Machine Learning Engineer BeyondMath is a pioneering startup, backed by top-tier VCs, on a mission to reshape the frontiers of engineering through Foundational AI models for Physics. We are replacing traditional, slow and expensive simulation methods with AI that rivals accuracy at orders of magnitude higher speed. We are moving beyond the "generic AI" hype to solve the world's hardest physical engineering challenges in automotive, aerospace, and energy. The Role As a Machine Learning Engineer, you'll play a central role in advancing our Generative Physics simulation platform. You'll work at the intersection of ML research and engineering contributing to core model development, shaping model architecture, and delivering performant systems that integrate seamlessly into our real-world design optimization workflows. You'll work closely with our ML research team, software engineers, and industry partners to deploy robust, scalable models that deliver real-world impact. Responsibilities Physics-Focused AI Model Development: Design and train deep learning models for physics simulation across aerodynamic and engineering domains. Scalability & Performance: Drive optimization efforts for model inference speed, accuracy, and robustness on large-scale industrial datasets. Geometry Representation: Research effective ways to represent geometric design variations for efficient use by machine learning models. Production Integration: Partner with engineering teams to deploy and monitor models in production-grade pipelines and tools. Architecture & Design: Contribute to design decisions around model and data architecture, tooling, and ML infrastructure. Essential Requirements Industrial Experience: Strong track record applying ML to complex real-world problems (ideally including geometry or physical systems). Foundational Knowledge: Deep understanding of machine learning theory, including optimization, generalisation, and various model architectures. Programming: Strong python skills and experience with deep learning libraries (TensorFlow/PyTorch/JAX). Communication: Ability to clearly explain complex ML concepts and research findings to both technical and non-technical audiences. Education: Master's Degree (PhD preferred) in Machine Learning, Computer Science, or a related quantitative field. Highly Desirable Aerodynamics/CFD Expertise: Familiarity with aerodynamic principles and computational fluid dynamics is a major plus. Design Optimization: Prior experience in optimization algorithms, particularly inthe context of engineering design. Physics/Science ML: Experience integrating physical laws or constraints intomachine learning models. Why Join Us? Full Ownership: You will have a direct seat at the table in shaping the future of a company redefining an entire industry. High Impact: Your work will directly accelerate the transition to sustainable energy and more efficient transport. Elite Team: Work alongside veterans from world-leading AI labs and engineering firms in a culture of "impact with integrity."
23/07/2026
Full time
Machine Learning Engineer BeyondMath is a pioneering startup, backed by top-tier VCs, on a mission to reshape the frontiers of engineering through Foundational AI models for Physics. We are replacing traditional, slow and expensive simulation methods with AI that rivals accuracy at orders of magnitude higher speed. We are moving beyond the "generic AI" hype to solve the world's hardest physical engineering challenges in automotive, aerospace, and energy. The Role As a Machine Learning Engineer, you'll play a central role in advancing our Generative Physics simulation platform. You'll work at the intersection of ML research and engineering contributing to core model development, shaping model architecture, and delivering performant systems that integrate seamlessly into our real-world design optimization workflows. You'll work closely with our ML research team, software engineers, and industry partners to deploy robust, scalable models that deliver real-world impact. Responsibilities Physics-Focused AI Model Development: Design and train deep learning models for physics simulation across aerodynamic and engineering domains. Scalability & Performance: Drive optimization efforts for model inference speed, accuracy, and robustness on large-scale industrial datasets. Geometry Representation: Research effective ways to represent geometric design variations for efficient use by machine learning models. Production Integration: Partner with engineering teams to deploy and monitor models in production-grade pipelines and tools. Architecture & Design: Contribute to design decisions around model and data architecture, tooling, and ML infrastructure. Essential Requirements Industrial Experience: Strong track record applying ML to complex real-world problems (ideally including geometry or physical systems). Foundational Knowledge: Deep understanding of machine learning theory, including optimization, generalisation, and various model architectures. Programming: Strong python skills and experience with deep learning libraries (TensorFlow/PyTorch/JAX). Communication: Ability to clearly explain complex ML concepts and research findings to both technical and non-technical audiences. Education: Master's Degree (PhD preferred) in Machine Learning, Computer Science, or a related quantitative field. Highly Desirable Aerodynamics/CFD Expertise: Familiarity with aerodynamic principles and computational fluid dynamics is a major plus. Design Optimization: Prior experience in optimization algorithms, particularly inthe context of engineering design. Physics/Science ML: Experience integrating physical laws or constraints intomachine learning models. Why Join Us? Full Ownership: You will have a direct seat at the table in shaping the future of a company redefining an entire industry. High Impact: Your work will directly accelerate the transition to sustainable energy and more efficient transport. Elite Team: Work alongside veterans from world-leading AI labs and engineering firms in a culture of "impact with integrity."
Moog is a performance culture that empowers people to achieve great things. Our people enjoy solving interesting technical challenges in a culture where everyone trusts each other to do the right thing. For you, working with us can mean deeper job satisfaction, better rewards, and a great quality of life inside and outside of work. Job Title Lead Systems Analysis Engineer / Principal Engineer (Internal title: Staff Engineer). Location & Schedule Onsite - Tewkesbury, GBR. Reporting To Manager, Engineering. Position Overview Moog Industrial Group is seeking a Lead Systems Analysis Engineer to support our growing business in motorsport and high-performance vehicles. You will lead the development of electrohydraulic and electromechanical components and systems, guiding projects from requirements definition and concept development through simulation, validation, and testing. In this highly technical leadership role, you will apply seasoned engineering judgment to evaluate, adapt, and extend both standard and non standard design techniques. You will independently create new approaches to complex challenges, coordinate major engineering efforts, and guide cross functional engineering teams. The position also includes substantial responsibility for sustaining existing products while driving forward new development programs. Responsibilities Lead the functional analysis, detailed trade studies, requirements allocation, and interface definition studies to translate customer requirements into component specifications. Effectively communicate internal design requirements to the design/project team. Drive the identification, planning, and definition of all Verification and Validation strategies for product and system development initiatives. Lead system or subsystem level integration and testing in collaboration with customers, including anomaly investigations. Apply working knowledge of magnetic analysis, computational fluid dynamics, and finite element analysis. Create concise reports that describe design decisions and analysis results. Lead customer engagement for new business opportunities, leveraging model based design to develop and support technical solutions. Drive the development of models and simulation tools that enable strategic growth initiatives and customer prototyping; maintain, update, and evaluate modeling and simulation tools as required. Provide technical leadership and mentorship to junior engineers, fostering their professional growth and development. Qualifications Bachelor of Science in Electrical, Mechanical, Aerospace, or Systems Engineering (Master's preferred). Minimum 10 years of progressive engineering experience. Proven expertise in performance analysis in Simulink, V&V, subsystem integration, and technical leadership. Demonstrated expertise in hydraulic systems and classical control theory. Proven experience in developing simulation and/or sizing tools. Demonstrated success in conducting failure analysis and implementing effective corrective actions. Proficiency in MATLAB/Simulink. Strong capabilities in building strategic working relationships, planning and organizing, and customer focus. Familiarity with servovalve design is preferred. What We Offer Moog named to Glassdoor's 2026 Best Places to Work. Flexible benefits package and development opportunities to support career progression. 33 days annual leave (including bank holidays). Private medical insurance, mental health support and financial advice. Onsite 24/7 state of the art gym. Generous life assurance and company pension contribution (from 6%). Employee share options. EV charging. EEO Statement Moog is committed to diversity, equity, and inclusion. Employees are valued, respected, and given equal opportunities to bring their authentic selves to work.
21/07/2026
Full time
Moog is a performance culture that empowers people to achieve great things. Our people enjoy solving interesting technical challenges in a culture where everyone trusts each other to do the right thing. For you, working with us can mean deeper job satisfaction, better rewards, and a great quality of life inside and outside of work. Job Title Lead Systems Analysis Engineer / Principal Engineer (Internal title: Staff Engineer). Location & Schedule Onsite - Tewkesbury, GBR. Reporting To Manager, Engineering. Position Overview Moog Industrial Group is seeking a Lead Systems Analysis Engineer to support our growing business in motorsport and high-performance vehicles. You will lead the development of electrohydraulic and electromechanical components and systems, guiding projects from requirements definition and concept development through simulation, validation, and testing. In this highly technical leadership role, you will apply seasoned engineering judgment to evaluate, adapt, and extend both standard and non standard design techniques. You will independently create new approaches to complex challenges, coordinate major engineering efforts, and guide cross functional engineering teams. The position also includes substantial responsibility for sustaining existing products while driving forward new development programs. Responsibilities Lead the functional analysis, detailed trade studies, requirements allocation, and interface definition studies to translate customer requirements into component specifications. Effectively communicate internal design requirements to the design/project team. Drive the identification, planning, and definition of all Verification and Validation strategies for product and system development initiatives. Lead system or subsystem level integration and testing in collaboration with customers, including anomaly investigations. Apply working knowledge of magnetic analysis, computational fluid dynamics, and finite element analysis. Create concise reports that describe design decisions and analysis results. Lead customer engagement for new business opportunities, leveraging model based design to develop and support technical solutions. Drive the development of models and simulation tools that enable strategic growth initiatives and customer prototyping; maintain, update, and evaluate modeling and simulation tools as required. Provide technical leadership and mentorship to junior engineers, fostering their professional growth and development. Qualifications Bachelor of Science in Electrical, Mechanical, Aerospace, or Systems Engineering (Master's preferred). Minimum 10 years of progressive engineering experience. Proven expertise in performance analysis in Simulink, V&V, subsystem integration, and technical leadership. Demonstrated expertise in hydraulic systems and classical control theory. Proven experience in developing simulation and/or sizing tools. Demonstrated success in conducting failure analysis and implementing effective corrective actions. Proficiency in MATLAB/Simulink. Strong capabilities in building strategic working relationships, planning and organizing, and customer focus. Familiarity with servovalve design is preferred. What We Offer Moog named to Glassdoor's 2026 Best Places to Work. Flexible benefits package and development opportunities to support career progression. 33 days annual leave (including bank holidays). Private medical insurance, mental health support and financial advice. Onsite 24/7 state of the art gym. Generous life assurance and company pension contribution (from 6%). Employee share options. EV charging. EEO Statement Moog is committed to diversity, equity, and inclusion. Employees are valued, respected, and given equal opportunities to bring their authentic selves to work.
Job Family: Software Req ID: 513810 Siemens Digital Industries Software is a leading provider of solutions for the design, simulation, and manufacture of products across many different industries. Formula 1 cars, skyscrapers, ships, space exploration vehicles, and many of the objects we see in our daily lives are being conceived and manufactured using our software. Are you a Physicist who enjoys working at the intersection of numerical methods, high-performance computing, and real-world engineering applications? We're hiring a CFD Solver Developer to join our M-Star team - a small yet highly effective and efficient specialized team that delivers world-class CFD software to multiple industries including Life Sciences, Chemical Material, and Industrial. M-Star is modern computational fluid dynamics (CFD) software that provides first-principles modeling tools for scientists and engineers, generating predictions that are functionally indistinguishable from measured data ( ). M-Star is building GPU-native, multiphysics solvers for complex fluid and particle systems. Our platform is designed to deliver high-fidelity, predictive simulations of real industrial processes, including turbulent mixing, multiphase flows, particle transport, heat and mass transfer, and chemically reactive systems. We focus on mechanistic modeling rooted in transport physics, rather than empirical tuning, enabling reliable predictions across a wide range of operating conditions and scales. If you are seeking a role where you can contribute directly to solver architecture, algorithm development, GPU acceleration, and multiphysics model implementation, while collaborating with users and support engineers to ensure the software remains both scientifically rigorous and practically useful, then we'd love to hear from you! You'll make a difference by: Developing M-Star's fluid dynamics solver, including maintenance, feature addition, algorithm implementation, and validation. Translating academic research in numerical methods into a high-performance, easy-to-use software product. Modeling a wide range of physics including fluid dynamics, multiphase flows, advection-diffusion, particle mechanics, and heat transfer. Implementing meshing algorithms and data structures for GPU architectures in a distributed memory environment. Working with support engineers and users to tailor software to current needs. Performing validation studies and presenting results at conferences. Your success is grounded in: Expertise with Lattice Boltzmann Methods (LBM) for fluid simulation. Background in numerical methods for transport physics (fluid dynamics, advection-diffusion, particle mechanics, or heat transfer). Writing high-performance physics codes using parallel computing in shared and distributed memory systems. Knowledge of the Discrete Element Method (DEM) for particle mechanics. Specialization in numerical methods for multiphase modeling of liquid-liquid and gas-liquid systems. Algorithms for computational geometry including structured/unstructured meshing, 3D search, and mesh refinement. Detailed understanding of GPU architectures and CUDA toolkit. MPI programming for multi-GPU code development. Experience working on a large multiphysics solver. Join our Digital World At Siemens Software, flexibility is how we work-hybrid by default, built on trust and autonomy. Together, 30,000 people across more than 200 countries build technology that shapes the real world. You'll grow through real projects, strong technical peers, and global mobility, backed by the scale and benefits of an industrial software leader. We're committed to equality and inclusion, and we hire based on merit, skills, and impact. Bring your curiosity and creativity and help us shape tomorrow! Compensation & Benefits The salary range for this position is 53,600 to 91,100 and this role is eligible to earn incentive compensation. The actual compensation offered is based on the successful candidate's job-related skills, experience, and relevant education/training. Siemens offers health and wellness benefits to employees; you can access the benefits available in your country via the link: Diversity & Inclusion We value equal opportunities and welcome applications from all candidates. At Siemens, we believe people who have had real experiences dealing with being different will excel as leaders. Let's foster a culture of creativity and innovation. We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation. Siemens Software. Transform the Everyday Organization: Digital Industries Job Type: Full-time Category: Research & Development
15/07/2026
Full time
Job Family: Software Req ID: 513810 Siemens Digital Industries Software is a leading provider of solutions for the design, simulation, and manufacture of products across many different industries. Formula 1 cars, skyscrapers, ships, space exploration vehicles, and many of the objects we see in our daily lives are being conceived and manufactured using our software. Are you a Physicist who enjoys working at the intersection of numerical methods, high-performance computing, and real-world engineering applications? We're hiring a CFD Solver Developer to join our M-Star team - a small yet highly effective and efficient specialized team that delivers world-class CFD software to multiple industries including Life Sciences, Chemical Material, and Industrial. M-Star is modern computational fluid dynamics (CFD) software that provides first-principles modeling tools for scientists and engineers, generating predictions that are functionally indistinguishable from measured data ( ). M-Star is building GPU-native, multiphysics solvers for complex fluid and particle systems. Our platform is designed to deliver high-fidelity, predictive simulations of real industrial processes, including turbulent mixing, multiphase flows, particle transport, heat and mass transfer, and chemically reactive systems. We focus on mechanistic modeling rooted in transport physics, rather than empirical tuning, enabling reliable predictions across a wide range of operating conditions and scales. If you are seeking a role where you can contribute directly to solver architecture, algorithm development, GPU acceleration, and multiphysics model implementation, while collaborating with users and support engineers to ensure the software remains both scientifically rigorous and practically useful, then we'd love to hear from you! You'll make a difference by: Developing M-Star's fluid dynamics solver, including maintenance, feature addition, algorithm implementation, and validation. Translating academic research in numerical methods into a high-performance, easy-to-use software product. Modeling a wide range of physics including fluid dynamics, multiphase flows, advection-diffusion, particle mechanics, and heat transfer. Implementing meshing algorithms and data structures for GPU architectures in a distributed memory environment. Working with support engineers and users to tailor software to current needs. Performing validation studies and presenting results at conferences. Your success is grounded in: Expertise with Lattice Boltzmann Methods (LBM) for fluid simulation. Background in numerical methods for transport physics (fluid dynamics, advection-diffusion, particle mechanics, or heat transfer). Writing high-performance physics codes using parallel computing in shared and distributed memory systems. Knowledge of the Discrete Element Method (DEM) for particle mechanics. Specialization in numerical methods for multiphase modeling of liquid-liquid and gas-liquid systems. Algorithms for computational geometry including structured/unstructured meshing, 3D search, and mesh refinement. Detailed understanding of GPU architectures and CUDA toolkit. MPI programming for multi-GPU code development. Experience working on a large multiphysics solver. Join our Digital World At Siemens Software, flexibility is how we work-hybrid by default, built on trust and autonomy. Together, 30,000 people across more than 200 countries build technology that shapes the real world. You'll grow through real projects, strong technical peers, and global mobility, backed by the scale and benefits of an industrial software leader. We're committed to equality and inclusion, and we hire based on merit, skills, and impact. Bring your curiosity and creativity and help us shape tomorrow! Compensation & Benefits The salary range for this position is 53,600 to 91,100 and this role is eligible to earn incentive compensation. The actual compensation offered is based on the successful candidate's job-related skills, experience, and relevant education/training. Siemens offers health and wellness benefits to employees; you can access the benefits available in your country via the link: Diversity & Inclusion We value equal opportunities and welcome applications from all candidates. At Siemens, we believe people who have had real experiences dealing with being different will excel as leaders. Let's foster a culture of creativity and innovation. We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation. Siemens Software. Transform the Everyday Organization: Digital Industries Job Type: Full-time Category: Research & Development
The Aerodynamic AI Engineer is part of a dedicated team leveraging data and artificial intelligence to enhance aerodynamic development, performance analysis, and operational efficiency within the Aerodynamics department. Reporting to the Lead AI Engineer, you will work on specialist projects that bridge aerodynamic engineering and AI - developing advanced machine learning models, surrogate models, and automated geometry tools that give Williams Racing a competitive edge in vehicle development. This is a technically demanding, hands on role in a fast paced, high pressure environment. You will collaborate closely with aerodynamicists and CFD engineers, translating complex engineering requirements into practical AI solutions and communicating your findings with clarity and impact. What you'll do Develop and deploy AI models for the analysis of CFD simulation data, extracting insights to support aerodynamic development decisions. Build advanced surrogate models for aerodynamic predictions, with a particular focus on fluid dynamics applications. Develop AI-driven mesh generation algorithms and automated geometry creation tools for aerodynamic applications. Conduct comprehensive analysis of wind tunnel data, including drift detection, anomaly identification, and statistical analysis to ensure data quality and reliability. Build and maintain robust CI/CD pipelines for AI model deployment, ensuring high code quality standards across all aerodynamic AI applications. Collaborate with aerodynamicists and CFD engineers to translate engineering requirements into AI solutions and communicate complex insights effectively. Stay current with emerging AI technologies relevant to computational fluid dynamics and aerodynamic applications. Identify AI-driven opportunities to improve aerodynamic development efficiency within cost cap requirements. Qualifications Proven experience developing and deploying AI/ML models, particularly for scientific or engineering applications. Strong proficiency in Python with the PyTorch framework. Understanding of computational geometry principles and familiarity with mesh generation algorithms. Experience with statistical analysis and anomaly detection techniques for large scientific datasets. Strong software engineering practices including CI/CD pipeline development and code quality standards. Excellent communication skills, with the ability to collaborate across technical disciplines and translate complex AI concepts for engineering audiences. Demonstrated ability to manage multiple projects and deliver results in a fast paced, high pressure environment. Master's or PhD in Engineering, Physics, Computer Science, Mathematics, or a related scientific discipline (or equivalent practical experience). Experience with NVIDIA PhysicsNemo or similar physics-informed machine learning frameworks. Knowledge of fluid dynamics concepts and CFD data analysis. Experience in motorsport, Formula 1, or aerospace aerodynamics.
04/07/2026
Full time
The Aerodynamic AI Engineer is part of a dedicated team leveraging data and artificial intelligence to enhance aerodynamic development, performance analysis, and operational efficiency within the Aerodynamics department. Reporting to the Lead AI Engineer, you will work on specialist projects that bridge aerodynamic engineering and AI - developing advanced machine learning models, surrogate models, and automated geometry tools that give Williams Racing a competitive edge in vehicle development. This is a technically demanding, hands on role in a fast paced, high pressure environment. You will collaborate closely with aerodynamicists and CFD engineers, translating complex engineering requirements into practical AI solutions and communicating your findings with clarity and impact. What you'll do Develop and deploy AI models for the analysis of CFD simulation data, extracting insights to support aerodynamic development decisions. Build advanced surrogate models for aerodynamic predictions, with a particular focus on fluid dynamics applications. Develop AI-driven mesh generation algorithms and automated geometry creation tools for aerodynamic applications. Conduct comprehensive analysis of wind tunnel data, including drift detection, anomaly identification, and statistical analysis to ensure data quality and reliability. Build and maintain robust CI/CD pipelines for AI model deployment, ensuring high code quality standards across all aerodynamic AI applications. Collaborate with aerodynamicists and CFD engineers to translate engineering requirements into AI solutions and communicate complex insights effectively. Stay current with emerging AI technologies relevant to computational fluid dynamics and aerodynamic applications. Identify AI-driven opportunities to improve aerodynamic development efficiency within cost cap requirements. Qualifications Proven experience developing and deploying AI/ML models, particularly for scientific or engineering applications. Strong proficiency in Python with the PyTorch framework. Understanding of computational geometry principles and familiarity with mesh generation algorithms. Experience with statistical analysis and anomaly detection techniques for large scientific datasets. Strong software engineering practices including CI/CD pipeline development and code quality standards. Excellent communication skills, with the ability to collaborate across technical disciplines and translate complex AI concepts for engineering audiences. Demonstrated ability to manage multiple projects and deliver results in a fast paced, high pressure environment. Master's or PhD in Engineering, Physics, Computer Science, Mathematics, or a related scientific discipline (or equivalent practical experience). Experience with NVIDIA PhysicsNemo or similar physics-informed machine learning frameworks. Knowledge of fluid dynamics concepts and CFD data analysis. Experience in motorsport, Formula 1, or aerospace aerodynamics.
