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.

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.
Show more Show less