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Machine Learning and Signal Processing Engineer

Spire Health

This is a Full-time position in Solon, MI posted April 2, 2020.

Company Background Spire Health is the market leader in remote patient monitoring (RPM) for respiratory diseases.

Our mission is to harness the power of algorithms and sensors to improve health outcomes by empowering patients and physicians with actionable data.

We currently work with partners across a range of health conditions, including chronic obstructive pulmonary disease (COPD), congestive heart failure, asthma, sleep disorders and anxiety.

Spire Health runs its own ecosystem of hardware, firmware, and software to gather patient respiratory data and process it in a distributed computing platform running modern day machine-learning algorithms.

Spire Health s platform processes billions of data points every single day and surfaces countless numbers of insights for care providers to act on.

Founded in 2013 and headquartered in San Francisco, Spire Health is growing rapidly and expanding its team across the nation.

New offices in New York and Traverse City, Michigan, have opened new doors and new opportunities to join the Spire Health team and help the world live better by breathing better.

Job Summary As Machine Learning and Signal Processing Engineer you are going to lead the effort to bring signal processing algorithms into production which condition and extract rich morphological features from our unique respiratory sensor.

In addition, you will bring machine learning models, which predict changes in a patient’s disease state, into production for both streaming and batch mode use cases.

You will collaborate closely with the research and data science teams and become the expert on tweaking, optimizing, deploying, and monitoring these algorithms in a commercial environment.

Our Tech Stack Dashboards Ruby on Rails, Redis, PostgresQL Backend ASGI Python on AWS Lambda Data pipeline Kinesis, Spark Spark Streaming Data Warehousing Snowflake, Parquet on S3 Monitoring, APM, and Metrics with Datadog Duties Lead the optimization, implementation, and validation of DSP algorithms and ML models for streaming physiological time series data Collaborate closely with the data science team on the design and validation of models Manage the build, test, and deploy pipelines Monitor and understand application performance and behavior in Production Participate in story-writing, estimation, and sprint-planning Gather and apply context and knowledge of our entire systembecome an expert Qualifications Experience with physiological sensors including PPG, Accelerometers, and force sensors Machine learning and modeling using scikit-learn, tensorflow, keras, pytorch, etc Proficient in Python for production applications testing (pytest) Familiarity with Spark (Batch, Streaming, ML) using Python or Scala Brings deep expertise in linear filtering (FIR, IIR, etc), frequency analysis (DFT, wavelets, etc), properties of discrete-time signals (aliasing, z-transform, etc), adaptive filtering (Kalman, LMS, etc), handling and processing of data (buffering, latency, jitter)