Principal Research Scientist - Machine Learning

PhysicsX

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 LEVELS AND POSITIONS, HOWEVER PLEASE ONLY APPLY FOR THE ROLE THAT BEST ALIGNS WITH YOUR SKILLSET AND CAREER GOALS. WHAT YOU WILL DO  * Own Research work-streams at a high-level to deliver outcomes. * Align priorities with problem stakeholders, internal and external. * Set the technical direction for the stream and apply judgement and taste to drive progress. * Plan roadmaps with clear milestones for key decisions and outcomes. * Organise and guide the more junior members of the team to effectively execute and deliver against this roadmap. * Communicate purpose and key outcomes to raise awareness across the company and create opportunities for use and deployment. * Contribute towards Research group strategy and culture. * Identify research areas that would be valuable to the company and champion their development, ordering wrt other research objectives. * Promote effective working patterns and proactively flag issues with team dynamics to foster a productive environment. * Nurture younger colleagues to grow their skillset and guide their professional development. * The below activities in particular. * Work closely with our machine learning engineers, simulation engineers, and customers to translate physics and engineering challenges into mathematical problem formulations. * Build models to predict the behaviour of physical systems using state-of-the-art machine learning and deep learning techniques. * Discuss the results and implications of your work with colleagues and customers, especially how these results can address real-world problems. * Collaborate with colleagues beyond the research team to translate your models into production-ready code. * Communicate your work to others internally and externally as called for in paper publication venues, industry workshops, customer conversations, etc. This will involve writing for academic and non-academic audiences.   WHAT YOU BRING TO THE TABLE * Ability to scope and effectively deliver projects. * Enthusiasm about using machine learning, especially deep learning and/or probabilistic methods, for science and engineering. * Strong problem-solving skills and the ability to analyse issues, identify causes, and recommend solutions quickly. * Excellent collaboration and communication skills — with teams and customers alike. * PhD in computer science, machine learning, applied statistics, mathematics, physics, engineering, or a related field, with particular expertise in any of the following: 1. operator learning (neural operators), or other probabilistic methods for PDEs; 2. geometric deep learning or other 3D computer vision methods for point-cloud or mesh-structured data; 3. generative models for geometry and spatiotemporal data (VAEs, Diffusion Models, Bayesian non-parametric, scaling to large datasets, etc.). * Ideally, >4 years of experience in a data-driven role in a professional industry setting, where you have been instrumental in: * building machine learning models and pipelines in Python, using common libraries and frameworks (PyTorch / CUDA, ideally with exposure to JAX, NumPy / SciPy), especially including deep learning applications; * developing models for bespoke problem settings that involve high-dimensional data (spatiotemporal, geometric, physical); * iterating on network architectures and model structure, tuning and optimising for inductive biases, improved generalisability, and improved performance; * combining theoretical reasoning with empirical intuition to guide investigation; * formulating and running experiment pipelines to benchmark models and produce comparable results; * writing skills for communication complex technical concepts to peers and non-peers, tailoring the message for the required audience. * Publication record in reputable venues that demonstrates mastery in your field, and in particular the domains of interest listed above. Desirable venues include (but not limited to): NeurIPS, ICML, ICLR, UAI, AISTATS, AAAI, Siggraph, CVPR or TPAMI/JMLR.   WHAT WE OFFER Build what actually matters Help shape an AI-native engineering company at a formative stage, tackling problems that genuinely matter for industry and society. This is work with real-world impact - and something you can be proud to stand behind. Learn alongside exceptional people Work with a high-caliber, collaborative team of engineers, scientists, and operators who care deeply about doing great work, and about helping each other get better. We come from diverse backgrounds, but we share a commitment to operating at the highest level and addressing some of the most complex challenges out there. If you’re ambitious, thoughtful, and driven by impact, you’ll feel at home. Influence over hierarchy We operate with a flat structure: good ideas win - wherever they come from. Questioning assumptions and challenging the status quo isn’t just welcomed, it’s expected. Sustainable pace, long-term ambition Building meaningful technology is a marathon, not a sprint. We believe in balancing focused, ambitious work with a life beyond it. Our hybrid model blends time together in our Shoreditch office with work-from-home days, giving you the flexibility to work sustainably while staying connected in person. And it doesn’t stop there … 🚀 Equity options - share meaningfully in the company you’re helping to build. 🏦 10% employer pension contribution - because investing in future matters. 🍽️ Free office lunches - to keep you energised and focused. 👶 Enhanced parental leave - 3 months full pay paternity and 6 months full pay maternity leave, to provide extra flexibility during the moments that matter most. 🍼 YellowNest nursery scheme - to help working parents manage childcare costs. ☀️ 25 days of Annual Leave (+ Public Holidays) - because taking time to rest matters. 🏥 Private medical insurance - 100% employee cover, giving you complete peace of mind. 💪 Wellhub Subscription - gain access to thousands of gyms, classes and wellness apps, supporting both physical and mental wellbeing. 👀 Eye tests - because good work depends on good health. 📈 Personal development - dedicated support for learning, development, and leveling up over time. 💛 Employee Assistance Programme (EAP) - confidential wellbeing support, available whenever you need it. 🚲 Bike2Work scheme and 🚆 Season ticket loan - to make getting to work easier and greener. 🚗 Octopus EV salary sacrifice - for a simpler, more sustainable way to drive electric. 🔎 Watch this space, we’re continuing to build this as we grow…   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.

Databricks

PRINCIPAL RESEARCH SCIENTIST – SCALING P-1227 ABOUT DATABRICKS AI At Databricks, we are obsessed with enabling data teams to solve the world’s toughest problems, from security threat detection to cancer drug development, by building and running the world’s best data and AI platform. The Databricks AI Research organization enables companies to develop AI models and agents using their own data, with technologies ranging from post-training open source LLMs to developing advanced multi-agent architectures. Databricks AI is committed to the belief that a company’s AI models and agents are just as valuable as any other core IP, and that high-quality AI should be available to all. ABOUT THE SCALING RESEARCH TEAM The Databricks AI Scaling team focuses on pushing the boundaries of large language model (LLM) training and inference efficiency beyond what is required to support existing models. The team explores novel avenues for scaling and efficiency improvements across algorithms, systems, and infrastructure, requiring researchers who can both drive independent research agendas and dive deep into low‑level implementation details with engineering partners. ROLE SUMMARY As a Principal Research Scientist – Scaling, you will lead a team of world‑class researchers and engineers to advance the state of the art in large‑scale machine learning, focusing on post-training, RL and inference efficiency, optimization, and scaling. You will define and execute a research roadmap that advances the Databricks AI platform and delivers tangible improvements to how customers train, serve, and adapt LLMs at scale, working closely with product, data, and engineering leaders to bring cutting‑edge methods into production. THE IMPACT YOU WILL HAVE * Lead and grow a multidisciplinary research team focused on foundational and applied AI problems, with a particular emphasis on LLM scaling, efficiency, and systems performance. * Define the scaling research roadmap in alignment with Databricks’ strategic objectives, prioritizing advances in foundation model efficiency and large‑scale training and inference. * Drive algorithmic innovations for large‑scale neural network training and inference, including novel optimizers, low‑precision techniques, and model adaptation methods, and guide your team in rigorous empirical validation against state‑of‑the‑art approaches. * Optimize end‑to‑end ML systems for distributed training and RL, memory efficiency, and compute efficiency through close collaboration with core systems and platform teams, ensuring that research ideas translate into performant, reliable infrastructure. * Partner with product and engineering to translate research breakthroughs, especially around scaling and efficiency, into customer‑impacting capabilities in the Databricks AI platform. * Foster a culture of scientific excellence and openness, including high‑quality research practices, reproducible experimentation, and effective internal knowledge sharing across Databricks AI. * Represent Databricks AI research externally through top‑tier publications, conference talks, and collaborations with academia and the open‑source community, with a focus on optimization and efficiency for large‑scale models. * Mentor and develop talent, providing both technical guidance (research agendas, experimentation, implementation) and career development support for research scientists and engineers. WHAT YOU WILL DO * Define and lead independent research programs on foundation model efficiency, covering topics such as optimizer design, low‑precision training/inference, scalable model architectures, and efficient adaptation methods. * Oversee the design and execution of large‑scale experiments, including benchmarking against state‑of‑the‑art methods and evaluating trade‑offs in quality, latency, throughput, and cost. * Work hands‑on with your team on high‑quality, efficient code in Python and PyTorch for research implementation, rapid prototyping, and integration with Databricks’ production systems. * Collaborate with distributed systems and infra teams to push the limits of distributed training, parallelism strategies, memory management, and hardware utilization for LLMs and other large models. * Establish metrics, evaluation protocols, and best practices for scaling‑focused research (e.g., training efficiency, inference cost, energy usage) and drive their adoption across Databricks AI. * Champion responsible and robust deployment of scaling innovations, ensuring that model behavior, reliability, and safety remain first‑class considerations. WHAT WE LOOK FOR * Proven ability to lead a research team to develop novel techniques for foundation model efficiency and related topics, with a strong track record of industry impact.  * Deep expertise in at least one of: generative AI, LLMs, distributed ML systems, model optimization, or responsible AI, with a strong emphasis on scaling and efficiency for large‑scale neural networks. * Hands on leadership - strong programming skills and demonstrated ability to write high‑quality, efficient code in Python and PyTorch for research implementation and experimentation. * Demonstrated ability to translate research innovation into scalable product capabilities in partnership with product and engineering teams. * Excellent communication, leadership, and stakeholder management skills, with experience influencing cross‑functional roadmaps and aligning research with business impact. NICE TO HAVE * Prior work at the intersection of systems and ML, such as distributed training frameworks, compiler and kernel optimization for deep learning workloads, or memory‑/compute‑efficient model design. * Strong industry and academic network in large‑scale ML, with ongoing collaborations or service (e.g., PC/area chair) at top conferences in ML and systems. * A strong record of research impact—such as first‑author publications at top ML/systems conferences (e.g., ICLR, ICML, NeurIPS, MLSys), influential open‑source contributions, or widely used deployed systems—especially in optimization or efficiency.   Pay Range Transparency Databricks is committed to fair and equitable compensation practices. The pay range(s) for this role is listed below and represents the expected salary range for non-commissionable roles or on-target earnings for commissionable roles.  Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to job-related skills, depth of experience, relevant certifications and training, and specific work location. Based on the factors above, Databricks anticipates utilizing the full width of the range. The total compensation package for this position may also include eligibility for annual performance bonus, equity, and the benefits listed above. For more information regarding which range your location is in visit our page here.   Local Pay Range $280,000—$350,000 USD About Databricks Databricks is the data and AI company. More than 10,000 organizations worldwide — including Comcast, Condé Nast, Grammarly, and over 50% of the Fortune 500 — rely on the Databricks Data Intelligence Platform to unify and democratize data, analytics and AI. Databricks is headquartered in San Francisco, with offices around the globe and was founded by the original creators of Lakehouse, Apache Spark™, Delta Lake and MLflow. To learn more, follow Databricks on Twitter, LinkedIn and Facebook. Benefits At Databricks, we strive to provide comprehensive benefits and perks that meet the needs of all of our employees. For specific details on the benefits offered in your region click here. Our Commitment to Diversity and Inclusion At Databricks, we are committed to fostering a diverse and inclusive culture where everyone can excel. We take great care to ensure that our hiring practices are inclusive and meet equal employment opportunity standards. Individuals looking for employment at Databricks are considered without regard to age, color, disability, ethnicity, family or marital status, gender identity or expression, language, national origin, physical and mental ability, political affiliation, race, religion, sexual orientation, socio-economic status, veteran status, and other protected characteristics. Compliance If access to export-controlled technology or source code is required for performance of job duties, it is within Employer's discretion whether to apply for a U.S. government license for such positions, and Employer may decline to proceed with an applicant on this basis alone.

H

About H: H exists to push the boundaries of superintelligence with agentic AI. By automating complex, multi-step tasks typically performed by humans, AI agents will help unlock full human potential. H is hiring the world’s best AI talent, seeking those who are dedicated as much to building safely and responsibly as to advancing disruptive agentic capabilities. We promote a mindset of openness, learning, and collaboration, where everyone has something to contribute. About the Team: The Agent team defines new learning algorithms and agent paradigms to push the frontiers of agentic systems. We build upon foundation models and reinforcement learning to develop new approaches to train artificial general agents and work closely with the LLM/VLM and Safety teams to explore new directions. This is a heavily engineering-focused role embedded within the research team. You will be responsible for defining the architecture and building the robust, scalable systems that underpin our research efforts. Your work will translate cutting-edge research concepts into high-performance, production-quality platforms, enabling the next generation of agentic AI. Key Responsibilities: * Research & Leadership: Design and develop new agents, proposing new research directions, e.g., combining state-of-the-art RL with foundation models (LLMs/VLMs). * Algorithm & Systems Design: Design, implement, and scale complex, high-performance systems for training large-scale agents. This includes both the foundational infrastructure and the novel algorithms, reward models, and sophisticated training environments. * Research-to-Production: Collaborate closely with researchers and engineers to implement, test, and productionize new agent logics, learning algorithms, and system architectures. * Evaluation & Reliability: Create, manage, and scale massive benchmarks and evaluation systems to rigorously track agent capabilities. You will own system reliability, scalability, and observability for our entire research infrastructure. * Mentorship & Standards: Mentor and guide other engineers and researchers on the team, fostering technical excellence. You will establish and enforce engineering standards, tooling, and best practices for both code and research design. * Innovation: Conduct thorough code and design reviews, champion technical innovation, and proactively address technical debt to accelerate the R&D lifecycle. Requirements: * Technical Skills: * Senior Experience: Previous demonstrable role(s) as a Staff, Principal, or Senior Engineer (or equivalent Research Scientist) in a Frontier AI Lab with a proven track record of leading complex, end-to-end AI/ML projects from conception to production. * Education / Publication: Preferably PhD (or equivalent research experience) in Machine Learning, Computer Science, or a related field, preferably with a strong publication record (e.g., NeurIPS, ICML, ICLR) in Computer Science. * Core Expertise: Deep theoretical and practical expertise in Agentic AI and proven experience building, scaling, and shipping solutions involving foundation models (LLMs/VLMs). * Soft Skills: * Collaborative: Enjoys collaboration and thrives in a teamwork-oriented, fast-paced research environment. * High-Impact Communicator: Possesses impactful communication skills, with the ability to bridge the gap between research and engineering and articulate complex ideas clearly. * Mission-Driven: Genuinely eager to explore and solve the new engineering and research challenges at the frontier of agentic AI. * Bonus Skills: * Practical experience applying Reinforcement Learning to systems built on Large Language Models (LLMs). * Experience with distributed systems or cloud computing, preferably in AWS. * Familiarity with building complex simulation environments for agent training. * Experience with LLM training or fine-tuning. * Experience developing large-scale evaluation and benchmarking systems for AI models. * Experience in an agentic framework (e.g., LangChain, AutoGen, CrewAI, OpenAI SDK). * Expertise in system architecture, instrumentation, observability, and monitoring for complex, high-performance systems. Location: * Paris or London. * This role is hybrid, and you are expected to be in the office 3 days a week on average. * Please expect some travel between offices on a reasonable cadence (e.g., every 4-6 weeks). What We Offer: * Join the exciting journey of shaping the future of AI, and be part of the early days of one of the hottest AI startups. * Collaborate with a fun, dynamic, and multicultural team, working alongside world-class AI talent in a highly collaborative environment. * Enjoy a competitive salary. * Unlock opportunities for professional growth, continuous learning, and career development. If you want to change the status quo in AI, join us.