Machine Learning Operations Engineer

Department: Precision

Location: Santa Clara, California

Compensation: $190,000 - $210,000 / year

Description

Precision Neuroscience is pioneering a brain implant, known as a brain-computer interface (BCI), to restore communication and independence for people with neurological conditions. Our cutting-edge technology is designed to empower people with paralysis to control digital devices—such as computers and smartphones—with their thoughts alone, opening new possibilities for daily life.

Precision's multidisciplinary team brings together leading experts in diverse fields such as neurosurgery, artificial intelligence, machine learning, microfabrication, electrical engineering, and more. We are committed to turning breakthrough scientific advancements into real-world solutions for people affected by conditions such as spinal cord injury, stroke, and ALS.

As a Precision employee, you will be joining one of the fastest moving and best-capitalized companies in the emerging field of BCI. Since our founding in 2021, Precision has secured over $155 million in funding, developed and validated our technology, and initiated human trials in collaboration with some of the nation's top hospitals.

We are seeking a Machine Learning Operations Engineer. As a ML Ops Engineer, you will bridge the gap between data science and operations by building and maintaining highly automated, self-service, infrastructure, pipelines, and processes needed to deploy and monitor machine learning models in production environments. You will also contribute heavily to creating data collection and labelling software.

This position is on-site at least 3 days a week at our Santa Clara, Chicago, New York or Indianapolis offices. We are unable to consider remote workers or people not currently based in the United States, and who do not have working rights.

Key Responsibilities

Build a paved path for ML engineers to preprocess data, train models and validate models. Start by leveraging AWS infrastructure and later swap out AWS components with third party tools or home grown modules as necessary. Also build infrastructure that can run training and inference at the edge and allow the seamless movement of ML training and inference code between the cloud and the edge.

We work with clinical partners around the country. Your team mates could be anywhere in the continental US. Staying in touch with our clinical partners and your team mates could require up to 20% travel.

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  Design: Create architecture/design documents for components you own. Lead/participate in Threat Model Analysis (TMA) and risk management activities.


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  Backend Programming: Optimize the efficiency of the ML team by creating dashboards, data pipelines training, inference and labelling infrastructure for the unique needs of our ML team and the product we need to build.


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  Deployment: Build scalable infrastructures for training and inference of machine learning models. Create CI/CD pipelines and automation tools for continuous deployment and integration of machine learning models.


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  Quality: Write automation frameworks, test plans, unit and integration tests. Work with the SW QA team on automated and manual system testing.


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  Teamwork: Provide timely design and code reviews. Test the code written by your peers.


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  Regulatory: Research and adhere to the software development process requirements mandated by various regulatory bodies.

Skills, Knowledge and Expertise

• 5+ years of experience creating ML infrastructure for deep learning models dealing with data at scale. 10+ years of software engineering experience.


• Deep knowledge of AWS and the AWS suite including a majority of the following: Sagemaker Studio, AWS Kinesis, S3, EC2, Lambda, Cloudwatch, EMR, Elastic Docker Container.


• Solid understanding of ML concepts (e.g., model selection, deep learning architectures, hyperparameter tuning) Ability to understand ML code leveraging modern ML and data frameworks such as Pytorch, Tensorflow, numpy and scikit-learn. Knowledge of AI/robotic frameworks like OpenCV, ROS2, Kaldi strongly preferred.


• Data engineering with distributed data processing and distributed training.


• Experience with MLOps frameworks like Kubeflow, MLFlow, Airflow, etc.


• Familiarity with containerization and orchestration tools such as Dockers and Kubernetes.


• Knowledge of A/B testing and benchmarking model performance in production.


• Expertise with Python and related ML libraries. Working knowledge of C++.


• PyQt user interface development experience or willingness to learn.


• Experience with IoT, edge computing and/or robotic systems strongly preferred.


• Knowledge of HIPPA, development of software/AI for medical devices is a plus.

Diverse workforces create the best culture, company, and products. We at Precision are committed to an inclusive culture that celebrates the uniqueness and contributions of everyone.
As an equal opportunity employer, Precision does not discriminate on the basis of sex, race, religion, national origin, disability status, protected veteran status, or any other characteristic protected by law.