Edtech.com's Summary

NWEA is hiring a Machine Learning Engineer. The position is remote.

Highlights

Manage, maintain, and improve machine learning models in production environments.
Design, test, and deploy models as prototypes, advancing to APIs for end-user predictions.
Optimize and evaluate machine learning models through tests and experiments.
Utilize CI/CD processes for code and model development without disrupting production.
Provide technical support to data and product teams regarding machine learning systems.
Masters or Ph.D. in computer science, engineering, or a quantitative field required.
5+ years of experience in machine learning or data science with commercial MLOps experience.
Proficient in Python, C++, R, and machine learning libraries such as PyTorch and TensorFlow.
Salary range is between $110k – $135k.

Machine Learning Engineer Full Description

Job Title: Machine Learning Engineer

Location: Remote

Who We Are

NWEA® is a division of HMH that supports students and educators through research, assessment solutions, policy and advocacy services, professional learning and school improvement services that fight for equity, drive classroom impact and push for systemic change in our educational communities. For nearly 50 years, NWEA has developed innovative pre-K–12 assessments, including their flagship interim assessment, MAP® Growth™ and their reading fluency and comprehension assessment, MAP® Reading Fluency™. For more information, visit NWEA.org to learn more.

What you will do

The Machine Learning Engineer leads the design, development, and deployment of real-world machine learning systems that facilitate and accelerate advanced AI solutions at scale. Machine learning engineers manage aspects of the machine learning pipeline, including sourcing and preparing data, building, and training models, and deploying to and monitoring models in production.

Responsibilities

Manage, maintain, scale, and improve machine learning models already running in production environments.
Design, test, and deploy models to production, initially as a prototype, and then as an API that can serve predictions for end users.
Design, run, and evaluate machine learning tests and experiments.
Optimize machine learning models according to how they behave in production.
Use CI/CD processes throughout the model development lifecycle to develop higher quality code and models without disruption to production.
Provide technical support to data and product teams, helping relevant parties use and understand machine learning systems and datasets.

Education and Experience

Masters or Ph.D. in computer science, engineering, or quantitative field.
5+ years of hands-on experience in machine learning or data science.
Commercial experience in MLOps e.g., SageMaker, MLflow, Kubeflow).
Proficient with machine learning algorithms, architectures, programming languages (e.g., Python, C++, R), and libraries (e.g., PyTorch, TensorFlow).
Proficient with SQL and analytical warehouses (e.g., Snowflake).
Familiarity with automation and deployment (e.g., Jenkins, GitHub actions, Concourse, etc.) and infrastructure as code (e.g., Terraform) technologies.
Employed distributed processing frameworks such as Spark and Dask and has interacted with cloud platforms and container technologies.
Practical knowledge of software engineering concepts and best practices, like testing frameworks, packaging, API design, DevOps, DataOps and MLOps.
Excellent problem-solving skills and easily adapt to new technologies, trends, and frameworks.
Proven ability to improve machine learning practices and create a collaborative, high-performing team.

Salary range: 110k – 135k

Application Deadline:

The application window for this position is anticipated to close on December 22, 2024. We encourage you to apply as soon as possible. The posting may be available past this date but is not guaranteed.

HMH is fully committed to Equal Employment Opportunity and to attracting, retaining, developing and promoting the most qualified employees without regard to race, gender, color, religion, sexual orientation, family status, marital status, pregnancy, gender identity, ethnic/national origin, ancestry, age, disability, military status, genetic predisposition, citizenship status, status as a disabled veteran, recently separated veteran, Armed Forces service medal veteran, other covered veteran, or any other characteristic protected by federal, state or local law. We are dedicated to providing a work environment free from discrimination and harassment, and where employees are treated with respect and dignity. We actively participate in E-Verify.

#LI-VA1

Original Job Description

Job Title: Machine Learning Engineer

Location: Remote

Who We Are

What you will do

Responsibilities

Manage, maintain, scale, and improve machine learning models already running in production environments.
Design, test, and deploy models to production, initially as a prototype, and then as an API that can serve predictions for end users.
Design, run, and evaluate machine learning tests and experiments.
Optimize machine learning models according to how they behave in production.
Use CI/CD processes throughout the model development lifecycle to develop higher quality code and models without disruption to production.
Provide technical support to data and product teams, helping relevant parties use and understand machine learning systems and datasets.

Education and Experience

Masters or Ph.D. in computer science, engineering, or quantitative field.
5+ years of hands-on experience in machine learning or data science.
Commercial experience in MLOps e.g., SageMaker, MLflow, Kubeflow).
Proficient with machine learning algorithms, architectures, programming languages (e.g., Python, C++, R), and libraries (e.g., PyTorch, TensorFlow).
Proficient with SQL and analytical warehouses (e.g., Snowflake).
Familiarity with automation and deployment (e.g., Jenkins, GitHub actions, Concourse, etc.) and infrastructure as code (e.g., Terraform) technologies.
Employed distributed processing frameworks such as Spark and Dask and has interacted with cloud platforms and container technologies.
Practical knowledge of software engineering concepts and best practices, like testing frameworks, packaging, API design, DevOps, DataOps and MLOps.
Excellent problem-solving skills and easily adapt to new technologies, trends, and frameworks.
Proven ability to improve machine learning practices and create a collaborative, high-performing team.

Salary range: 110k – 135k

Application Deadline:

#LI-VA1

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