Machine Learning Research Engineer | Remote

STEM Sync AI is seeking a Machine Learning Research Engineer to design computational problems using Bayesian statistics and applied mathematics. The role involves creating benchmark 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 benchmark 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 Dedalus
• Design original computational problems requiring advanced use of Bayesian inference, probabilistic programming, or numerical applied mathematics tools
• Develop tasks involving MCMC methods, hierarchical Bayesian modeling, uncertainty quantification, or inference under noisy and partial observations
• Construct problems involving numerical PDE solving, finite element or finite difference methods, mesh-based modeling, or computational physics-style simulations
• Demonstrate strong Python programming skills for building probabilistic models, simulation pipelines, oracle 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 benchmark design, computational reproducibility, or advanced mathematical modeling frameworks is preferred

Requirements:

• Design original computational problems requiring advanced use of Bayesian inference, probabilistic programming, or numerical applied mathematics tools
• Develop tasks involving MCMC methods, hierarchical Bayesian modeling, uncertainty quantification, or inference under noisy and partial observations
• Construct problems involving numerical PDE solving, finite element or finite difference methods, mesh-based modeling, or computational physics-style simulations
• Demonstrate strong Python programming skills for building probabilistic models, simulation pipelines, oracle 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 benchmark design, computational reproducibility, or advanced mathematical modeling frameworks