CNC - AI Solutions PP Developer

Job Title: AI Solutions Developer (CNC Manufacturing Software)
Req: R0097768
No. of Vacancies: 1 position (Experience mentioned below)
Location: Pune
Full-time/Part-time: Full-time
Regular /Temporary: Regular
Company Overview:
We’re on an exciting growth journey, building the new digital era of manufacturing. Our digital manufacturing solutions automate and connect the component manufacturing value chain – from design and planning to preparation, production and verification. Our objective is to make the shift towards efficient and sustainable manufacturing. As the market leader in CAM and metrology software with over 400,000 software licenses and global footprint, we have a unique foundation to build on. Please read more about us here.
We are seeking for people who are passionate in their work and possess the drive to excel to join us.
Position Overview:
Shape the Future of CNC Manufacturing with AI
At GibbsCAM, a leading CAM solution within Sandvik Manufacturing Solutions, we are redefining how manufacturing software is developed and delivered through the power of Artificial Intelligence.
We are looking for an innovative and technically skilled AI Solutions Developer to help transform engineering workflows, automate complex processes, and accelerate the development of CNC manufacturing solutions.
If you're passionate about AI, software development, automation, and manufacturing technologies, this is an exciting opportunity to play a key role in driving the next generation of intelligent engineering solutions.
What You'll Do
As an AI Solutions Developer, you will lead initiatives that leverage AI and modern automation technologies to improve engineering productivity, software quality, testing efficiency, and knowledge management across our Post Processor organization.
At GibbsCAM, you'll work at the intersection of advanced manufacturing, software engineering, and artificial intelligence.
Key Responsibilities:
Designing and implementing AI-powered tools and workflow automation solutions
Exploring and deploying emerging AI technologies, including LLMs and intelligent engineering assistants
Creating solutions that reduce manual effort and accelerate software development processes
Developing and maintaining CNC post processors within the GibbsCAM environment
Translating CAM toolpaths into machine-ready NC programs
Supporting machine validation, testing, and controller-specific implementations
Building and maintaining databases and knowledge repositories to support engineering teams
Collaborating with global engineering teams and sharing best practices across Sandvik business units
Acting as a champion for AI adoption and continuous improvement
What You Bring
Required Qualifications
Experience with CNC machining, CAM software, or CNC post processor development
Understanding of G-code, NC programming, CNC controllers, or machine kinematics
Ability to interpret technical documentation, machine manuals, and controller specifications
Experience working with relational and/or non-relational databases
Strong software development and problem-solving skills
Preferred Qualifications
Knowledge of AI-assisted software development and intelligent automation
Experience with Large Language Models (LLMs), AI productivity tools, or prompt engineering
Familiarity with software testing, validation, and automation frameworks
The Materials Modelling team accelerates the development of next-generation cutting tool materials through advanced modelling, simulation, data analytics, and AI. We work across multiple length scales, from atomic-scale phenomena to industrial processes, transforming scientific understanding into practical solutions that support materials design, process development, digitalization, and sustainable innovation. By combining materials science, computational methods, and data-driven approaches, we help reduce development time, improve decision-making, and enable virtual product development.
We’re now looking for a student who wants to complete their master's thesis with us, focusing on data-efficient exploration of compositions and mechanical properties using first-principles calculations and active learning.
Background and purpose
The computational design of materials for cutting tools requires predictive models that can describe mechanical-property trends across multicomponent composition spaces. Existing first-principles databases provide valuable information on elastic constants and related mechanical-property descriptors, but their coverage is generally non-uniform. In particular, predictions in sparsely sampled regions may be associated with substantial uncertainty.
Work Description
This thesis will develop active-learning strategies to reduce prediction uncertainty by identifying a limited but sufficient set of informative compositions for additional autoQMAS calculations. The newly generated data will be iteratively incorporated into a database to update and improve the predictive models.
The objective is to achieve a predefined confidence level across the compositional region of interest. At the same time, the number of additional calculations should be minimized and compositions with the best trade-off between hardness and toughness identified.
Project plan:
Weeks 1-2: Literature review and project definition
Weeks 3-4: Existing workflow and database - baseline model and uncertainty assessment
Weeks 5 -12: Active-learning method development and implementation
Weeks 13-15: Final analysis and documentation
Weeks 16-20: Writing thesis and preparing presentation
Location
The position is based in Västberga, Stockholm.
Profile
We are looking for a Master's student with a strong foundation in physics, materials science, applied mathematics, or computational engineering. The ideal candidate has experience with Python programming, scientific computing, data analysis, and machine learning. Knowledge of atomistic simulations, autoQMAS calculations, density functional theory (DFT), or computational materials science is meritorious. The project requires curiosity, initiative, and an interest in combining physics-based modelling with AI-driven approaches for accelerated materials design.
Our Culture
At Sandvik, we work with advanced technology and exciting innovations, but it is our people who are the true key to our success. We believe that diversity creates a better environment and that inclusion is essential to achieving strong results. This means supporting one another, sharing knowledge, and embracing each other's differences.
To learn more about us, we encourage you to visit our website, LinkedIn, or Facebook.
Scope
The master’s thesis project corresponds to 30 ECTS credits and is expected to be carried out full-time over approximately 20 weeks. The project is planned to start in January 2027.
Contact Information
For more information about the position, please contact Aayush Sharma, Senior R&D Professional +46 (0)70 616 79 47
We have carefully selected the recruitment channels and marketing methods we wish to use and kindly, but firmly, decline any additional contacts regarding these matters.
Application
Please submit your application no later than December 1, 2026. Click Apply and attach your CV and cover letter. Please note that we do not accept applications via email. Job ID: R0097769.

Work arrangement
Hybrid