Parallel Haskell Programmers

Parallel Scientific, LLC is a Boulder, CO based early stage, but funded startup company working in the area of scalable parallelization for scientific and large data computing.  We are implementing radically new software tools for the creation and optimization of parallel programs benefiting applications and leveraging modern systems architecture. We build on our mathematical knowledge, cutting edge programming languages and our understanding of systems software and hardware.  We are currently working with the Haskell development team and major HPC laboratories world wide on libraries and compiler extensions for parallel programming.

Parallel Scientific was founded by Peter Braam in 2010.  Peter formerly taught mathematics and computer science at Oxford and Carnegie Mellon.  Then he contributed file systems to Linux and invented Lustre (which provides storage to 9 of the top 10 systems in the world). He ran several successful startups, and Parallel Scientific is run by an very experienced management team and board.    

Successful candidates can in some cases work remotely and will work in a modern virtual environment.  We provide training in advanced processes for software design and implementation and domain specific knowledge.

Required skills:

• Very strong background in computer science or mathematics
• Experience with Haskell
• Knowledge of systems programming and operating systems functionality
• Knowledge of system architectures, such as high performance networking, memory architectures, multi and manycore CPUs and GPGPUs
• Experience with performance tuning of parallel or concurrent algorithms
• Experience designing and implementing concurrent or parallel programs

Experience in one or more of the following areas is desirable:

• Experience with Haskell compiler technology
• In depth knowledge of core Haskell libraries for parallel programming (NDP, REPA etc)
• Experience in the area of middleware algorithms for data flow programming, graphs, cloud based data analytics, or sparse matrices
• Domain specific knowledge in scientific programming areas related to irregular and sparse problems, e.g. data analytics using graph analysis, genomics, tightly connected numerical analysis
• Experience with performance tuning for parallel applications on multi core systems (e.g. with the Intel toolkit), for GPGPU's using Cuda/OpenCL or with MPI/OpenMP on clusters of SMPs

To apply, please send a resume to jobs@parsci.com.