At Arrayo, we don’t just build software - we build intelligent systems that think, adapt, and accelerate real-world science and manufacturing. Our engineers are fluent in AI-augmented development and deploy production systems at a pace that would be impossible without it.
We’re hiring Forward Deployed Engineers across multiple levels who combine scientific domain expertise with modern AI-native software engineering. This is a high-autonomy, customer-embedded role for engineers who use AI as a core part of how they design, write, test, and ship code.
This track focuses on chemistry, materials science, and industrial manufacturing environments.
The Role
As a Forward Deployed Engineer at Arrayo, you will embed with scientific and industrial teams to understand how their labs and plants truly operate. You will architect and deploy AI-enabled systems that transform fragmented workflows into intelligent, automated processes.
AI fluency is not optional in this role. You will:
- Use AI tools to accelerate development, testing, debugging, and documentation
- Design agent-driven workflows that automate complex scientific and operational tasks
- Build systems where AI is embedded into the product itself - not layered on as an afterthought
- Rapidly prototype, validate, and productionize solutions in days or weeks
You are expected to operate as a force multiplier - leveraging AI to extend your individual engineering output and impact.
What You’ll Deliver
- AI-powered scientific workflows for materials analysis, formulation optimization, and process control
- Intelligent integrations between industrial hardware (PLCs, lab instrumentation, control systems) and enterprise platforms (MES, ERP, LIMS, SCADA)
- Real-time data pipelines that clean, contextualize, and structure experimental and manufacturing data
- Agentic systems that assist scientists and engineers in decision-making
- Production-grade full-stack applications that make advanced AI capabilities intuitive and accessible
- Deployments that scale from pilot environments to enterprise-wide adoption
Core Expectations
AI-Native Engineering
- Comfort using AI coding assistants and tooling as part of your daily development workflow
- Ability to design systems that incorporate LLMs, automation agents, and machine learning components
- Understanding of prompt engineering, evaluation, observability, and model limitations
- Pragmatic judgment about when to use AI - and when not to
Technical Execution
- Strong full-stack capabilities across backend systems, APIs, data engineering, and frontend development
- Experience building and maintaining reliable data pipelines for complex datasets
- Ability to write production-ready, maintainable code under tight iteration cycles
Domain Expertise
- Background in chemistry, materials science, chemical engineering, or related field
- Experience operating in industrial or manufacturing contexts
Communication & Ownership
- Comfortable interfacing with executives, scientists, and operators
- Clear communicator who can translate between technical and domain stakeholders
- Willingness to travel for on-site collaboration and deployment
Nice to Have
- Experience deploying AI/ML systems in production environments
- Background in controls engineering or industrial automation
- Experience with scientific computing ecosystems (Python, data analysis, modeling tools)
- Experience building modern web applications
What Makes Someone Exceptional Here
- AI-Leveraged – You treat AI as a core engineering primitive, not a novelty.
- Outcome-Focused – You prioritize deployed systems and measurable results.
- Systems-Oriented – You understand the interplay between hardware, software, and data.
- Comfortable in Ambiguity – You can define structure in chaotic environments.
- Relentlessly Practical – You optimize for what works in production, not what looks elegant in theory.