21 of 21 Probabilistic Modelling Jobs

Hands-On Development Define and implement end-to-end AI pipelines: data collection/cleaning, feature engineering, model training, validation, and inference. Rapidly prototype novel models (eg, neural networks, probabilistic models) using PyTorch, TensorFlow, JAX, or equivalent. Productionize models in cloud/on-prem environments (AWS/GCP/Azure) with containerization (Docker/Kubernetes) and ensure low-latency More ❯

Hands-On Development • Define and implement end-to-end AI pipelines: data collection/cleaning, feature engineering, model training, validation, and inference. • Rapidly prototype novel models (e.g., neural networks, probabilistic models) using PyTorch, TensorFlow, JAX, or equivalent. • Productionize models in cloud/on-prem environments (AWS/GCP/Azure) with containerization (Docker/Kubernetes) and ensure low-latency More ❯

Hands-On Development Define and implement end-to-end AI pipelines: data collection/cleaning, feature engineering, model training, validation, and inference. Rapidly prototype novel models (e.g., neural networks, probabilistic models) using PyTorch, TensorFlow, JAX, or equivalent. Productionize models in cloud/on-prem environments (AWS/GCP/Azure) with containerization (Docker/Kubernetes) and ensure low-latency More ❯

science and ML engineering. Requirements: ML Expertise: 5+ years of experience in machine learning, data science, or applied AI; strong grasp of supervised/unsupervised learning, deep learning, and probabilistic modeling. NLP & LLM Skills: Hands-on experience with NLP pipelines, vector stores, transformer models, and generative AI architectures. Production Experience: Demonstrated success in shipping and maintaining ML models in More ❯

science and ML engineering. Requirements: ML Expertise: 5+ years of experience in machine learning, data science, or applied AI; strong grasp of supervised/unsupervised learning, deep learning, and probabilistic modeling. NLP & LLM Skills: Hands-on experience with NLP pipelines, vector stores, transformer models, and generative AI architectures. Production Experience: Demonstrated success in shipping and maintaining ML models in More ❯

daily directed guidance stands to redefine primary care while helping people live happier, healthier, and longer. Job Summary: We're seeking a Bayesian Data Scientist with deep expertise in probabilistic modeling and a strong grasp of modern AI advancements, including foundation models , generative AI , and variational inference . This role is perfect for someone who thrives on solving complex … driving real business impact. Location: Remote/Hybrid/USA-SF, USA-remote, UK-London, UK-remote Responsibilities: Translate predictive modeling problems and business constraints into robust Bayesian or probabilistic AI solutions. Design and implement reusable libraries of predictive features and probabilistic representations for diverse ML tasks. Build and optimize tools for scalable probabilistic inference under memory … learning analyses, simulations, and experimental design. Stay current with emerging trends in generative modeling, causality, uncertainty quantification, and responsible AI. Requirements/Qualifications: Strong experience in Bayesian inference and probabilistic modeling : PGMs, HMMs, GPs, MCMC, variational methods, EM algorithms, etc. Proficiency in Python (must) and familiarity with PyMC, NumPyro, TensorFlow Probability , or similar probabilistic programming tools. Hands-on More ❯

ML frameworks (e.g. PyTorch, TensorFlow, scikit-learn) Expertise in one or more of the following areas : o Multi-modal data fusion or time series analysis o Uncertainty quantification or probabilistic modelling o Active learning, explainable AI, or online learning o Geospatial data analysis or sensor-based modelling • Experience preparing technical deliverables and engaging with stakeholders Why Apply More ❯

experience with ML frameworks (e.g. PyTorch, TensorFlow, scikit-learn) Expertise in one or more of the following areas: Multi-modal data fusion or time series analysis Uncertainty quantification or probabilistic modelling Active learning, explainable AI, or online learning Geospatial data analysis or sensor-based modelling Experience preparing technical deliverables and engaging with stakeholders Eligible for SC Clearance More ❯

experience with ML frameworks (e.g. PyTorch, TensorFlow, scikit-learn) Expertise in one or more of the following areas: Multi-modal data fusion or time series analysis Uncertainty quantification or probabilistic modelling Active learning, explainable AI, or online learning Geospatial data analysis or sensor-based modelling Experience preparing technical deliverables and engaging with stakeholders Eligible for SC Clearance More ❯

are on the lookout for a AI & Data Science specialist to support one of our market leading clients. The ideal candidate will have the following experience – Data Science & ML : Probabilistic modeling, optimization, machine learning. Frameworks : Python, scikit-learn, pandas, PyTorch. Generative AI : Experience with LLMs, Semantic Kernel, or LangChain. AI Engineering : Designing and deploying scalable AI solutions. Cloud : Azure More ❯

skills in Python (NumPy, PyTorch or TensorFlow, SciPy, pandas). In-depth knowledge of machine learning for time-series, including RNNs, LSTMs, GRUs, transformers, attention mechanisms. Solid understanding of probabilistic models (HMMs, Bayesian inference, graphical models). Experience designing or adapting dynamic programming algorithms. A graduate degree (PhD or MSc) in Computer Science, Mathematics, Physics, Bioinformatics, or a related More ❯

regularly open-sourcing the code of our novel discoveries. Key Responsibilities: Develop high-impact research output in the field of reinforcement learning, Bayesian optimization, game theory, multi-agent learning, probabilistic modeling, and/or risk-averse learning Write research-level code capable of testing novel ideas and new approaches Effectively communicate research findings to the team and to the … submissions in any of the following: ICML, NeurIPS, AISTATS, AAAI, UAI, IJCAI, JMLR, Annals of Statistics, and Annals of Probability Hands-on Experience in implementing reinforcement learning, Bayesian optimisation, probabilistic modeling, and/or multi-agent algorithms Knowledge of Python, specifically PyTorch or TensorFlow in addition to OpenAI Gym, GPFlow, Pyro, among others Ability to work in a diverse More ❯

to run semi-technical interviews, assessing how candidates apply knowledge, explain ideas, and contribute in practice. Bonus: Experience hiring across other technical roles, including data scientists (especially those using probabilistic models), data engineers, product managers, and front-end or full-stack developers. Ability to test new sourcing tools and methods, and adapt based on evidence and feedback. Confidence using More ❯

to run semi-technical interviews, assessing how candidates apply knowledge, explain ideas, and contribute in practice. Bonus: Experience hiring across other technical roles, including data scientists (especially those using probabilistic models), data engineers, product managers, and front-end or full-stack developers. Ability to test new sourcing tools and methods, and adapt based on evidence and feedback. Confidence using More ❯

to run semi-technical interviews, assessing how candidates apply knowledge, explain ideas, and contribute in practice. Bonus: Experience hiring across other technical roles, including data scientists (especially those using probabilistic models), data engineers, product managers, and front-end or full-stack developers. Ability to test new sourcing tools and methods, and adapt based on evidence and feedback. Confidence using More ❯

Positions are available at all stages; we seek to fill most positions now but leave some for future years as well: - Research Fellow - Postdoc - PhD Student The work involves probabilistic modelling in exciting new settings, and developing new methods for probabilistic machine learning and inference. Applicants with outstandingly strong expertise in one of following topics are welcome … or strong expertise in one and keen interest in working with expert colleagues on the others: automatic experimental design, Bayesian inference, human-in-the-loop learning, advanced user modelling, machine teaching, privacy-preserving learning, reinforcement learning, inverse reinforcement learning, simulator-based inference, likelihood-free inference. There will be particularly good opportunities to join new work on collaborative modelling More ❯

other participants of the "AI Hub in Generative Models" (UCL , Oxford, Imperial, Edinburgh, Cardiff, Manchester and Surrey) and with its industrial partners. Key responsibilities include working on deep learning, probabilistic modelling, deep generative modelling, and graph neural networks. Additional responsibilities include developing research objectives and proposals; presentations and publications; assisting with teaching; liaising and networking with colleagues More ❯

a team with access to cutting-edge multiomic and interventional datasets, advanced computational infrastructure, and deep interdisciplinary expertise. This is an opportunity to push the boundaries of what causal modelling can achieve in complex, high-dimensional, and noisy real-world systems, and to see your work tested directly in experimental biology. Your Responsibilities Collaborate with domain experts to translate … biological hypotheses into formal causal modelling problems. Design and implement causal learning approaches that capture regulatory logic, cell fate trajectories, and intervention effects from diverse biological data, including single-cell perturbation experiments. Develop models that go beyond correlation, focusing on generalisation, counterfactual prediction, and experimental design. Collaborate with experimental teams to design and validate computational hypotheses via iterative strategies … science or a related quantitative field. Deep expertise in causal inference, such as causal graphical models, counterfactual reasoning, or invariant representation learning. Strong background in one or more of probabilistic modelling, time series analysis, or dynamical systems. Proficiency in Python and familiarity with scalable ML tooling and high-performance computing. Desirable Knowledge Or Experiences Familiarity with biological datasets More ❯

a team with access to cutting-edge multiomic and interventional datasets, advanced computational infrastructure, and deep interdisciplinary expertise. This is an opportunity to push the boundaries of what causal modelling can achieve in complex, high-dimensional, and noisy real-world systems, and to see your work tested directly in experimental biology. Your responsibilities Collaborate with domain experts to translate … biological hypotheses into formal causal modelling problems. Design and implement causal learning approaches that capture regulatory logic, cell fate trajectories, and intervention effects from diverse biological data, including single-cell perturbation experiments. Develop models that go beyond correlation, focusing on generalisation, counterfactual prediction, and experimental design. Collaborate with experimental teams to design and validate computational hypotheses via iterative strategies … science or a related quantitative field. Deep expertise in causal inference, such as causal graphical models, counterfactual reasoning, or invariant representation learning. Strong background in one or more of probabilistic modelling, time series analysis, or dynamical systems. Proficiency in Python and familiarity with scalable ML tooling and high-performance computing. Desirable knowledge or experiences Familiarity with biological datasets More ❯

for designing, developing, and optimizing high-performance, low-latency trading infrastructure and quantitative models within a hedgefund in London. This role focuses on implementing models related to Stochastic Processes & Probabilistic Modeling, ensuring their efficient execution within a real-time trading environment. The developer will work closely with quantitative researchers and traders to translate mathematical concepts into robust and highly … Problem-Solving: Strong analytical and problem-solving skills with attention to detail. Preferred Qualifications Distributed Systems: Knowledge of distributed system architectures and messaging frameworks. Familiarity with Stochastic Processes and Probabilistic Modeling. More ❯

for designing, developing, and optimizing high-performance, low-latency trading infrastructure and quantitative models within a hedgefund in London. This role focuses on implementing models related to Stochastic Processes & Probabilistic Modeling, ensuring their efficient execution within a real-time trading environment. The developer will work closely with quantitative researchers and traders to translate mathematical concepts into robust and highly … Problem-Solving: Strong analytical and problem-solving skills with attention to detail. Preferred Qualifications Distributed Systems: Knowledge of distributed system architectures and messaging frameworks. Familiarity with Stochastic Processes and Probabilistic Modeling. More ❯