Parallel Scientific, Inc. is a Boulder, CO based, funded startup company with a presence around the globe, working in the cutting edge area of scalable parallelization. We seek experienced Haskell programmers to build a sophisticated, ultra-scalable high availability framework to be used in conjunction with scientific applications, big data applications and I/O systems.

Successful candidates will work in a modern, virtual environment, with a distributed international team. All candidates are welcome to apply no matter their location. However, for this particular position, preference may be given to someone residing in or willing to relocate to the UK. Responsibilities include design, implementation in Haskell and in C, testing, infrastructure development, and optionally formal verification. We provide training in advanced processes for software design and implementation, as well as domain specific knowledge.

Required skills:

Very strong background in computer science or mathematics.

Experience with Haskell, or network and cluster programming in other functional languages (e.g. Erlang, OCaml, Clojure).

Knowledge of systems programming and operating systems.

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

Experience in distributed and/or parallel programming.

Knowledge of sophisticated distributed algorithms (fault tolerance, consensus, distributed transactions, etc).

Experience with delivering quality products on time and within budget.

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

About Parallel Scientific

Parallel Scientific was founded by Dr Peter Braam in 2010. Peter formerly taught mathematics and computer science at Oxford and Carnegie Mellon. He then 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 a very experienced management team and board. Our expertise is in the areas of distributed scientific parallel computing, compilers, domain specific languages, scalable computing for cloud and HPC, and the synthesis of hardware appliances.