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.
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