Rescale

Applications Engineers, Agentic Workflows

Rescale5 days ago
Location

Remote (United States)

Workplace

Remote

Type

Full Time

Salary

USD 156,000 – 180,000

Level

Mid

Role

Backend Engineer

Posted

May 11, 2026

Full TimeRemoteMid

The role

Summary

Rescale seeks an Application Engineer specializing in Agentic Workflows to build and deploy AI-driven automation across engineering and CAE workflows. This customer-facing, product-first role combines deep software engineering expertise with hands-on experience integrating intelligent agents into complex engineering tools, simulation data pipelines, and end-to-end orchestration systems. You'll transform early-stage agent prototypes into production-grade, reusable platform capabilities while working directly with engineering customers to encode real workflows into deterministic, hardened automation systems.

What you'll do

Design and Deploy Agent-Driven Automation: Build and ship agent-based capabilities that operate across engineering and CAE workflows, designing agents that interact intelligently with engineering tools, simulation data, and complex processes while ensuring reliability and repeatability in production environments.
Architect Workflow Automation Systems: Encode engineering workflows as structured, agent-executable steps with clearly defined inputs, outputs, and guardrails. Integrate agents into end-to-end pipelines as reusable, production-grade components rather than one-off scripts or point solutions.
Harden and Productionize Agent Use Cases: Transform early-stage agent prototypes into deterministic, production-ready systems through rigorous testing, debugging, and optimization across agent logic, tool execution, data handling, and orchestration layers.
Direct Customer Engagement and Field Deployment: Work directly with customer engineers to understand real workflows, technical constraints, and operational requirements. Participate in on-site technical working sessions and critical deployment milestones, with occasional travel for execution-focused engagements.
Debug Complex Multi-Layer Systems: Identify and resolve issues spanning agent decision logic, tool execution, data pipelines, and orchestration frameworks. Develop diagnostic expertise across the entire automation stack to ensure system reliability and performance.
Translate Customer Learnings into Product Direction: Close gaps between real-world workflows and platform capabilities by gathering concrete customer feedback and technical insights. Feed operational learnings back into product teams to inform platform evolution and feature prioritization.

What we look for

Technical

Python ProficiencyStrong expertise in Python with ability to write robust, maintainable code for automation systems, API integration, and data processing pipelines. Must be comfortable with Python's ecosystem for building production systems.
Agent-Based and LLM Systems ExperienceHands-on experience building, deploying, or operating agent-based systems or LLM-driven applications. Understanding of agent frameworks, reasoning loops, tool use patterns, and agentic workflow orchestration is essential.
Engineering Software AutomationDemonstrated experience building automation around engineering or technical software, including CAE tools, simulation platforms, and solver-driven processes. Knowledge of engineering workflows and technical computing pipelines is critical.
Workflow Orchestration and Tool IntegrationExperience orchestrating complex tools, scripts, and workflows across multiple systems. Proficiency in designing deterministic, reliable automation that handles tool execution, data transformation, and inter-system communication.
Data Handling and Simulation WorkflowsFamiliarity with CAE workflows, simulation data structures, solver-driven processes, and computational data pipelines. Understanding of how simulation data flows through engineering tools and systems.

Education

Bachelor's Degree in Computer Science, Software Engineering, or Related FieldFormal education in computer science, software engineering, physics, mechanical engineering, or related quantitative field. Equivalent professional experience demonstrating strong engineering fundamentals is acceptable.

Experience

Software Engineering FoundationMinimum 3-5 years of professional software engineering experience with strong fundamentals in system design, debugging, and production software development. Track record of shipping reliable, scalable systems.
Internal Tool to Product ConversionPrior experience converting internal automation or one-off scripts into reusable, product-grade components. Understanding of the transition from prototype to production and the considerations involved.
Engineering Domain KnowledgeBackground working with engineering teams, technical computing, or scientific software. Direct experience in aerospace, energy, manufacturing, or life sciences domains is highly valuable but not required.

Skills

Required skills

PythonProduction-level Python expertise for building automation systems, API clients, and data pipelines.
Agent Framework ArchitectureUnderstanding of agent frameworks, including ReAct patterns, tool-use systems, agentic orchestration, and LLM integration patterns.
System Debugging and TroubleshootingStrong capability to debug across multiple layers including agent logic, API interactions, data transformations, and distributed system components.
API IntegrationExperience integrating with external systems and APIs, building API clients, and handling authentication, error handling, and data serialization.
Software Architecture FundamentalsUnderstanding of system design principles, modularity, testability, and creating reusable components from monolithic scripts.

Nice to have

LLM Prompt EngineeringExperience with prompt design, few-shot learning, and tuning LLM behavior for specific tasks and domains.
CAE and Simulation ToolsFamiliarity with specific CAE platforms such as ANSYS, ABAQUS, OpenFOAM, or similar engineering simulation software.
Container and Orchestration TechnologiesKnowledge of Docker, Kubernetes, or similar containerization and orchestration platforms for deployment automation.
Data Engineering and ETLExperience building data pipelines, ETL processes, or handling large-scale data transformation relevant to engineering datasets.
Machine Learning OperationsFamiliarity with MLOps practices, model deployment, monitoring, and operational considerations for AI systems in production.
Aerospace, Energy, or Manufacturing Domain ExperienceProfessional experience in aerospace, energy, manufacturing, or life sciences sectors provides valuable domain context.

Compensation & benefits

Salary

USD 156,000 – 180,000 (annual)

Stock options

Available

Benefits

Comprehensive Health Insurance

Medical, dental, and vision coverage for employees and eligible dependents, with competitive company contribution rates.

Equity and Stock Options

Participate in company growth through stock option grants aligned with your role level and tenure.

Professional Development

Learning and development budget for courses, conferences, certifications, and professional growth in AI, engineering, and software systems.

Flexible Work Environment

Hybrid or remote-friendly work arrangements with flexibility to support both focused deep work and collaborative engineering.

Unlimited PTO

Flexible time-off policy allowing you to manage work-life balance and take time for rest and personal development.

Collaborative Mission-Driven Culture

Work with passionate engineers and researchers focused on transforming digital engineering and scientific discovery across industries.

Impact at Scale

Direct influence on how engineering organizations adopt agent-driven automation and shape core platform capabilities used across aerospace, energy, life sciences, and manufacturing.

Interview process

  1. 1
    Initial Screening Call — 30-45 minute conversation with technical recruiter to discuss your background, interest in agent-based systems, and experience with engineering software automation.
  2. 2
    Technical Assessment — Take-home or live coding exercise focusing on Python proficiency, system design thinking, and practical problem-solving around workflow automation or agent integration challenges.
  3. 3
    Engineering Interview — Deep-dive technical conversation with engineering team members covering agent architecture, debugging strategies, and how you approach turning prototypes into production systems.
  4. 4
    Product and Customer Context Interview — Discussion with product or customer-facing team to assess your ability to translate customer needs into technical solutions and provide feedback on platform direction.
  5. 5
    Leadership and Culture Fit — Final conversation with engineering leadership to evaluate collaboration style, ability to operate autonomously on field deployments, and alignment with Rescale's mission.
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Rescale

Rescale

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Rescale is a cloud computing platform that provides high-performance computing (HPC) and simulation software solutions for engineering and scientific organizations. The company offers a managed cloud infrastructure and software suite that enables researchers and engineers to run complex computational simulations, including finite element analysis, computational fluid dynamics, and other engineering workloads. Rescale serves industries such as automotive, aerospace, energy, and manufacturing by reducing the complexity and cost of accessing powerful computing resources traditionally required for advanced simulations.

San Francisco, USAFounded 2011rescale.com

Tech Stack

Languages
PythonJavaScript/TypeScript
Frameworks
LangChain or LlamaIndexFastAPIPydantic
Databases
PostgreSQLVector Databases (e.g., Pinecone, Weaviate)
Tools
Git and Version ControlDockerKubernetesJupyter Notebooks
Other
OpenAI API or Similar LLM ProvidersRESTful APIs and gRPCHPC and Cloud Computing PlatformsCI/CD Pipelines
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