Applications Engineers, Agentic Workflows
Backend Engineer · Mid · Full Time · Remote
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Role
What you'll do.
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.
Responsibilities
- 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.
Qualifications
What we look for.
Technical
Python Proficiency
Strong 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 Experience
Hands-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 Automation
Demonstrated 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 Integration
Experience 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 Workflows
Familiarity 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 Field
Formal 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 Foundation
Minimum 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 Conversion
Prior 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 Knowledge
Background 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
Python
Production-level Python expertise for building automation systems, API clients, and data pipelines.
Agent Framework Architecture
Understanding of agent frameworks, including ReAct patterns, tool-use systems, agentic orchestration, and LLM integration patterns.
System Debugging and Troubleshooting
Strong capability to debug across multiple layers including agent logic, API interactions, data transformations, and distributed system components.
API Integration
Experience integrating with external systems and APIs, building API clients, and handling authentication, error handling, and data serialization.
Software Architecture Fundamentals
Understanding of system design principles, modularity, testability, and creating reusable components from monolithic scripts.
Preferred
LLM Prompt Engineering
Nice to haveExperience with prompt design, few-shot learning, and tuning LLM behavior for specific tasks and domains.
CAE and Simulation Tools
Nice to haveFamiliarity with specific CAE platforms such as ANSYS, ABAQUS, OpenFOAM, or similar engineering simulation software.
Container and Orchestration Technologies
Nice to haveKnowledge of Docker, Kubernetes, or similar containerization and orchestration platforms for deployment automation.
Data Engineering and ETL
Nice to haveExperience building data pipelines, ETL processes, or handling large-scale data transformation relevant to engineering datasets.
Machine Learning Operations
Nice to haveFamiliarity with MLOps practices, model deployment, monitoring, and operational considerations for AI systems in production.
Aerospace, Energy, or Manufacturing Domain Experience
Nice to haveProfessional experience in aerospace, energy, manufacturing, or life sciences sectors provides valuable domain context.
Tech stack
Languages
Frameworks
Databases
Tools
Other
Compensation
Pay and benefits.
Base·USD 156,000 – 180,000
Equity·Stock options
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.
Process
Interview steps.
- 01
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.
- 02
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.
- 03
Engineering Interview
Deep-dive technical conversation with engineering team members covering agent architecture, debugging strategies, and how you approach turning prototypes into production systems.
- 04
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.
- 05
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.
Full posting
Original listing.
About Rescale
Rescale is pioneering the future of engineering and scientific discovery. As the leader in digital engineering, we’re transforming how products are developed—through intelligent automation, applied AI, data management, and the integration of the world’s largest network of engineering and R&D applications. Joining Rescale means becoming part of a diverse, collaborative, and mission-driven team that’s unlocking faster innovation across industries like aerospace, energy, life sciences, and manufacturing. We’re solving complex challenges that traditional HPC can’t—and we’re seeking passionate, curious minds to help build the next wave of breakthroughs
We are seeking a highly motivated Application Engineer, Agentic Workflows to help build and deliver the next generation of AI Physics capabilities at Rescale!
You will work directly with platform and AI teams to design and ship agent-based capabilities that operate on real engineering tools and data. You will also work closely with customers to apply these agents to real workflows, ensuring what gets built is reliable, repeatable, and productizable.
The role is customer-facing, but product-first. Work done in customer environments must translate into reusable product capabilities rather than one-off solutions.
This role is ideal for engineers who enjoy building real systems, integrating with complex engineering software, and turning early agent prototypes into durable platform capabilities.
What You’ll Do
Build and ship agent-driven automation across engineering and CAE workflows
Design and implement agents that interact with engineering tools, simulation data, and workflows
Encode engineering workflows as structured, agent-executable steps with clear inputs, outputs, and guardrails
Integrate agents into end-to-end pipelines as reusable capabilities rather than one-off scripts
Harden early agent use cases into deterministic, production-ready systems
Debug and resolve issues across agent logic, tool execution, data handling, and orchestration
Work directly with customer engineers to understand real workflows and technical constraints
Close gaps between real workflows and platform capabilities, feeding concrete learnings back into product direction
Operate as a builder first, with the ability to be forward-deployed on critical engagements when needed
Core Skill Set
Strong software engineering fundamentals, with heavy emphasis on Python
Hands-on experience building automation around engineering or technical software
Familiarity with CAE workflows, simulation data, and solver-driven processes
Experience building or deploying agent-based or LLM-driven systems
Experience orchestrating tools, scripts, or workflows across multiple systems
Ability to design deterministic, reliable automation around complex tools
Experience turning internal automation into reusable, product-grade components
Comfortable debugging across tool execution, data handling, and orchestration layers
Customer Engagement & Travel
This role includes direct customer engagement during agent deployments and early workflow rollouts. Expect occasional travel to customer sites for deep technical working sessions or critical milestones. Travel is execution-focused and tied to building and deploying real capabilities.
Why This Role Matters
AI Agent Applications Engineers ensure that agent-based automation is not just experimental, but reliable, usable, and embedded into real engineering workflows.
Your work will directly influence how engineering organizations adopt agent-driven automation, and will shape the core capabilities that become part of the platform.
Rescale is an equal opportunities employer and welcomes applications from all qualified persons regardless of their race, sex, disability, religion/belief, sexual orientation or age. As part of our standard hiring process for new employees, employment with Rescale will be contingent upon successful completion of a comprehensive background check. Here at Rescale, we are committed to being transparent in our policies around candidate privacy. For more details on the information Rescale collects in your application, please view the Rescale Applicant Privacy Policy here.
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