The goal of this work is to develop a predictive method for evaluating material performance under thermal runaway conditions due to escaping gas and particles at materials used in venting structures. To achieve this, a neural network will be designed, trained, and applied to new materials. The neural network will be trained and validated using existing experimental and simulation data. The generated evaluation data will be compared with results from classical substitute and cell tests to evaluate the performance and reliability of the developed approach. Based on the obtained data, characteristic parameters for evaluating material failure under thermal runaway conditions will be identified and derived.

In the first step, a systematic literature review will be conducted on existing experimental, analytical, and simulation methods for evaluating fire protection materials in the thermal runaway context. Based on this, a suitable model for evaluating material performance in the context of thermal runaway will be developed, trained, and implemented. The model will be validated using experimental data to assess its predictive accuracy and robustness. Finally, the applicability of the developed approach and potential opportunities for further development will be critically discussed.

Key tasks:

• Conduct a systematic literature review on existing experimental, analytical, and simulation methods for evaluating fire protection materials in the thermal runaway context.

• Design, train, and implement a neural network for predictive evaluation of material performance under thermal runaway conditions.

• Evaluate and analyse experimental and simulation data to validate the model.

• Compare generated evaluation data with results from classical substitute and cell tests.

• Identify and derive characteristic parameters for evaluating material failure under thermal runaway conditions.

• Critically discuss the applicability of the developed approach and potential opportunities for further development.

Requirements:

• Bachelor's or master's degree in computer science, mechanical engineering, electrical engineering, or a related field.

• Experience in machine learning and deep learning.

• Familiarity with Python programming language and relevant libraries.

• Good understanding of thermal runaway phenomena and fire protection materials.

• Excellent communication and teamwork skills.

• Ability to work independently and manage multiple tasks.

Preferred skills:

• Experience with neural networks and deep learning frameworks such as TensorFlow or PyTorch.

• Familiarity with simulation software such as ANSYS or COMSOL.

• Knowledge of thermal analysis and heat transfer.

• Experience with data analysis and visualisation tools such as Matplotlib or Seaborn.

• Familiarity with version control systems such as Git.

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To be successful in this role, you will need 5+ years of experience in ML infrastructure, including model training and model deployments, hands-on experience with ML optimization, deep experience with cloud-based technologies, and proficiency with common programming languages and frameworks of ML. You should also have strong organizational and communication skills, experience working with graph databases and graph neural networks, and a deep understanding of the machine learning development lifecycle.

In addition to base salary, this job is eligible to receive equity in the form of restricted stock units, and depending on the position offered, it may also be eligible to receive a commission. Reddit offers a wide range of benefits to U.S.-based employees, including medical, dental, and vision insurance, 401(k) program with employer match, generous time off for vacation, and parental leave.

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When you see what modern language models are capable of, do you wonder, "How do these things work? How can we trust them?" The Interpretability team at Anthropic is working to reverse-engineer how trained models work because we believe that a mechanistic understanding is the most robust way to make advanced systems safe.

We're looking for researchers and engineers to join our efforts. People mean many different things by "interpretability". We're focused on mechanistic interpretability, which aims to discover how neural network parameters map to meaningful algorithms.

A few places to learn more about our work and team at a high level are this introduction to Interpretability from our research lead, Chris Olah; a discussion of our work on the Hard Fork podcast produced by the New York Times, and this blog post (and accompanying video) sharing more about some of the engineering challenges we’d had to solve to get these results.

Some of our team's notable publications include A Mathematical Framework for Transformer Circuits, In-context Learning and Induction Heads, Toy Models of Superposition, Scaling Monosemanticity, and our Circuits’ Methods and Biology papers.

This work builds on ideas from members' work prior to Anthropic such as the original circuits thread, Multimodal Neurons, Activation Atlases, and Building Blocks.

We aim to create a solid foundation for mechanistically understanding neural networks and making them safe (see our vision post).

In the short term, we have focused on resolving the issue of "superposition" (see Toy Models of Superposition, Superposition, Memorization, and Double Descent, and our May 2023 update), which causes the computational units of the models, like neurons and attention heads, to be individually uninterpretable, and on finding ways to decompose models into more interpretable components.

Our subsequent work found millions of features in Sonnet, one of our production language models, represents progress in this direction.

In our most recent work, we develop methods that allow us to build circuits using features and use this circuits to understand the mechanisms associated with a model's computation and study specific examples of multi-hop reasoning, planning, and chain-of-thought faithfulness on Haiku 3.5, one of our production models.

This is a stepping stone towards our overall goal of mechanistically understanding neural networks.

We often collaborate with teams across Anthropic, such as Alignment Science and Societal Impacts to use our work to make Anthropic’s models safer.

We also have an Interpretability Architectures project that involves collaborating with Pretraining.

Responsibilities

• Develop methods for understanding LLMs by reverse engineering algorithms learned in their weights
• Design and run robust experiments, both quickly in toy scenarios and at scale in large models
• Create and analyze new interpretability features and circuits to better understand how models work.
• Build infrastructure for running experiments and visualizing results
• Work with colleagues to communicate results internally and publicly

You may be a good fit if you:

• Have a strong track record of scientific research (in any field), and have done some work on Interpretability
• Enjoy team science – working collaboratively to make big discoveries
• Are comfortable with messy experimental science. We're inventing the field as we work, and the first textbook is years away
• You view research and engineering as two sides of the same coin. Every team member writes code, designs and runs experiments, and interprets results
• You can clearly articulate and discuss the motivations behind your work, and teach us about what you've learned. You like writing up and communicating your results, even when they're null

To learn more about the skills we look for and how to prepare for this role, see our blog post – So You Want to Work in Mechanistic Interpretability?

Familiarity with Python is required for this role.

Role Specific Location Policy:

• This role is based in San Francisco office; however, we are open to considering exceptional candidates for remote work on a case-by-case basis.

The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.

Annual Salary: $350,000-$850,000 USD

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The role reports to the Director, Machine Learning Science. This role can be a Hybrid role based in our Brisbane, California headquarters (2-3 days per week in office), or remote.

Key responsibilities include:

• Independently pursuing cutting-edge research in AI applied to biological problems
• Building new models or fine-tuning existing models to identify biological changes resulting from disease
• Building models that achieve high accuracy and that generalise robustly to new data
• Applying contemporary interpretability techniques to provide a deeper understanding of the underlying signal identified by the model, ideally suggesting potential biological mechanisms
• Working closely with ML Engineering partners to ensure that Freenome's computational infrastructure supports optimal model training and iteration
• Taking a mindful, transparent, and humane approach to your work

Requirements include:

• PhD or equivalent research experience with an AI emphasis and in a relevant, quantitative field such as Computer Science, Statistics, Mathematics, Engineering, Computational Biology, or Bioinformatics
• 3+ years of postdoc or post-PhD industry experience achieving impactful results using relevant modelling techniques
• Expertise, demonstrated by research publications or industry achievements, in applied machine learning, deep learning and complex data modelling
• Practical and theoretical understanding of fundamental ML models like generalised linear models, kernel machines, decision trees and forests, neural networks
• Practical and theoretical understanding of DL models like large language models or other foundation models
• Extensive experience with training paradigms like supervised learning, self-supervised learning, and contrastive learning
• Proficient in current state of the art in ML/DL approaches in different domains, with an ability to envision their applications in biological data
• Proficiency in a general-purpose programming language: Python, R, Java, C, C++, etc.
• Proficiency in one or more ML frameworks such as; Pytorch, Tensorflow and Jax; and ML platforms like Hugging Face
• Experience in ML analysis and developer tools like TensorBoard, MLflow or Weights & Biases
• Excellent ability to communicate across disciplines, work collaboratively, and make progress in smaller steps via experimental iterations
• A passion for innovation and demonstrated initiative in tackling new areas of research

Nice to have qualifications include:

• Deep domain-specific experience in computational biology, genomics, proteomics or a related field
• Experience in building DL models for genomic data, with knowledge of state-of-the-art DNA foundation models
• Experience in NGS data analysis and bioinformatic pipelines
• Experience with containerized cloud computing environments such as Docker in GCP, Azure, or AWS
• Experience in a production software engineering environment, including the use of automated regression testing, version control, and deployment systems

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We are seeking a highly motivated Staff Engineer to join our team, focusing on AI/ML and Digital Twin technologies. As a Staff Engineer, you will lead and execute technical engagements across the customer lifecycle, including discovery, solution development, demonstrations, evaluations, and deployment.

Responsibilities

• Lead and execute technical engagements across the customer lifecycle, including discovery, solution development, demonstrations, evaluations, and deployment.
• Engage directly with customers to understand engineering workflows, data availability, and decision-making processes, translating them into AI-enabled simulation and digital engineering solutions.
• Develop and implement differentiated solutions using technologies such as automation, reduced order modeling, optimization, simulation democratization, system-level modeling, and digital twins.
• Integrate machine learning models within simulation and digital twin pipelines to improve prediction accuracy, reduce computational cost, and enable near real-time insights.
• Define and deliver automated and scalable workflows that reduce reliance on expert-driven simulation and enable broader adoption across engineering teams.
• Lead or contribute to first-of-a-kind or ambiguous use cases, including AI-assisted design exploration, surrogate modeling, and digital twin deployment.
• Collaborate closely with product development teams to influence roadmap, validate new capabilities, and improve usability of AI-enabled features.
• Deliver professional services, training, and technical guidance to ensure successful adoption of advanced workflows.
• Support pre-sales and technical marketing activities through demonstrations, evaluations, and industry engagement.

Benefits

• Enable customers to transition from traditional simulation to AI-augmented and automated engineering workflows.
• Reduce time-to-insight through surrogate modeling, optimization, and intelligent automation.
• Expand access to simulation by supporting democratization across engineering and non-expert users.
• Drive adoption of digital twin technologies for predictive and operational decision-making.
• Influence product direction by connecting real-world use cases with next-generation AI-enabled capabilities.
• Contribute to business growth through high-impact technical engagements and solution delivery.

Requirements

• MS (or PhD) in Engineering, Computer Science, Applied Mathematics, or related field.
• 5+ years of experience in engineering systems, simulation, or data-driven modeling.
• Strong programming skills (Python preferred).
• Experience working with modeling, simulation, optimization, or data-driven engineering workflows.
• Strong analytical, problem-solving, and communication skills.
• Ability to operate effectively in a customer-facing, consultative engineering role.
• Proven experience in automation of engineering workflows or pipelines using tools such as optiSLang, modeFrontier, HEEDS or equivalent.
• Demonstrated expertise applying machine learning techniques in engineering contexts, including surrogate modeling, regression methods, or neural networks (CNNs, RNNs, autoencoders).
• Understanding of projection-based ROMs, dimensionality reduction, and feature engineering.
• Knowledge of multi-fidelity system modeling using Twin Builder, Simulink, AMESim or equivalent.
• Familiarity with deployment and operationalization of AI models, including integration into engineering workflows and use of frameworks such as PyTorch, TensorFlow, scikit-learn, Kubernetes, AWS/Azure equivalent.
• Exposure to cloud or HPC-based environments for large-scale simulation or data processing.

Who We Are Looking For

• Customer-focused and able to build trusted relationships.
• Comfortable working in ambiguous, fast-evolving technical environments.
• A strong communicator who can translate complex concepts into actionable insights.
• Self-driven, organized, and capable of managing multiple priorities.
• A collaborative team player who contributes to a culture of learning and innovation.

The Team You’ll Be A Part Of

You will be part of a multidisciplinary engineering team focused on advancing industry adoption of simulation through AI, automation, digital twin, and MBSE technologies. The team collaborates closely with customers, product development, and go-to-market functions to deliver innovative, high-impact solutions.

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Research Scientist, Robotics

Role Details

We are looking for Research Scientists to join the Robotics team at Google DeepMind. The team's mission is to build 'Embodied AI' - a robot brain capable of whole-body, dexterous, general and useful physical actions - to improve the lives of billions of people in the physical world.

Key Responsibilities

• Design, implement, train and evaluate large models and algorithms for robotic agents.
• Write software to implement research ideas and iterate quickly.
• Leverage expertise to participate in a wide variety of research, including learning from simulation, reinforcement learning, learning from demonstrations, vision-language-action models, transformers, video generation, robot control, humanoid robots and more.
• Work effectively with a large collaborative team with fast-paced agendas to meet ambitious research goals.
• Generate creative ideas, set up experiments and test hypotheses. Report and present research findings clearly and efficiently both internally and externally.

Requirements

• PhD in a technical field or equivalent practical experience.
• Knowledge of the latest in large machine learning research.
• Experience working with simulators and real-world robots.
• Expertise in using large datasets with deep neural networks to make real robots useful.
• A real passion for AI impacting real world robots!

Benefits

The US base salary range for this full-time position is between $166,000 - $244,000 + bonus + equity + benefits.

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When you see what modern language models are capable of, do you wonder, 'How do these things work? How can we trust them?'

The Interpretability team at Anthropic is working to reverse-engineer how trained models work because we believe that a mechanistic understanding is the most robust way to make advanced systems safe. We’re looking for researchers and engineers to join our efforts.

People mean many different things by 'interpretability'. We're focused on mechanistic interpretability, which aims to discover how neural network parameters map to meaningful algorithms. Some useful analogies might be to think of us as trying to do 'biology' or 'neuroscience' of neural networks using “microscopes” we build, or as treating neural networks as binary computer programs we're trying to 'reverse engineer'.

Responsibilities:

• Develop methods for understanding LLMs by reverse engineering algorithms learned in their weights

• Design and run robust experiments, both quickly in toy scenarios and at scale in large models

• Create and analyse new interpretability features and circuits to better understand how models work.

• Build infrastructure for running experiments and visualising results

• Work with colleagues to communicate results internally and publicly

You may be a good fit if you:

• Have a strong track record of scientific research (in any field), and have done _some_ work on Interpretability

• Enjoy team science – working collaboratively to make big discoveries

• Are comfortable with messy experimental science. We're inventing the field as we work, and the first textbook is years away

• You view research and engineering as two sides of the same coin. Every team member writes code, designs and runs experiments, and interprets results

• You can clearly articulate and discuss the motivations behind your work, and teach us about what you've learned. You like writing up and communicating your results, even when they're null

Role Specific Location Policy:

• This role is based in San Francisco office; however, we are open to considering exceptional candidates for remote work on a case-by-case basis.

Logistics

Education requirements: We require at least a Bachelor's degree in a related field or equivalent experience. Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.

Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.

We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.

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Anthropic's mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.

About the Interpretability team:

When you see what modern language models are capable of, do you wonder, 'How do these things work? How can we trust them?'

The Interpretability team's mission is to reverse engineer how trained models work, and Interpretability research is one of Anthropic's core research bets on AI safety. We believe that a mechanistic understanding is the most robust way to make advanced systems safe.

People mean many different things by 'interpretability'. We're focused on mechanistic interpretability, which aims to discover how neural network parameters map to meaningful algorithms. Some useful analogies might be to think of us as trying to do 'biology' or 'neuroscience' of neural networks, or as treating neural networks as binary computer programs we're trying to 'reverse engineer'.

About the role:

As a manager on the Interpretability team, you'll support a team of expert researchers and engineers who are trying to understand at a deep, mechanistic level, how modern large language models work internally.

Few things can accelerate this work more than great managers. Your work as manager will be critical in making sure that our fast-growing team is able to meet its ambitious safety research goals over the coming years. In this role, you will partner closely with an individual contributor research lead to drive the team's success, translating cutting-edge research ideas into tangible goals and overseeing their execution. You will manage team execution, careers and performance, facilitate relationships within and across teams, and drive the hiring pipeline.

Responsibilities:

• Partner with a research lead on direction, project planning and execution, hiring, and people development
• Set and maintain a high bar for execution speed and quality, including identifying improvements to processes that help the team operate effectively
• Coach and support team members to have more impact and develop in their careers
• Drive the team's recruiting efforts, including hiring planning, process improvements, and sourcing and closing
• Help identify and support opportunities for collaboration with other teams across Anthropic
• Communicate team updates and results to other teams and leadership
• Maintain a deep understanding of the team's technical work and its implications for AI safety

You may be a good fit if you:

• Are an experienced manager (minimum 2-5 years) with a track record of effectively leading highly technical research and/or engineering teams
• Have a background in machine learning, AI, or a related technical field
• Actively enjoy people management and are experienced with coaching and mentorship, performance evaluation, career development, and hiring for technical roles
• Have strong project management skills, including prioritization and cross-functional coordination and collaboration
• Have managed technical teams through periods of ambiguity and change
• Are a quick learner, capable of understanding and contributing to discussions on complex technical topics and are motivated to learn about our research
• Are a strong communicator

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San Francisco; New York City; Remote - US

Employment Type

Full time

Location Type

Hybrid

Department

Intelligence & Investigations

Compensation

• $198K – $220K • Offers Equity

The base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. If the role is non-exempt, overtime pay will be provided consistent with applicable laws. In addition to the salary range listed above, total compensation also includes generous equity, performance-related bonus(es) for eligible employees, and the following benefits.

• Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts

• Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit)

• 401(k) retirement plan with employer match

• Paid parental leave (up to 24 weeks for birth parents and 20 weeks for non-birthing parents), plus paid medical and caregiver leave (up to 8 weeks)

• Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees

• 13+ paid company holidays, and multiple paid coordinated company office closures throughout the year for focus and recharge, plus paid sick or safe time (1 hour per 30 hours worked, or more, as required by applicable state or local law)

• Mental health and wellness support

• Employer-paid basic life and disability coverage

• Annual learning and development stipend to fuel your professional growth

• Daily meals in our offices, and meal delivery credits as eligible

• Relocation support for eligible employees

• Additional taxable fringe benefits, such as charitable donation matching and wellness stipends, may also be provided.

More details about our benefits are available to candidates during the hiring process.

This role is at-will and OpenAI reserves the right to modify base pay and other compensation components at any time based on individual performance, team or company results, or market conditions.

About the Team

OpenAI’s mission is to ensure that general-purpose artificial intelligence benefits all of humanity. We believe achieving this goal requires real-world deployment and continuous iteration based on how our products are used—and misused—in practice.

The Intelligence and Investigations team supports this mission by detecting, investigating, and disrupting the misuse of our products, particularly critical or novel harms. Our work enables partner teams to develop data-backed model policies and build scalable safety mitigations. By precisely understanding abuse, we help ensure OpenAI’s products can be used safely to build meaningful, rewarding applications.

About the Role

As a Technical Abuse Investigator on the Intelligence and Investigations team, you will be responsible for detecting, investigating, and disrupting malicious use of OpenAI’s platform. You will further scale parts of the investigative process to help our team disrupt harm at scale. This role combines traditional investigative judgment with strong technical fluency: much of the work involves navigating complex datasets to surface actionable abuse signals, not just reviewing individual reports.

In addition to conducting investigations directly, this role is explicitly designed to act as a force multiplier for the broader investigations team. You will be scaling or automating highly manual, important and nuanced processes. You will design and implement lightweight technical solutions—such as notebook templates, data pipelines or internal utilities—that enable specialized investigators to identify, track, and action abuse at a greater scale than a single investigator can currently achieve. Success in this role is measured not only by investigations completed, but by how effectively your work enables you and your team members to operate more efficiently and consistently.

You will work closely with engineering, legal, investigations, security, and policy partners to respond to time-sensitive escalations, investigate activity that falls outside existing safeguards, and translate investigative insights into scalable detection and enforcement strategies.

This role includes participation in an on-call rotation to handle urgent escalations outside of normal work hours. Some investigations may involve sensitive content, including sexual, violent, or otherwise disturbing material. This role will work PST and is open to remote work within the United States, though we heavily prefer candidates based in San Francisco or New York.

In this role, you will:

• Detect, investigate and disrupt abuse and harm with policy, legal, global affairs, security, and engineering teams via complex datasets.

• Develop and iterate on abuse signals and investigative methods, scaling one-off insights to reduce manual effort and expand coverage.

• Build and maintain lightweight technical solutions (e.g., SQL/ Python data pipelines, investigation templates, dashboards, or internal utilities) for investigators focused on specific harm domains.

• Develop a deep understanding of OpenAI’s products, data systems, and enforcement mechanisms, and collaborate with engineering and data teams to improve investigative tooling, data quality, and workflows.

• Communicate investigation findings effectively to internal stakeholders through written briefs, data-backed recommendations, and escalation summaries

• Rotate (in-frequently) into an incident response role that requires rapid threat triaging, investigation, mitigation, sound judgement and concise briefing to senior leadership.

• Be someone people enjoy working with.

• Proven ability to quickly learn new processes, systems and team dynamics while thriving in ambiguous, rapidly changing, and high-pressure environments.

You might thrive in this role if you:

• Have a strong background in computer science, software engineering, or a related field.
• Have experience with data analysis, machine learning, or other technical skills relevant to the role.
• Are able to work effectively in a fast-paced, dynamic environment.
• Are able to communicate complex technical information to non-technical stakeholders.
• Are able to work independently and as part of a team.
• Are able to adapt to changing priorities and deadlines.
• Are able to maintain confidentiality and handle sensitive information.
• Are able to work in a remote environment.

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Microsoft AI are looking for a talented Model Design Staff - Language Engineer at their Redmond office. This role sits at the heart of strategic decision-making, turning market data into actionable insights for a company that's revolutionising the field of artificial intelligence. You'll work directly with leadership to shape the company's direction in the development of AI solutions.

About the Role

As a Model Design Staff - Language Engineer, you will be responsible for designing and developing language models that can be used in a variety of applications, including chatbots, virtual assistants, and language translation systems. You will work closely with cross-functional teams to understand business requirements and develop models that meet those needs. You will also be responsible for testing and deploying models, as well as monitoring their performance and making recommendations for improvement.

Accountabilities

• Design and develop language models that meet business requirements
• Work closely with cross-functional teams to understand business needs and develop models that meet those needs
• Test and deploy models, and monitor their performance
• Make recommendations for model improvement

The Candidate we're looking for

Experience:

• 3+ years of experience in natural language processing or a related field
• Experience with deep learning and neural networks

Technical skills:

• Python programming language
• Experience with TensorFlow or PyTorch
• Knowledge of natural language processing and machine learning algorithms

Personal attributes:

• Strong problem-solving skills
• Excellent communication and collaboration skills
• Ability to work in a fast-paced environment

Benefits

• Competitive salary and benefits package
• Opportunities for professional growth and development
• Collaborative and dynamic work environment
• Access to cutting-edge technology and tools
• Flexible work arrangements

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Microsoft are looking for a talented Principal Applied Scientist at their Bengaluru office. This role sits at the heart of strategic decision-making, turning market data into actionable insights for a company that's revolutionising the advertising ecosystem. You'll work directly with leadership to shape the company's direction in the threat modelling and adversarial defence space.

About the Role

As a Principal Applied Scientist, you will be responsible for developing and maintaining comprehensive adversarial frameworks to map the lifecycle of emerging threats, from account compromise (ATO) to malicious payload delivery. You will also advance the continuous, signal-based security protocol and research and implement behavioral biometrics and Proof of Liveness models to detect synthetic identities and coordinated fraud rings.

Accountabilities

• Develop and maintain comprehensive adversarial frameworks to map the lifecycle of emerging threats, from account compromise (ATO) to malicious payload delivery.
• Advance the continuous, signal-based security protocol.
• Research and implement behavioral biometrics and Proof of Liveness models to detect synthetic identities and coordinated fraud rings.

The Candidate we're looking for

Experience:

• Bachelor's, Master's, or PhD degree in Computer Science, Cybersecurity, Mathematics, or a related field, with 10+ years of related experience.

Technical skills:

• Deep technical expertise in Cybersecurity, Anti-Abuse, or Adversarial Machine Learning.
• Strong programming skills in C++ or Python (at least one is required), with experience in building production-quality security or ML systems.

Personal attributes:

• Strong communication and collaboration skills, with experience articulating complex security risks to business and product leadership.

Benefits

• Competitive salary and benefits package.
• Opportunities for professional growth and development.
• Collaborative and dynamic work environment.

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Your focus will be on Data- and Process Analytics (e.g. Process Mining), Data processing (data preparation, structuring, provision, and visualization) of real data from production and logistics processes or other business processes (end-to-end, e.g. in the field of plant and production technology, supply chain, financial services).

What you need

Your focus will be on Data- and Process Analytics (e.g. Process Mining), Data processing (data preparation, structuring, provision, and visualization) of real data from production and logistics processes or other business processes (end-to-end, e.g. in the field of plant and production technology, supply chain, financial services).

Why this matters

This role keeps a world-championship-winning F1 team running. When equipment fails, races can be lost, so your work directly impacts performance. You'll develop deep expertise in high-spec facilities and have clear progression into senior facilities management roles. The F1 environment means you'll work with cutting-edge building systems and learn from the best in the industry.

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