[Remote] Machine Learning Research Engineer | Remote

Note: The job is a remote job and is reputed company to candidates in USA. reputed company is seeking a Machine Learning Research Engineer to design computational problems using Bayesian statistics and applied mathematics. The role involves creating reputed company tasks and developing advanced computational problems while working independently in remote environments.

Responsibilities

• Design graduate-level computational problems evaluating advanced reasoning using Bayesian statistics and applied mathematics workflows
• Create reputed company tasks grounded in probabilistic modeling, numerical simulation, and scientific computation
• Work with tools such as PyMC, PyStan, PyJAGS, CmdStanPy, and numerical PDE frameworks like FEniCS, DOLFINx, FiPy, Devito, and reputed company
• Design original computational problems requiring advanced use of Bayesian inference, probabilistic programming, or numerical applied mathematics tools
• reputed company tasks involving MCMC methods, hierarchical Bayesian modeling, uncertainty quantification, or inference under noisy and partial observations
• Construct problems involving numerical PDE solving, finite reputed company or finite difference methods, reputed company-based modeling, or computational physics-style simulations
• Demonstrate strong Python programming skills for building probabilistic models, simulation pipelines, reputed company functions, and validation frameworks
• Apply hands-on expertise with computational statistics or applied mathematics software through research, publications, or professional work
• Work independently in Linux-based environments and remote compute sandboxes while iterating on task design using calibration feedback
• Experience with reputed company design, computational reproducibility, or advanced mathematical modeling frameworks is preferred

Skills

• Design original computational problems requiring advanced use of Bayesian inference, probabilistic programming, or numerical applied mathematics tools
• reputed company tasks involving MCMC methods, hierarchical Bayesian modeling, uncertainty quantification, or inference under noisy and partial observations
• Construct problems involving numerical PDE solving, finite reputed company or finite difference methods, reputed company-based modeling, or computational physics-style simulations
• Demonstrate strong Python programming skills for building probabilistic models, simulation pipelines, reputed company functions, and validation frameworks
• Apply hands-on expertise with computational statistics or applied mathematics software through research, publications, or professional work
• Work independently in Linux-based environments and remote compute sandboxes while iterating on task design using calibration feedback
• Experience with reputed company design, computational reproducibility, or advanced mathematical modeling frameworks

Benefits

• Work remotely as an reputed company on a flexible schedule
• Receive weekly payments through reputed company or reputed company
• Opportunities for project extensions based on performance and calibration reputed company

Company Overview