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Machine Learning Operations Engineer
- Ann Arbor, Michigan, United States
- Ann Arbor, Michigan, United States
About
Sintela is a deep tech company that specializes in Distributed Acoustic Sensing (DAS). DAS is a technology that can transform an ordinary hundred-mile-long optical fiber into a hundred thousand acoustic sensors—equivalent to microphones, accelerometers, and geophones— that are distributed every few feet along the entire fiber length. DAS enables real-time, large-scale monitoring of critical infrastructure, the natural environment, and the energy and transportation sectors to name a few. Sintela is the global leader in DAS, serving clients across the world. Applications include border security, perimeter intrusion detection, and monitoring of oil and gas pipelines, mining, roads, and rail. Among our most significant applications is the use of DAS to detect illegal activity along US borders. Central to Sintela’s success is the Autonomous Detection Group, which develops and maintains machine learning and signal processing algorithms for DAS. Sintela is recruiting a Machine Learning Operations Engineer to join the Autonomous Detection Group.
What You will Do
As a Machine Learning Operations Engineer, your role will be to build and maintain tools that streamline existing and new machine learning and signal processing workflows. You will enable the Autonomous Detection Group to efficiently and at pace develop and maintain algorithms for DAS. Specifically, you will:
Build and maintain basic Docker and Podman containerization and orchestration.
Build and maintain basic Linux based servers.
Maintain and refactor machine learning codebases.
Build and maintain experiment and model tracking tools for machine learning.
Build and maintain model and data repositories for machine learning and signal processing.
Build and maintain proprietary tools to evaluate machine learning and signal processing algorithm performance.
Interact across teams with database, software, and networking engineers.
Minimum Requirements
Have an existing or can obtain and maintain a security clearance with the Department of Homeland Security
Undergraduate degree in computer science, electrical engineering, physics, or similar and 2 years of experience.
A strong foundation in – and passion for – machine learning.
Experience with MLOps tools (e.g., MLflow, Docker, Kubeflow, Airflow, Kubernetes).
Excellent programming skills with Python and associated ML libraries.
Experience with software version control tools such as Gitlab.
Technical documentation experience.
Will work well individually and in collaboration with an international (primarily US-UK) team.
Demonstrate integrity as well as physical and cyber security consciousness.
Experience with Linux systems.
Other Competencies of Interest
Knowledge and skills in the following domain areas are additionally of interest:
Go programming language.
Cuda programming.
PostrgreSQL.
Data management / data science.
Signal processing.
Distributed Acoustic Sensing.
Experience with cloud platforms (AWS, GCP, Azure).
Data fusion.
Physics and mathematics.
Seismology.
Conventional image processing (e.g. shape detection).
Benefits
Enjoy working as part of an international (primarily US-UK), multi-disciplinary team of scientists/engineers in a friendly, informal and fast-paced development environment delivering robust Autonomous Signature Classification workflows.
Hone your expert skills and experience the satisfaction of pitting them against a range of temporally, spatially and spectrally diverse signatures.
Witness the product of your efforts transition rapidly from concept to operational deployment and delivery of real-world effect, often thereby directly contributing to the prevention of illegal activity.
Employment Conditions
Tight collaboration and the sensitivity of some signature datasets demand an on-site working policy.
Candidates must be willing to undergo the Homeland Security Full Background Investigation. Employment is contingent on satisfying this security check.
Languages
- English
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