Job description

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. This position will require at least 1 day per week in office in Boston 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, a minimum of 3 years in AI, 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 quality (RAG, vector indexes, reranking) were measured. Agent security: permissions, sandboxing, human approval gates, prompt injection defenses, and audit logging, tested against adversarial inputs. Tool and API design for models: tools or MCP servers built for model use, with tool-selection and call accuracy measured. Production engineering: Python with type checking, automated tests, and packaging in CI, plus Node.js/TypeScript and React services and interfaces, including streaming output, with monitoring, tracing, and managed configuration and secrets. You may also have: Experience with agent frameworks such as LangGraph, or building custom harnesses from scratch. Working knowledge of Databricks and BI tools (Sigma, Power BI, Spotfire). Observability for LLM systems (tracing, cost and latency monitoring). Fine-tuning, prompt optimization, or model evaluation experience. Authored skills, hooks, or MCP servers that made agent-driven delivery repeatable for a team. We are open to speaking to candidates with more or less experience than listed The compensation for different levels or experience can be discussed with your recruiter Total Rewards Dotmatics utilises a national market-based approach to base pay benchmarking and pay band development. The candidate's final starting pay is based on job-related skills, experience, job specific qualifications & location. In addition to base salary, Dotmatics has implemented a total rewards strategy, which is the combination of compensation, benefits and recognition. Certain positions are also eligible for variable pay; your recruiter will discuss the full compensation package details. Other Total Rewards Offered Medical, Dental, Vision, Insurance Health Spending Accounts Wellness Benefits (Mental Health Apps and Fitness Perks) Company-paid Life and Disability Insurance 401k Retirement Plan (with 4% company match; immediate vesting) Flexible Time off Plan (for US exempt employees) This is the range that we, in good faith, believe is the range of possible compensation for this role at the time of this posting. We may ultimately pay more or less than the posted range. This range may be modified in the future. Base Pay Range $160,000 — $256,400 USD Research shows us the confidence gap and imposter syndrome can get in the way of meeting outstanding candidates, so please don’t hesitate to apply — we’d love to hear from you. By submitting your application, you agree that Dotmatics may collect your personal data for recruiting, global organization planning, and related purposes. Dotmatics Privacy Notice explains what personal information we may process, where we may process your personal information, our purposes for processing your personal information, and the rights you can exercise over Dotmatics use of your personal information. Dotmatics is an equal opportunity employer. We are a welcoming place for everyone, and we do our best to make sure all people feel supported and connected at work.

Requirements

Must-haves

Our Why At Dotmatics
We have a vision for a new Lab of the Future that will change the future of scientific research.
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
We are Science Driven. We are Customer Centric. We are Better Together.
What do we need
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.
Adapt the harness as models improve, and remove scaffolding that is no longer needed.
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
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.
Agent security: permissions, sandboxing, human approval gates, prompt injection defenses, and audit logging, tested against adversarial inputs.
Tool and API design for models: tools or MCP servers built for model use, with tool-selection and call accuracy measured.
Experience with agent frameworks such as LangGraph, or building custom harnesses from scratch.
Working knowledge of Databricks and BI tools (Sigma, Power BI, Spotfire).
Observability for LLM systems (tracing, cost and latency monitoring).

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aici/cdpythonnode.jstypescriptreactllmapi

Platform: greenhouse
Effort: quick

Benefits

Benefits detected in the listing.

Dental

Vision

401k

Wellness