Backend Software Engineer, New Grad

Description

We have an immediate opening for a recent college graduate with an affinity for backend development and an interest in machine learning to join a growing team of developers, researchers, and medical professionals.

The ideal candidate is excited by the idea of developing and architecting solutions in a variety of different environments and problem domains and is interested in being an essential part of a team.

Responsibilities

  • Developing and architecting extensible, robust, and efficient backend solutions with a small team of developers.

  • Rapidly learning and adapting to new programming languages, technologies, and projects.

  • This is primarily a software engineering role (80% of your time). You think in terms of micro services and APIs and will work towards building out new backend features and evolving our codebase towards improved reliability and observability.

  • This role also requires a strong systems engineering background (e.g. bash scripting, Unix-based systems, troubleshooting network/database performance issues, logging, init systems, chroot/containers; 20% of your time).

  • You will ultimately own the backend and infrastructure of our systems-- identify and resolve performance bottlenecks, design and build out new features and services, automate provisioning of new micro services.

Must Haves

  • You must have experience with software development in Python (i.e. beyond just scripting and data analysis/visualization using Python).

  • You must have experience working in a Linux environment (e.g. bash scripting, init and logging systems, configuring web and database services).

  • You should have experience with web applications, databases, Git in a team environment, monitoring/logging systems.

  • You should have a passion for continued learning to improve your skills and those around you.

Extra Credit

  • Experience with C++11/C++14/C++17.

  • Experience in a JVM language.

  • Experience with writing multi-threading software.

  • Electronics and hardware tinkering experience.

  • Embedded systems experience (deployment into low resource and offline environments, interaction with hardware through code, signal processing of analog and digital signals).

  • Android filesystems experience (e.g. Android shell, adb).

Research Engineer: Signal Processing, Machine Learning, EEG Analysis

The position involves developing machine learning algorithms for innovative biomedical technologies involving physiological signal processing. The successful candidate will work closely with a team of physicians, nurses, engineers, and scientists in designing new medical technologies for the intensive care unit.

Candidates with experience in the analysis of experimental data derived from---but not limited to---auditory/visual/cross-sensory psychophysical, EEG, ECG, and galvanic-skin conductance, data would be given a higher priority.

Minimum Requirements:

  • MS in electrical engineering or biomedical engineering or a similar discipline.

  • Expertise and innovation in methods, theory, and application of machine learning and data mining with a broad understanding of methodological approaches and proficiency in practice.

  • Expert abilities to work with new data sets regardless of prior exposure to current topic.

  • Strong interest in research and learning new technologies.

  • Experience with Python.

Responsibilities

  • Collaborate with a team of machine learning, control engineering, and signal processing researchers.

  • Provide technical expertise to address supervised and unsupervised learning problems in an applied research environment.

  • Develop and deploy modern machine learning and statistical methods for finding patterns and models from physiological data.

Preferred Qualifications:

  • Prior expertise and exposure using non-invasive human physiological measures such as EEG, ECG, galvanic-skin conductance, or other categorically similar methodologies.

  • Prior experience in feature extraction from physiological signals.

  • Experience working with quantitative methods of neural data analysis.