Because Change takes Energy.

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Reports To: Co-Founder, CTO

Employee Type: Full Time, Remote

Compensation: 0.25 - 1.0% Equity + $110-160k

About Morton Labs At Morton Labs, we are building the computational backbone for the fusion energy age. As the industry transitions from scientific curiosity to commercial deployment, it faces a massive software bottleneck: the critical physics codes needed to design these reactors are fragmented, scattered across National Labs, and written in legacy languages that do not natively communicate.

We act as the "Systems Integration Layer" for the industry, transforming world-class scientific codes into an industrial-grade operating system. Our mission is simple: "We don’t rewrite the physics; we build the plumbing". By providing standardized data schemas, workflow orchestration, and cross-environment deployment, we allow the world's leading physicists to focus on advancing fusion science rather than wrestling with software infrastructure.

The Role 

We are seeking a highly technical, customer-facing Solutions Architect to bridge the gap between our prospective clients (private fusion startups, National Laboratories, and universities) and our core engineering team.

You will lead the technical engagement process, translating the complex, disjointed workflows of computational physicists into reproducible, enterprise-grade architecture on the Morton Labs Simulation Platform. You will not be responsible for inventing new physics; instead, you will architect the "connective tissue" that allows disparate solvers to run seamlessly in automated, multi-physics workflows.

Key Responsibilities

• Lead Customer Discovery & Deep Dives: Conduct detailed technical audits of prospective clients' current simulation processes. You will inventory the codebases they use, review their meshes, solvers, and workflows, and identify their biggest gaps.

• Design Code Coupling Frameworks: Identify specific requirements to loosely or tightly couple distinct physics codes (e.g., passing thermal loads from OpenFOAM to mechanical models, or integrating OpenMC for neutronics) without requiring engineers to manually write interpolation scripts.

• Architect Infrastructure Strategies: Propose and design deployment architectures tailored to customer needs, whether that involves cloud compute (AWS, Azure, GCP), on-premises HPC clusters, or secure, air-gapped environments.

• Data Modeling & Standardization: Help define the data models and metadata pipelines required to solve the "Dark Data" problem, ensuring that chaotic log files are translated into structured, AI-ready schemas with automated provenance tracking.

• Deliver Implementation Roadmaps

• Navigate Legacy to Modern: Assist in bridging the gap between legacy Fortran/C++ codebases and modern, reproducible "press-go" workflows (utilizing containerization and the Spack package manager).

Qualifications

• Technical Foundation: Advanced degree (or equivalent experience) in Computational Physics, Nuclear Engineering, Computer Science, or a related high-consequence engineering discipline.

• Domain Familiarity: Experience working with complex, multi-physics simulation tools commonly used in the nuclear or aerospace sectors (e.g., OpenMC, OpenFOAM, MOOSE, MCNP, WarpX).

• Systems Architecture Expertise: Deep understanding of High-Performance Computing (HPC), cloud-native infrastructure, containerization (Docker/Singularity), and package management for complex dependencies.

• Customer-Facing Experience: Proven ability to interface with highly specialized stakeholders (e.g., PhD physicists, Lab Directors, CTOs), deeply understand their technical pain points, and map those to scalable software solutions.

• Problem-Solving Mindset: You excel in environments where the answer is not yet defined, finding ways to apply structured systems engineering to workflows that are currently reliant on "tribal knowledge" and manual bash scripts.

The Role

We need a Full-Stack Engineer who can own the data infrastructure side of our platform and contribute meaningfully to the product layer our customers see. That means building the pipelines, storage systems, and provenance tracking that every run produces data into, extending the backend API, and working alongside the rest of the engineering team. This is not a role with a narrow scope. The team is small and the product is early enough that what you build in the first six months will be in production for years.

We strongly believe in providing growth opportunities at all levels and this position has meaningful potential to grow to technical leadership over time.

What We Are Looking For

• 3 or more years of production software engineering experience working across backend systems and frontend product development.

• Real data engineering experience: you have designed schemas, built pipelines, and made deliberate decisions about how structured data is stored, versioned, and queried at scale.

• AWS experience in a production context.

• Some exposure to scientific computing, HPC environments, or simulation-adjacent tooling. You do not need to be a physicist, but you should be able to read a pipeline definition in the context of our primary industries and understand what is happening.

Requirements

• 3 or more years of relevant software engineering experience in production environments.

• Demonstrated experience with data pipelines, artifact or object storage, and structured data systems.

• Frontend development experience with React sufficient for production feature work.

• AWS experience in a production deployment context.

• A bachelor's degree in computer science, engineering, or a related field, or equivalent demonstrated depth through work history.

• Eligible to work in the United States without sponsorship.

Compensation and Logistics

• Base salary $120,000 to $150,000 depending on experience, plus equity.

• Full-time W2, remote anywhere in the United States.

• Occasional travel for team meetings or customer visits.