health insurance coverage
• Life insurance
• Disability insurance
• 401(k) employer matching
• Paid holidays
• Self-care days and quarterly self-care days off
• Paid time off
• Paid parental leave
• Stock options
• Learning and development initiatives, including access to LinkedIn Learning
• Quarterly wellness education sessions and wellness days
• Equipment and support for working and connecting with teams at home or in an office

What This Job Offers

• Job Type: Full-time
• Career Level: Senior