Design, build, and run applied research experiments focused on manufacturing
processes, systems, and technologies.
Develop experimental workflows connecting design, engineering, simulation,
manufacturing, inspection, sensing, automation, and production data.
Apply AI and data-driven methods to manufacturing problems such as process
planning, parameter optimization, anomaly detection, quality prediction, simulation
feedback, workflow automation, and decision support.
Collect, structure, analyze, and interpret data from manufacturing processes,
machines, sensors, simulations, inspections, and experiments.
Compare manufacturing methods and evaluate their process capabilities, limitations,
material considerations, quality requirements, and production trade-offs.
Build research prototypes that integrate software, hardware, data, and physical
manufacturing processes.
Plan and conduct experiments using appropriate research methods, controls,
measurements, and evaluation criteria.
Evaluate emerging technologies, including AI, digital twins, simulation, machine
perception, robotics, sensing, and industrial automation, within manufacturing
contexts.
Work with Technology Center specialists to conduct experiments involving processes
such as machining, metal fabrication, joining, molding, forming, additive
manufacturing, assembly, and inspection.
Collaborate with Autodesk researchers, engineers, Technology Center staff, industry
partners, and academic institutions.
Contribute to research projects from problem definition and literature review through
experimentation, prototyping, analysis, validation, and implementation.
Translate research findings into prototypes, demonstrations, intellectual property,
technical publications, tools, and scalable technologies.
Communicate findings clearly through documentation, presentations, demonstrations,
and technical discussions.
Minimum Qualifications
Bachelor’s or Master’s degree in Manufacturing Engineering, Mechanical
Engineering, Industrial Engineering, Materials Engineering, Computer Science,
Robotics, or a related technical field.
Relevant experience in applied research, industrial research and development,
manufacturing engineering, process engineering, or a related environment.
Demonstrated understanding of multiple manufacturing processes rather than
experience limited to a single technology or process.
Ability to explain how manufacturing processes work, including relevant materials,
process parameters, equipment, constraints, sources of variation, and quality
considerations.
Understanding of manufacturing workflows from design and process planning
through production, inspection, and feedback.
Experience planning, conducting, and evaluating technical experiments.
Experience working with manufacturing, machine, sensor, simulation, inspection,
production, or other experimental datasets.
Ability to analyze data using appropriate statistical, computational, or visualization
methods.
Initial practical experience applying machine learning, generative AI, agentic
workflows, or other AI-assisted methods to engineering, manufacturing, research, or
physical systems.
Ability to prototype integrated systems involving software, data, hardware, sensors,
equipment, or physical processes.
Programming experience sufficient to support data analysis, automation,
experimentation, and prototype development.
Strong technical communication and collaboration skills.
Ability to work across disciplines and engage effectively with researchers, software
engineers, manufacturing specialists, and industry partners.
Preferred Qualifications
PhD in Manufacturing Engineering, Mechanical Engineering, Industrial Engineering,
Materials Engineering, Computer Science, Robotics, or a related field.
Applied knowledge of several manufacturing process families, such as:
o Machining and subtractive manufacturing
o Forming and sheet-metal processes
o Casting and molding
o Welding, joining, and assembly
o Additive manufacturing
o Inspection, metrology, and quality control
o Industrial automation and production systems
Experience applying machine learning or AI to manufacturing processes, production
systems, simulation, quality, inspection, maintenance, or process optimization.
Experience with AI-assisted engineering workflows, agentic systems, MCP or similar
toolchains, or AI-enabled automation.
Experience with physics-informed machine learning, surrogate modeling,
optimization, or AI for physical systems.
Familiarity with design of experiments, statistical process control, uncertainty,
process capability, or manufacturing-quality methods.
Experience integrating data from multiple sources, including machines, sensors,
simulations, inspection systems, and engineering software.
Familiarity with digital twins, model-based engineering, manufacturing simulation, or
process simulation.
Familiarity with industrial data standards and communication protocols such as
MQTT, MTConnect, OPC UA, or related industrial IoT technologies.
Experience developing research prototypes or intellectual property that has transferred
into an industrial or commercial setting.
Evidence of curiosity, research rigor, and the ability to work in areas where the
technical path is not yet defined.
Indicators of a Strong Fit
You may be a strong fit for this role if you:
Can compare several manufacturing methods and explain when and why each would
be used.
Understand that manufacturing data must be interpreted in the context of materials,
equipment, process parameters, tolerances, quality, and physical constraints.
Have conducted structured experiments and can explain how you formed a
hypothesis, selected measurements, analyzed results, and determined whether an
approach worked.
Have used AI or machine learning in a practical engineering or research project, even
if AI has not been the primary focus of your career.
Are comfortable moving between research questions, data analysis, software
prototypes, physical experiments, and technical discussions with manufacturing
specialists.
Are motivated by manufacturing problems rather than by robotics, software, or AI in
isolation.
Candidates whose experience is primarily in pure software development, building-
information modeling, architecture and construction, biomedical applications, robotics
without broader manufacturing-process knowledge, or a single additive-manufacturing
method may not have the manufacturing breadth required for this position.
