Offer summary

Qualifications:

Bachelor's or Master's degree in Computer Science, Machine Learning, or a related field., 2 to 3 years of hands-on experience as a Machine Learning Engineer., Proficiency in Python and strong coding skills with a focus on clean and efficient code., Experience with AWS services, particularly SageMaker, EC2/ECS, AWS SDK, and S3 buckets..

Key responsibilities:

Collaborate with cross-functional teams to understand business needs and develop machine learning solutions.

Utilize AWS services to build, train, and deploy machine learning models at scale.

Create and analyze datasets, conduct experiments, and fine-tune models for optimal performance.

Stay up to date with the latest advancements in machine learning and AWS services to drive innovation.

Job description

This is a remote position.

Job Description:

We are seeking a skilled and motivated Machine Learning Engineer to join our team. As a Machine

Learning Engineer at Ventera, you will have the opportunity to work on cutting-edge projects that

leverage the AWS Machine Learning ecosystem (SageMaker, EC2/ECS, S3 buckets, etc.). Your primary

responsibilities will involve developing, deploying, and maintaining dozens of machine learning models

in production using AWS services, as well as optimizing data pipelines for maximum efficiency. The

current model development focus is on Anomaly Detection Timeseries prediction, with more added

projects in the future.

Key Responsibilities:

- Collaborate with cross-functional teams to understand business needs and develop machine learning

solutions.

- Utilize AWS SageMaker, EC2/ECS, AWS SDK, S3 buckets and the rest of the AWS ML ecosystem to build,

train and deploy machine learning models at scale.

- Develop and maintain clean, efficient, and well-documented Python code following best coding

practices.

- Knowledge of deep learning frameworks such as PyTorch/Tensorflow, and other ML packaged libraries

to design and implement machine learning algorithms (i.e. DARTS for timeseries, PyCaret for tree-based

solutions, etc.).

- Create and analyze datasets, conduct experiments, and fine-tune models to achieve optimal

performance.

- Use SageMaker Notebooks and other relevant tools for data exploration, visualization, and model

evaluation.

- Stay up to date with the latest advancements in machine learning and AWS services to drive innovation

within the team.

Requirements

Qualifications:

- Bachelors or Masters degree in Computer Science, Machine Learning, or a related field.

- 2 to 3 years of hands-on experience as a Machine Learning Engineer.

- Proficiency in Python and strong coding skills with a focus on clean and efficient code.

- Experience with AWS services, particularly SageMaker, EC2/ECS, AWS SDK, and S3 buckets.

- Familiarity with AIML frameworks such as PyTorch/Tensorflow/other open-sourced libraries.

- Knowledge of/experience in LLM (Large Language Models) fine-tuning and training techniques.

- Strong analytical and problem-solving skills.

- Excellent communication and teamwork abilities.

- Great all around get it done attitude. Although we work remotely, the team here has a great culture,

and we are looking to maintain that great team!

Additional Nice to Have Qualifications:

- Previous experience with Docker and containerization within AWS.

- Knowledge of serverless computing using AWS Lambda.

- Understanding of MLOps and DevOps practices, particularly model deployment.

- Experience with version control systems like Git.

- Experience with multivariate time series forecasting.

- Experience using publisher/subscriber for messaging queues.

- Experience developing front end applications for data science POCs.

- Experience in an Agile coding environment is a bonus (though not required, that can be picked up

quickly).

Benefits

Competitive salary
Fully paid CareFirst BCBS Medical, Dental, and Vision coverage for you and your family
Amazing team and great management that takes good care of their employees
Generous paid time off and 11 paid holidays
401(k) retirement plan with employer matching
Performance-based bonus system
Professional development budget