Staff Engineer, AI/LLM Platform

Simulations-Plus,-Inc

United States

Remote

USD 100,000 - 120,000

Full time

41 hours ago
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Benefits offered by this job

Fully remote work
Flexible schedules
Generous vacation policy
Annual bonus
Health coverage

Job summary

Simulations Plus, a global leader in model-informed and AI-accelerated drug development, seeks a Staff Engineer, AI/LLM Platform, to design, build, and distribute agentic systems that surface our scientific engines to customers.

This remote role sits in the Product and Technology department, with a lean, high-leverage team and collaboration across Anthropic/OpenAI; it focuses on context, knowledge, and tool layers for agent workflows.

Qualifications

  • 5+ years of professional software engineering experience focusing on agents, LLM features, or MCP and tool servers.
  • Strong Node.js and TypeScript background with service-layer design experience.
  • Experience with agent orchestration, prompt design, and context-management patterns (RAG, vector search).
  • Hands-on with at least one major LLM provider SDK (Anthropic, OpenAI, or equivalent).

Responsibilities

  • Design, build, and distribute agentic systems that surface scientific capability to customers.
  • Develop agent loops, orchestration code, and control structures balancing autonomy, cost, and latency.
  • Stand up MCP servers and the context-management layer feeding agents with timely information.
  • Mentor engineers and contribute to architectural patterns and technical standards.

Skills

Node.js
TypeScript
Agent orchestration
LLM provider SDKs
Prompt design

Education

Bachelor's degree in CS/Engineering
Master’s degree preferred

Tools

LangGraph
Mastra
Pydantic AI
LlamaIndex

Job description

If you are unable to complete this application due to a disability, contact this employer to ask for an accommodation or an alternative application process.

Full Time Professional US 1 Attachments

9 days ago Requisition ID: 1091

Salary Range: $100,000.00 To $120,000.00 Annually

Simulations Plus stands as a global leader in model-informed and AI-accelerated drug development. We create value for our clients by accelerating the discovery, development, and commercialization of pharmaceuticals and other products through innovative science-based software and consulting solutions.

Leadership truly cares about maintaining a positive culture and employee well-being. We offer fully remote work, flexible schedules, and generous vacation policy along with affordable health coverage, annual bonus, and more! Check out how much our employees love working here: https://www.comparably.com/companies/simulations-plus .

The Staff Engineer, AI/LLM Platform will be the senior applied engineer on the AI side of a small department whose charter spans both web/SaaS and AI/agents products. The role will help design, build, and distribute the agentic, context, and tool layers that turn decades of scientific modeling and simulation capability into agent-driven product experiences — from MCP servers that expose our core scientific engines, through the context and knowledge systems that feed agents, up to the agent flows customers actually interact with. This is an applied engineering role, not a research one, on a deliberately lean, high-leverage team that's growing quickly and intentionally.

Department:Product and Technology

Internal Grade:11

Direct Reports:No

Status: Exempt

Location:Remote

Job Responsibilities:

  • Agent Systems and Product Surfaces
    • Help design, build, and distribute the agentic systems that surface our scientific capability to customers
    • Build agent loops, orchestration code, and control structures that balance autonomy, cost, and latency
    • Make day-to-day decisions on prompts, tools, structured outputs, and provider tradeoffs across Anthropic, OpenAI, and others
  • Context, Knowledge and MCP Layer
    • Build and operate MCP servers that expose our core scientific engines and product capabilities to agentic clients
    • Design and maintain the context-management layer that feeds agents the right information at the right time
    • Stand up vector stores, knowledge bases, and retrieval patterns that power both agent reasoning and customer-facing workflows
  • Quality & Team Impact
    • Build the evals, traces, and feedback loops needed to debug and improve a non-deterministic system
    • Contribute to the architectural patterns and technical standards the team builds on
    • Mentor engineers contributing to AI features and raise the floor on applied-AI practice across the team
  • Other Duties as Assigned

Required Qualifications:

  • 5+ years of professional software engineering experience, with recent production work on agents, LLM-integrated features, or MCP and tool servers
  • Strong Node.js and TypeScript background; comfort designing service-layer code and tool integrations
  • Working knowledge of agent orchestration, prompt and tool design, and context-management patterns (RAG, vector search, memory)
  • Hands-on experience with at least one major LLM provider SDK (Anthropic, OpenAI, or comparable)
  • Pragmatic, applied mindset — this is an engineering role, not a research or data-science role
  • Fluency using AI coding tools (Claude, Cursor, Copilot) in daily workflow, and comfort working across languages by leveraging them

Preferred Qualifications:

  • Python proficiency for prototyping, evals, or interfacing with ML tooling
  • Experience with agent frameworks such as LangGraph, Mastra, Pydantic AI, or LlamaIndex — with the judgment to know when not to use them
  • Familiarity with vector databases and RAG patterns (Pinecone, pgvector, MongoDB Atlas Vector Search, or comparable)
  • Experience in regulated-software environments (SOC 2, HIPAA, GxP, 21 CFR Part 11) or life-sciences SaaS
  • Open-source contributions to MCP servers, agent frameworks, or LLM tooling

Education:

  • Bachelor's degree in computer science, engineering, or related field preferred
  • Master’s degree in computer science, engineering or related field a plus
  • Relevant technical certifications welcomed
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