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Funding10M-20M USD
IndustrySaaS, Web 2.0, Biotech, Software, E-commerce
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Systems Engineer

at DNAnexus in Mountain View, CA   —   Jul 11, 2014   |  
At DNAnexus we are solving the most challenging computer science problems you’re ever likely to see.

In the last few years, there has been a dramatic development in the world of genomics that has created a huge new opportunity. The price to sequence the full human genome (all of your DNA, not just a sample of it) has fallen to the point were it will soon be affordable for a patient to have multiple samples of their whole genome sequenced to help treat their disease. Want to know what specific gene mutation caused a patient’s cancer? We are building the platform to answer that kind of question. One of the many challenges is the huge amount of data. Think you’ve seen big-data problems? Think again – with each genome comprising 100 GB and months of CPU time to crunch the information, DNA is the next big-data problem, requiring exabytes of storage and parallel workloads distributed across 100,000 servers. We are tackling this by combining web technologies, big-data analytics, and scalable systems on cloud computing infrastructure.
We are a well-funded startup backed by Google Ventures, TPG Biotech, and First Round capital. Our founders, Andreas Sundquist, Arend Sidow, Serafim Batzoglou are world-renowned genomics and bioinformatics experts from Stanford University.

We are looking for smart motivated people. Ideal candidates will likely know several of the following technologies: C, C++, Boost, Ruby, Rails, HAML, HTML, CSS, JavaScript, ECMAScript, V8, jQuery, Flash, Flex/ActionScript, Node.js, Python, Perl, PHP, Amazon Web Services (AWS), SQL, MySQL, PostgreSQL, MongoDB, Solr, Sphinx, Hadoop, MapReduce, ZooKeeper, Hive, Pig, Oozie, HDFS, ZFS, MCache. We obviously do not use all of these, but if you know several of them you are our kind of candidate.

You're a hard-core systems engineer that wants to work on building core technologies for scaling up systems on 10,000 servers and Petabyte datasets.
Architect and implement frameworks for parallel compute in genomics (think: Hadoop for DNA)
Design novel data storage schemes for efficient compression, transfer, and query on genomics datasets
Build scalable compute infrastructure specifically targeted to cloud resources
Work closely with computational biologists to understand domain-specific needs
Minimum of five years relevant experience
Strong computer science background, including low-level systems and algorithmic complexity
Familiarity with cloud computing services, including Amazon EC2 and S3
Experience working with Tera/Petabyte datasets and coordinating 1000s of CPUs
Deep understanding of parallel computing architectures from SIMD, SMP, GPGPUs, to MPI and MapReduce
Administer Linux servers with ease
Fluency with C++, familiarity with other common languages such as Ruby, PHP, Python, Perl, Java, JavaScript
BS, MS or PhD in computer science or relevant technical field
Competitive base salary, stock options and health benefits.
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