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Senior Machine Learning Engineer

Оценка ИИ

Исключительная вакансия в сфере Deep Tech с сильным социальным пакетом (equity, 10% пенсия), интересными задачами на стыке физики и ИИ, а также возможностью международных командировок.


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Сложность вакансии

ЛегкоСложно
Оценка ИИ

Высокая сложность обусловлена требованием глубоких знаний в физике (CAE/CFD), работе с 3D-данными и необходимостью совмещать навыки инженерии данных с лидерскими качествами и частыми командировками.

Анализ зарплаты

Медиана100 000 £
Рынок85 000 £ – 130 000 £
Оценка ИИ

Предлагаемая роль Senior уровня в Лондоне в сфере Deep Tech обычно оплачивается выше среднего по рынку из-за специфических требований к знаниям физики и инженерии. Рыночный диапазон для таких позиций составляет £85,000 – £120,000 в год.

Сопроводительное письмо

I am writing to express my strong interest in the Senior Machine Learning Engineer position at PhysicsX. With a solid background in deploying end-to-end ML systems and a deep fascination with numerical physics, I am particularly drawn to your mission of accelerating hardware innovation through AI-driven simulation. My experience in building scalable data pipelines and working with 3D point-cloud data aligns perfectly with your technical requirements for geometry-aware modelling.

In my previous roles, I have successfully translated complex R&D outputs into production-ready libraries and mentored junior engineers to achieve high-impact results. I am excited by the prospect of working directly with customers across Aerospace and Automotive sectors to embed cutting-edge models into their engineering lifecycles. I am confident that my expertise in Python, PyTorch, and Kubeflow, combined with my ability to lead technical initiatives, will allow me to contribute significantly to the Delivery team at PhysicsX.

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Откликнитесь в physicsx уже сейчас

Присоединяйтесь к PhysicsX, чтобы внедрять передовые ИИ-решения в аэрокосмическую и автомобильную отрасли — подайте заявку сегодня!

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.

Note: We are currently recruiting for multiple positions, however please only apply for the role that best aligns with your skillset and career goals.

Who We're Looking For

As a Senior Machine Learning Engineer in Delivery, you are an experienced problem solver and technical leader who stays anchored to impact. You are someone who can grasp advanced engineering concepts across multiple industries, lead technical initiatives, and excel at working directly with customers (and often side-by-side with them on-site) to embed cutting-edge AI models into tools that are useful and used.

You’ve shipped ML systems end-to-end and at scale: you design, build and test reliable, scalable ML data pipelines; you know how to explore and manipulate 3D point-cloud and mesh data to enable geometry-aware modelling; you select the right libraries, frameworks and tools and make pragmatic product decisions that set Delivery up for success. Working at the intersection of data science and software engineering, you translate R&D and project outputs into reusable libraries, tooling and products.

With at least 3 years industry experience (post Masters or PhD) in a commercial, non-research environment, you're ready to not only execute but also lead and mentor others. You're truly excited about taking ownership of complex work streams and guiding teams to success, while continuously improving the systems and solutions you work on to ensure they are practical, impactful and meet the evolving needs of our customers.

This Role

As a Senior MLE, you'll work closely with our Data Scientists, Simulation Engineers, and customers to understand and define the engineering and physics challenges we are solving. You will iterate with customers and use your influence to drive decisions around reliable deployment with measurable outcomes.

You'll:

  • Own the deployment of ML models and engineering surrogates (e.g., deep learning on CAE/CFD/FEA data, time‑series forecasting, anomaly detection, optimization & control) to customer production environments.
  • Communicate results and trade‑offs to senior stakeholders; steer roadmaps and influence product direction with evidence.
  • Lead scoping and architecture design for data/ML systems; define success metrics, delivery plans and quality bars.
  • Excel at building robust and scalable ML systems, training and inference pipelines and APIs, running both on cloud and on-prem environments. The tech stack you will use for this includes: Python, PyTorch, Pandas, fastAPI, Scipy, Kubeflow, among others.
  • Mentor and develop engineers and data scientists; provide technical direction and clear, calm decision‑making under pressure.
  • Travel to customer sites in North America, Europe, Asia, Oceania, for an average of 3-4 weeks per quarter, where you'll collaborate closely with customers to build solutions on-site.
  • Own the scoping of new projects and work-streams with existing customers and taking part in bringing new customers to PhysicsX.

As a senior member of the team, you’ll significantly influence our technical direction and will be involved in shaping future solutions and products, while developing your skills as a technical leader.

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 operate on a hybrid model, with three days per week based in our Shoreditch office.

What we offer

  • Equity options – share in our success and growth.
  • 10% employer pension contribution – invest in your future.
  • Free office lunches – great food to fuel your workdays.
  • Flexible working – balance your work and life in a way that works for you.
  • Hybrid setup – enjoy our new Shoreditch office while keeping remote flexibility.
  • Enhanced parental leave – support for life’s biggest milestones.
  • Private healthcare – comprehensive coverage
  • Personal development – access learning and training to help you grow.
  • Work from anywhere – extend your remote setup to enjoy the sun or reconnect with loved ones.

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

  • Python
  • PyTorch
  • Pandas
  • FastAPI
  • SciPy
  • Kubeflow
  • Machine Learning
  • Deep Learning
  • 3D Point Cloud
  • Mesh Data
  • CFD
  • FEA
  • CAE
  • Time Series Forecasting
  • Anomaly Detection

Возможные вопросы на собеседовании

Вакансия предполагает работу с симуляциями и геометрическими данными.

Как бы вы подошли к обработке и нормализации 3D-облаков точек или мешей для обучения нейронной сети, чувствительной к геометрии?

Роль Senior предполагает лидерство в архитектуре.

Опишите ваш опыт проектирования масштабируемого ML-пайплайна для деплоя моделей в закрытых (on-prem) контурах заказчика.

Позиция требует тесного взаимодействия с клиентами.

Расскажите о случае, когда вам приходилось объяснять сложные технические компромиссы ML-модели нетехническому заказчику или стейкхолдеру.

Упоминается работа с временными рядами и аномалиями.

Какие архитектуры нейронных сетей вы считаете наиболее эффективными для прогнозирования временных рядов в контексте инженерных данных?

Вакансия включает менторство.

Как вы подходите к код-ревью и техническому развитию менее опытных членов команды в условиях сжатых сроков поставки?

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physicsx
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