Sr. Systems Engineer (Distributed Machine Learning)
- 901 N Glebe Rd, Arlington, VA 22203, USA
When extreme data requires companies to act with unprecedented agility, Kinetica powers business in motion. Kinetica is the instant insight engine for the Extreme Data Economy. Across healthcare, energy, telecommunications, retail, and financial services, enterprises utilizing new technologies like connected devices, wearables, mobility, robotics, and more can leverage Kinetica for machine learning, deep learning, and advanced location-based analytics that are powering new services. Kinetica’s accelerated parallel computing brings thousands of GPU cores to address the unpredictability and complexity that result from extreme data.
For more information and trial downloads, visit kinetica.com or follow us on LinkedIn and Twitter.
We are seeking a Senior Systems Engineer with Python and Distributed Systems experience. to join our accomplished team to help build a new product line for our company.
Our team of engineers is building out a scalable, distributed machine learning and data science platform with tight integrations and pipelines to a distributed, sharded GPU-powered database. This means the product would need to be developed in Linux and operate inside containers (Docker for us), work in a container-orchestrated environment (Kubernetes for us), operate in a scalable resource managed system (GPUs via Kubernetes) and interact with complex analytical systems (TensorFlow, etc.)
Day to day responsibilities:
- Integrate a variety of components into an overall smooth-functioning product
- Ability to bring things to a close -- not just exploring but getting things to the finish line.
- Research products and keep abreast of marketplace offerings and possibilities
- Work with commercial and open source packages to find stacks to achieve required product features
- Work with a close-knit team to design and develop a release-quality commercial product
- Work with our broader engineering group to ensure products fit into the company’s product lineup
- Work iteratively to hone proofs-of-concept for new product features and steadily merge development into the overall product
- Keep attuned to the marketplace and spot opportunities to expand functionality in response to new technical capabilities as they arise
- Keep attuned to customer use and actively work to improve product experience to meet usage, both current and future usage the customer may not even realize they need
- Bachelors in Computer Science, Operations Research, Statistics, Math, Physics, or equivalent
- Solid written and verbal communication skills
- Proficiency with Python development in a Linux environments
- Familiarity with SQL and databases
- Familiarity with containerized Python applications (Docker specifically)
- Familiarity with Container Orchestration (Kubernetes specifically), ideally via Python bindings
- A desire to work with highly analytical systems and work with large datasets
- Exposure to one machine learning open source package (sklearn, TensorFlow, Caffe2, Torch, etc.) The more experience the better. Relevant portfolios a big plus.
- Graduate degree in Computer Science, Data Science, ORIE, Statistics, Math, Physics, or equivalent
- Experience (7+ years) at high-tech startup, technology/data science consultancy with data science tools, dev-ops tools; Academic experience is a valid substitute (e.g., Ph.D., Fellowship)
- Strong communications skills as demonstrated by personal projects, technical blog postings, volunteer activities, etc.
- Interest in Machine Learning and Data Science
- Excitement about a small company with close team interactions and a fast-moving culture
- Familiarity with popular python libraries (numpy)
- Understanding of the data science ecosystem -- commercial and open source
- Openness to roll-with-the-punches, as required for highly competitive markets with competitive landscapes that require constant product and features enhancements
- Experience at high-tech startup, technology/data science consultancy, or tech-intensive org
- An active participant in the technology community (e.g., Hackathons, StackOverflow, Kaggle, open source contributions/projects)
- Experience working with highly complex technical ecosystems (resource managers, containers, automated testing -- e.g., Mesos, Kubernetes, Docker)
- (automated builds, continuous integration, automated testing, containers -- e.g, Git, Jenkins)
- Experience working with computational systems (e.g., NumPy, Pandas, Spark, etc.)
All your information will be kept confidential according to EEO guidelines.