all.health
all.health is at the forefront of revolutionizing healthcare for millions of patients worldwide. Combining more than 20 years of proprietary wearable technology with clinically relevant signals, all.health connects patients and physicians like never before with continuous, data-driven dialogue. This unique position of 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 modeling challenges, optimizing predictions under uncertainty, and developing interpretable, high-impact models in real-world systems. You will apply state-of-the-art techniques from Bayesian statistics and modern machine learning to build scalable, efficient, and insightful models-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, latency, and compute constraints. Apply and innovate on methods like Bayesian neural networks, variational autoencoders, diffusion models, and Gaussian processes for modern AI use cases. Collaborate closely with product, engineering, and business teams to build end-to-end modeling solutions. Conduct deep-dive statistical and machine 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 experience with classical ML and modern techniques, including deep learning, transformers, diffusion models, and ensemble methods. Solid understanding of feature engineering, dimensionality reduction, model construction, validation, and calibration. Experience with uncertainty quantification and performance estimation (e.g., cross-validation, bootstrapping, Bayesian credible intervals). Familiarity with database and data processing tools (e.g., SQL, MongoDB, Spark, Pandas). Ability to translate ambiguous business problems into structured, measurable, and data-driven approaches. Preferred Qualifications: M.Sc or PhD in Statistics, Electrical Engineering, Computer Science, Physics, or a related field. Background in generative modeling, Bayesian deep learning, signal/image processing, or graph models. Experience applying probabilistic models in real-world applications (e.g., recommendation systems, anomaly detection, personalized healthcare, etc.). Understanding of modern ML pipelines and MLOps (e.g., MLFlow, Weights & Biases). Experience with recent trends such as foundation models, causal inference, or RL with uncertainty. Track record of publishing or presenting work (e.g., NeurIPS, ICML, AISTATS, etc.) is a plus. What we are looking for: Curiosity-driven and research-oriented mindset, with a pragmatic approach to real-world constraints. Strong problem-solving skills, especially under uncertainty. Comfortable working independently and collaboratively across cross-functional teams. Eagerness to stay up to date with the fast-moving AI ecosystem. Excellent communication skills to articulate complex technical ideas to diverse audiences. The successful candidate's starting pay will be determined based on job-related skills, experience, qualifications, work location, and market conditions. These ranges may be modified in the future.
all.health is at the forefront of revolutionizing healthcare for millions of patients worldwide. Combining more than 20 years of proprietary wearable technology with clinically relevant signals, all.health connects patients and physicians like never before with continuous, data-driven dialogue. This unique position of 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 modeling challenges, optimizing predictions under uncertainty, and developing interpretable, high-impact models in real-world systems. You will apply state-of-the-art techniques from Bayesian statistics and modern machine learning to build scalable, efficient, and insightful models-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, latency, and compute constraints. Apply and innovate on methods like Bayesian neural networks, variational autoencoders, diffusion models, and Gaussian processes for modern AI use cases. Collaborate closely with product, engineering, and business teams to build end-to-end modeling solutions. Conduct deep-dive statistical and machine 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 experience with classical ML and modern techniques, including deep learning, transformers, diffusion models, and ensemble methods. Solid understanding of feature engineering, dimensionality reduction, model construction, validation, and calibration. Experience with uncertainty quantification and performance estimation (e.g., cross-validation, bootstrapping, Bayesian credible intervals). Familiarity with database and data processing tools (e.g., SQL, MongoDB, Spark, Pandas). Ability to translate ambiguous business problems into structured, measurable, and data-driven approaches. Preferred Qualifications: M.Sc or PhD in Statistics, Electrical Engineering, Computer Science, Physics, or a related field. Background in generative modeling, Bayesian deep learning, signal/image processing, or graph models. Experience applying probabilistic models in real-world applications (e.g., recommendation systems, anomaly detection, personalized healthcare, etc.). Understanding of modern ML pipelines and MLOps (e.g., MLFlow, Weights & Biases). Experience with recent trends such as foundation models, causal inference, or RL with uncertainty. Track record of publishing or presenting work (e.g., NeurIPS, ICML, AISTATS, etc.) is a plus. What we are looking for: Curiosity-driven and research-oriented mindset, with a pragmatic approach to real-world constraints. Strong problem-solving skills, especially under uncertainty. Comfortable working independently and collaboratively across cross-functional teams. Eagerness to stay up to date with the fast-moving AI ecosystem. Excellent communication skills to articulate complex technical ideas to diverse audiences. The successful candidate's starting pay will be determined based on job-related skills, experience, qualifications, work location, and market conditions. These ranges may be modified in the future.
all.health
all.health is at the forefront of revolutionizing healthcare for millions of patients worldwide. Combining more than 20 years of proprietary wearable technology with clinically relevant signals, all.health connects patients and physicians like never before with continuous, data-driven dialogue. This unique position of daily directed guidance stands to redefine primary care while helping people live happier, healthier, and longer. Job Summary: We're looking for a Machine Learning Engineer with a passion for developing impactful healthcare solutions using wearable data. You'll play a key role in building real-time, FDA-compliant algorithms that analyze continuous physiological signals from wearables. This is a high-impact role with the opportunity to shape the future of digital health and help bring clinically validated, regulatory-ready ML solutions to market. Location: Remote / Hybrid / USA-SF, USA-remote, UK-London, UK-remote Responsibilities: Design and implement machine learning models for real-time analysis of wearable biosignal data (e.g., ECG, PPG, accelerometer). Develop algorithms that meet clinical-grade performance standards for use in regulated environments. Preprocess and manage large-scale, continuous time-series datasets from wearable sensors. Collaborate with clinical, product, and regulatory teams to ensure solutions align with FDA, SaMD, and GMLP requirements. Optimize algorithms for deployment on resource-constrained devices (e.g., edge, mobile, embedded systems). Run thorough validation experiments including performance metrics like sensitivity, specificity, ROC-AUC, and precision-recall. Contribute to technical documentation and regulatory submissions for medical-grade software. Requirements/Qualifications: MS or PhD in Machine Learning, Biomedical Engineering, Computer Science, or a related field. 3-5+ years of experience applying machine learning to time-series or physiological data. Strong foundation in signal processing and time-series modeling (e.g., deep learning, classical ML, anomaly detection). Proficient in Python and ML frameworks such as PyTorch or TensorFlow. Familiarity with FDA regulatory pathways for medical software (e.g., 510(k), De Novo), and standards like IEC 62304 or ISO 13485. Experience with MLOps practices and model versioning in compliant environments. Preferred Qualifications: Experience building ML models with wearable data (e.g., continuous heart rate, motion, respiration). Exposure to embedded AI or edge model deployment (e.g., TensorFlow Lite, Core ML, ONNX). Knowledge of healthcare data privacy and security (e.g., HIPAA, GDPR). Familiarity with GMLP (Good Machine Learning Practice) and clinical evaluation frameworks. The successful candidate's starting pay will be determined based on job-related skills, experience, qualifications, work location, and market conditions. These ranges may be modified in the future.
all.health is at the forefront of revolutionizing healthcare for millions of patients worldwide. Combining more than 20 years of proprietary wearable technology with clinically relevant signals, all.health connects patients and physicians like never before with continuous, data-driven dialogue. This unique position of daily directed guidance stands to redefine primary care while helping people live happier, healthier, and longer. Job Summary: We're looking for a Machine Learning Engineer with a passion for developing impactful healthcare solutions using wearable data. You'll play a key role in building real-time, FDA-compliant algorithms that analyze continuous physiological signals from wearables. This is a high-impact role with the opportunity to shape the future of digital health and help bring clinically validated, regulatory-ready ML solutions to market. Location: Remote / Hybrid / USA-SF, USA-remote, UK-London, UK-remote Responsibilities: Design and implement machine learning models for real-time analysis of wearable biosignal data (e.g., ECG, PPG, accelerometer). Develop algorithms that meet clinical-grade performance standards for use in regulated environments. Preprocess and manage large-scale, continuous time-series datasets from wearable sensors. Collaborate with clinical, product, and regulatory teams to ensure solutions align with FDA, SaMD, and GMLP requirements. Optimize algorithms for deployment on resource-constrained devices (e.g., edge, mobile, embedded systems). Run thorough validation experiments including performance metrics like sensitivity, specificity, ROC-AUC, and precision-recall. Contribute to technical documentation and regulatory submissions for medical-grade software. Requirements/Qualifications: MS or PhD in Machine Learning, Biomedical Engineering, Computer Science, or a related field. 3-5+ years of experience applying machine learning to time-series or physiological data. Strong foundation in signal processing and time-series modeling (e.g., deep learning, classical ML, anomaly detection). Proficient in Python and ML frameworks such as PyTorch or TensorFlow. Familiarity with FDA regulatory pathways for medical software (e.g., 510(k), De Novo), and standards like IEC 62304 or ISO 13485. Experience with MLOps practices and model versioning in compliant environments. Preferred Qualifications: Experience building ML models with wearable data (e.g., continuous heart rate, motion, respiration). Exposure to embedded AI or edge model deployment (e.g., TensorFlow Lite, Core ML, ONNX). Knowledge of healthcare data privacy and security (e.g., HIPAA, GDPR). Familiarity with GMLP (Good Machine Learning Practice) and clinical evaluation frameworks. The successful candidate's starting pay will be determined based on job-related skills, experience, qualifications, work location, and market conditions. These ranges may be modified in the future.