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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 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 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.
Salary Context
This $160K-$256K range is above the median for AI Software Engineer roles in our dataset (median: $194K across 269 roles with salary data).
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 11,520 AI roles we're tracking, AI Software Engineer positions make up 5% of the market. At Dotmatics Ltd., this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $220,000 based on 1,688 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($208K) sits 5% below the category median. Disclosed range: $160K to $256K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Dotmatics Ltd. AI Hiring
Dotmatics Ltd. has 1 open AI role right now. They're hiring across AI Software Engineer. Based in Boston, MA, US. Compensation range: $256K - $256K.
Location Context
AI roles in Boston pay a median of $215,000 across 386 tracked positions.
Career Path
Common paths into AI Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
AI Hiring Overview
The AI job market has 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 roles).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
The AI Job Market Today
The AI job market spans 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (140) are outnumbered by mid-level (5,630) and senior (4,176) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $266,050 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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