Software Engineer, Agentic AI

Responsibilities

Build and own the production AI agent for the Luma platform, including its reasoning loop, tools, memory, security controls, recovery, and evaluation. Develop the surrounding services and AI-first engineering workflow, and improve agent reliability, performance, and safe delivery in collaboration with product teams and scientists.

Requirements

The role seeks an experienced software engineer, ideally with around 10 years of experience and hands-on experience shipping production LLM agents that handle multi-step tool use, recovery, and long-running tasks. Candidates should have strong Python skills and experience with TypeScript/Node.js, React, context and retrieval engineering, agent security, testing, and production monitoring; a technical degree and life-sciences interest are desirable.

Skills

• AI-Native Engineering
• Autonomous Coding Agents
• Production LLM Agents
• Agent Harness Design
• Context Engineering
• Memory and Retrieval
• RAG
• Agent Security
• Prompt Injection Defense
• Tool and API Design
• Python
• Node.js
• TypeScript
• React
• Automated Testing
• LLM Observability

Benefits

• Medical Insurance
• Dental Insurance
• Vision Insurance
• Health Spending Accounts
• Wellness Benefits
• Mental Health Apps
• Fitness Perks
• Company-Paid Life Insurance
• Company-Paid Disability Insurance
• 401(k) Retirement Plan With 4% Company Match
• Flexible Time Off

About this role

Our Why At Dotmatics
At Dotmatics, a Siemens company, we believe science, data, and decision-making must be deeply intertwined for innovation to thrive. Our Portfolio includes Luma, LumaLab Connect, ELN Platform, Graphpad Prism, Geneious, SnapGene, Protein Metrics, OMIQ, FCS Express, LabArchives, NQuery, EasyPanel, SoftGenetics and Virscidian.
We have a vision for a new Lab of the Future that will change the future of scientific research.
We have created the world’s most comprehensive digital science platform – best-of-breed software applications already used by more than 2 million scientists, together in a single ecosystem united by a powerful, flexible enterprise data platform. This is not flat data buried away in digital graveyards. This is dynamic, multi-dimensional decision-making.
Scientific enterprises need a new level of effectiveness to achieve tomorrow’s breakthroughs. Illness will not wait. The biosphere will not wait. We are tireless in our vision, because the time for innovation is now.
Shaping the Future of Science At Dotmatics
Our global team of more than 800 colleagues are dedicated to supporting our customers in over 180 countries. Together, with our scientific community of users, we accelerate scientific innovation in order to make the world a healthier, cleaner, and safer place to live.
You’ll join a collaborative, global team pushing the boundaries of scientific innovation. Your ideas and efforts will have a tangible impact, accelerating scientific progress and discovery. We offer a dynamic, remote-friendly environment that fosters high integrity and collaboration, empowering you to excel. Dotmatics is a company built by scientists, for scientists. Combined, we are now the world’s largest cloud-based scientific research R&D platform. We need your help to keep growing and pioneering the future.
We are Science Driven. We are Customer Centric. We are Better Together.
What do we need
We are looking for an experienced and highly skilled Software Engineer (Agentic AI), to build the AI agent at the core of Dotmatics Luma platform. You will own the agent end to end: how it reasons, which tools it uses, what it remembers, how it recovers from failure, and how we know it is working. You will work in a small, high-trust engineering team alongside Product, scientists, and the wider Dotmatics organisation.
This is also an AI-native engineering role. Your success won’t be measured by how many lines of code you write. It will come from how well you decompose work for coding agents, give them the right context, tools, and constraints, verify their output with tests and review, and ship production software faster than a traditional senior engineer without lowering the quality bar. Coding agents are your default path for implementation, and you stay accountable for architecture, correctness, security, and review.
We want someone who thinks like the engineers who build frontier AI products: rigorous about quality, curious about how agents fail, and quick to adapt as models improve. You use AI tools daily to build AI tools, and you ship agents that work in production, not just in demos.
In this role you will get to
Develop the agent itself: the loop, harness, and control flow connecting the model, tools, and state, including subagents, long-running tasks, checkpointing, and recovery.
Practice context engineering: decide what goes into the context window, when to compact or summarize, and how to keep the agent coherent over long-running experiments.
Design tools for the agent, with clear interfaces and descriptions, covering instrument orchestration, scheduling, data capture, and developer tooling such as GitHub, repositories, CI/CD, and workflows.
Give the agent memory and state persistence so it learns from and builds on earlier work.
Implement permissions and sandboxing that define what the agent can do on its own, what needs human approval, and how mistakes are contained.
Defend the agent against prompt injection and untrusted content, with auditability and data isolation suited to regulated environments.
Evaluate the agent end-to-end on real multi-step tasks, then debug its behavior by reading traces and finding root causes, fixing the harness, tools, or prompts to improve reliability, tool-use accuracy, cost, and latency.
Adapt the harness as models improve, and remove scaffolding that is no longer needed.
Run an AI-first development loop (scope, plan, delegate to coding agents, verify, ship), preferring agent execution over hand-writing whenever quality holds, and build and maintain the team's agent harness for development itself (project memory, skills, hooks, MCP, CI checks) so agents can safely do bounded engineering work.
Measure and improve that workflow through throughput, defect rate, and review cost, and coach others on AI-native practices to raise the team's bar for agent-driven delivery
Set up the architecture and infrastructure to run the agent reliably, and manage configuration, environments, and secrets
Deliver the services and interfaces around the agent in Python, Node.js/TypeScript, and React, including streaming output and extension points such as MCP
We are looking for people who have around 10 years of experience in software engineering, including hands-on experience building LLM-powered agents in production. Ideally you will have a degree or higher education in Computer Science, Engineering, or a related technical field, and an interest in life sciences, lab instrumentation, or automation.
The Key Skills we are looking for:
AI-native engineering: autonomous coding agents (Cursor, Claude Code, Codex, or equivalent) as the primary way you build.
Production agents: at least one LLM-powered agent shipped to real users, handling multi-step tool use, error recovery, and long-running tasks.
Context engineering, memory, and retrieval: a long-running LLM application where you can explain what went into the window, when it was compacted, and how coherence and retrieval...

About Dotmatics

Software Development · 501-1,000 employees · headquartered in Boston, Massachusetts