Forward Deployed Engineer

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.

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 product ionize solutions in days or weeks

You are expected to operate as a force multiplier - leveraging AI to extend your individual engineering output and impact.

• 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

• 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

• 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

• Background in chemistry, materials science, chemical engineering, or related field
• Experience operating in industrial or manufacturing contexts

• 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

• 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

• 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.