СШАInternal Tool Software Engineer
5 дн. назад
СШАOnsite
pythonaiautomation
Develop internal AI-powered tools and software to improve engineering, manufacturing, and operational workflows.
О компании
- is a hard-tech company developing innovative systems to reshape the physical world. We combine mechanical engineering, robotics, automation, and real-world field operations to tackle complex engineering challenges.
- We're looking for an AI Tool Engineer to help us build smarter, more efficient ways of working across our engineering, manufacturing, and field operations.
- At , you'll have the opportunity to apply AI to real-world engineering problems rather than building software in isolation. Your work will directly influence how physical systems are designed, manufactured, tested, and operated.
- You'll have significant ownership over projects, the freedom to experiment with new technologies, and the ability to see your solutions implemented in the shop and in the field.
- We're looking for a builder who enjoys moving quickly, solving ambiguous problems, and using AI and software to make complex engineering operations more efficient.
Обязанности
- As an AI Tool Engineer at , you'll develop practical AI-powered tools, software, and analytical models that improve how our team designs, builds, tests, and operates complex machinery.
- This is a highly hands-on role focused on solving real engineering and operational problems. You'll work closely with mechanical, electrical, software, and field engineers to identify inefficiencies, automate repetitive workflows, and turn operational data into actionable insights.
- We're looking for someone who can independently identify opportunities, rapidly prototype solutions, and deploy tools that make a measurable difference in how the company operates.
Требования
- Strong proficiency in Python and experience building software tools, automation scripts, and data-processing pipelines.
- Experience working with LLM APIs, AI agents, machine learning frameworks, or applied AI systems.
- Ability to build end-to-end applications, from data ingestion and backend logic to simple user interfaces.
- Strong data analysis skills, including SQL, Pandas, and statistical modeling.
- Experience integrating APIs, databases, and third-party software platforms.
- Comfortable working with messy, incomplete, or unstructured real-world data.
- Strong problem-solving skills and the ability to independently take loosely defined problems and turn them into working solutions.
- Interest in physical engineering systems, manufacturing, robotics, or industrial operations.
- Ability to work closely with multidisciplinary teams and communicate technical concepts clearly.
Будет плюсом
- Experience with industrial automation, PLCs, sensors, or equipment telemetry.
- Familiarity with mechanical engineering, fluid systems, hydraulics, or manufacturing processes.
- Experience with time-series data, predictive modeling, or optimization algorithms.
- Experience developing computer vision applications for industrial monitoring or quality control.
- Familiarity with CAD software, engineering simulations, or engineering data pipelines.
- Experience deploying internal tools using modern web frameworks and cloud services.
Другое
- Design and build internal AI-powered tools that streamline engineering, manufacturing, and operational workflows.
- Identify repetitive, manual, or cumbersome processes and develop software solutions to automate them.
- Integrate AI tools with existing engineering software, databases, documentation, and operational systems.
- Build internal applications that help engineers and operators access information, make decisions, and complete tasks more efficiently.
- Develop systems to collect, track, and analyze metrics across shop operations, equipment performance, and field activities.
- Analyze field injection data, including flow rates, pressures, material consumption, ground uplift, and operational efficiency.
- Build dashboards, reporting tools, and data pipelines to visualize performance and identify opportunities for improvement.
- Help quantify engineering and manufacturing processes to establish benchmarks, monitor performance, and reduce inefficiencies.
- Develop models to predict operational outcomes, improve process parameters, and support engineering decisions.
- Build and deploy machine learning, statistical, and predictive models for engineering applications.
- Use LLMs, AI agents, and other emerging technologies to automate complex workflows and improve internal productivity.
- Develop tools for analyzing experimental results, identifying trends, and optimizing system performance.
- Explore new applications of AI across robotics, manufacturing, equipment monitoring, and field operations.
- Work directly with engineers, technicians, and operators to understand challenges and develop practical solutions.
- Take projects from problem identification through prototyping, implementation, and iteration.
- Build lightweight, maintainable tools that can be deployed quickly and improved based on real-world feedback.
- Proactively identify opportunities where software, data, or AI can improve the company's efficiency and capabilities.