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Senior FEA Engineer

Оценка ИИ

Уникальная возможность работать в deep-tech стартапе на острие технологий (AI + Physics). Высокий балл за инновационность задач, международную среду и четкие перспективы карьерного роста до тимлида.


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Сложность вакансии

ЛегкоСложно
Оценка ИИ

Роль требует глубоких знаний в области механики деформируемого твердого тела, опыта работы с промышленными FEA-пакетами и навыков программирования на Python. Высокая сложность обусловлена необходимостью интеграции традиционного моделирования с методами машинного обучения и частыми международными командировками.

Анализ зарплаты

Медиана95 000 $
Рынок80 000 $ – 120 000 $
Оценка ИИ

Предлагаемая позиция Senior FEA Engineer в Сингапуре соответствует высокому уровню рыночных ожиданий для специалистов с PhD или Master's и опытом от 3-5 лет. В технологическом секторе Сингапура зарплаты для таких узких специалистов часто превышают средние показатели по отрасли.

Сопроводительное письмо

I am writing to express my strong interest in the Senior FEA Engineer position at PhysicsX. With a solid background in structural mechanics and extensive experience in high-fidelity multi-physics simulations, I am excited by your mission to accelerate hardware innovation through AI-driven software. My expertise in automating workflows using NASTRAN and Abaqus, combined with a deep understanding of parametric CAD modeling, aligns perfectly with your goal of building scalable optimization pipelines.

Throughout my career, I have focused on bridging the gap between complex engineering phenomena and practical, data-driven solutions. I have a proven track record of delivering high-quality simulation datasets and working closely with cross-functional teams to integrate CAE models into advanced AI tools. I am particularly drawn to PhysicsX's unique approach of using Deep Learning surrogates to push the boundaries of traditional FEA, and I am eager to bring my technical leadership and problem-solving skills to your delivery team in Singapore.

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Присоединяйтесь к PhysicsX, чтобы создавать будущее инженерии на стыке ИИ и численного моделирования!

Описание вакансии

About us

PhysicsX is a deep-tech company with roots in numerical physics and Formula One, dedicated to accelerating hardware innovation at the speed of software.

We are building an AI-driven simulation software stack for engineering and manufacturing across advanced industries. By enabling high-fidelity, multi-physics simulation through AI inference across the entire engineering lifecycle, PhysicsX unlocks new levels of optimization and automation in design, manufacturing, and operations — empowering engineers to push the boundaries of possibility. Our customers include leading innovators in Aerospace & Defense, Materials, Energy, Semiconductors, and Automotive.

Who We’re Looking For

As a Senior FEA Engineer (Delivery), you are a problem solver and a builder, who is passionate about creating practical solutions that enable customers to make better engineering decisions. You are someone who can grasp and apply advanced engineering concepts across multiple industries, and you excel at working directly with customers (often directly on-site) to build CAE models that are integrated into AI-tools that are both useful and used.

You bring deep expertise in structural mechanics, materials science, and multi-physics modelling, with a solid ability to apply fundamental knowledge to real-world phenomena across a wide range of engineering applications. Highly proficient in at least one of NASTRAN, ABAQUS or ANSYS Mechanical, adept at automating these tools to create scalable optimisation workflows that drive impactful results. Experience with open source FEA packages such as CalculiX or FEniCS would be highly advantageous. Proficiency in parametric CAD modelling (NX or CATIA) and coding in Python, (or the ability to quickly learn programming languages), is an advantage. Experience with explicit dynamics (LS-DYNA, Abaqus/Explicit) such as crash simulation is highly valued. Background in coupled simulations such as thermo-mechanical analysis and fluid-structure interaction modelling also is highly desirable.

With 3-5 years of industry experience (post Masters or PhD) in a commercial, non-research environment, you’re ready to hit the ground running. You’re confident setting up FEA simulations independently, interpreting complex results with depth, and making informed decisions based on solid engineering judgement.

This Role

In this role, you’ll work closely with our Data Scientists, Machine Learning Engineers, and customers to understand and define the engineering challenges we are solving.

You’ll play a crucial role in delivering high-fidelity simulations by:

  • Independently building complex structural and multi-physics models from geometry clean-up and meshing, to simulating and post-processing complex real-world phenomena, integrating experimental data for model validation.
  • Building robust parametric CAD models (NX or CATIA) coupled with simulation pipeline automation through scripting, for executing advanced design optimization and DoE studies.
  • Partner with customers to address their most complex engineering challenges through advanced FEA & AI solutions; communicate results clearly, recommend actionable next steps, and balance accuracy with efficiency under tight deadlines.
  • Working at the intersection of CAE and Data Science to generate high-quality simulation datasets for training Machine/Deep Learning models. Leveraging data sampling techniques to efficiently capture the design space, reduce computational cost, and enhance model accuracy.
  • Accelerate high-fidelity modelling by using Flux (our cloud platform) and on-premise HPC resources, going beyond smart meshing and model setup to achieve real performance gains.
  • Continuously improving engineering best practices, adapting FEA model setups and outputs to support the development of Deep Learning surrogates.
  • Documenting workflows and results comprehensively via technical reports and process documentation that enable knowledge transfer and support reproducible engineering methodologies
  • Combining project leadership with a strong commitment to mentoring junior colleagues, contributing to a culture of collaboration, growth, and shared success.
  • Traveling globally (North America, Europe, Asia, Oceania) up to 2–3 weeks per quarter to work side-by-side with customers in building solutions on-site.

As the role evolves, there are exciting opportunities for growth as an Individual Contributor (IC) or a Team Lead (TL), especially if you’re driven to take ownership of more complex projects and lead the direction of future solutions.

Our delivery teams drive innovation to turn AI models into practical solutions - read our blog to learn more about how you’ll contribute to this exciting journey!

We value diversity and are committed to equal employment opportunity regardless of sex, race, religion, ethnicity, nationality, disability, age, sexual orientation or gender identity. We strongly encourage individuals from groups traditionally underrepresented in tech to apply. To help make a change, we sponsor bright women from disadvantaged backgrounds through their university degrees in science and mathematics.

We collect diversity and inclusion data solely for the purpose of monitoring the effectiveness of our equal opportunities policies and ensuring compliance with UK employment and equality legislation. This information is confidential, used only in aggregate form, and will not influence the outcome of your application.

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Навыки

  • FEA
  • Nastran
  • Abaqus
  • ANSYS Mechanical
  • Python
  • CalculiX
  • FEniCS
  • NX
  • CATIA
  • LS-DYNA
  • Structural Analysis
  • Multi-physics
  • CAD
  • HPC

Возможные вопросы на собеседовании

Проверка глубины технических знаний в основном инструменте.

Опишите ваш опыт автоматизации рабочих процессов в NASTRAN, ABAQUS или ANSYS с использованием скриптов. Какие конкретные задачи оптимизации вы решали?

Оценка понимания специфики работы компании на стыке CAE и AI.

Как бы вы подошли к подготовке набора данных симуляций для обучения суррогатной модели глубокого обучения, чтобы обеспечить максимальную точность при минимальных вычислительных затратах?

Проверка навыков работы с геометрией для сложных расчетов.

Расскажите о самом сложном проекте по подготовке сетки (meshing) и очистке геометрии. С какими трудностями вы столкнулись и как обеспечили валидность модели?

Оценка опыта в продвинутых дисциплинах.

Есть ли у вас опыт работы с явной динамикой (explicit dynamics) или связанными задачами (FSI, тепло-механика)? Приведите пример реализации.

Проверка готовности к клиентской работе и командировкам.

Как вы расставляете приоритеты между точностью модели и жесткими сроками при работе на объекте заказчика? Приведите пример успешного взаимодействия.

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physicsx
Страна
Сингапур