Senior Machine Learning Engineer - (Remote - US)

Jobgether; United States; Remote; USD 100,000 - 720,000; Full time

Job summary

Jobgether is seeking a Senior Machine Learning Engineer to advance their AI-driven job matching platform. In this role, you will be pivotal in crafting the infrastructure that facilitates model deployment and enhancement, collaborating with diverse teams to optimize data processes and ensure scalability. This opportunity is perfect for someone with strong expertise in machine learning and data engineering, looking to impact innovative projects significantly.

Benefits

Medical, dental, and vision insurance

Mental health support

Flexible time off

Paid parental leave

Access to Employee Resource Groups

401(k) plan with employer matching

Work-from-home equipment

Qualifications

• 5+ years of experience in machine learning engineering or data science.
• 3+ years working with AWS tools for MLOps.
• Experience with large language models is a plus.

Responsibilities

• Design, develop, and optimize the ML infrastructure.
• Curate datasets and manage data pipelines.
• Implement automation to streamline model deployment.

Skills

Python

SQL

AWS

Machine Learning

Data Engineering

Education

Bachelor's degree in Computer Science or related field

Tools

AWS Sagemaker

Snowflake

Job description

About Jobgether

Jobgether is a Talent Matching Platform that partners with companies worldwide to efficiently connect top talent with the right opportunities through AI-driven job matching.

About Jobgether

Jobgether is a Talent Matching Platform that partners with companies worldwide to efficiently connect top talent with the right opportunities through AI-driven job matching.

One of our companies is currently looking for a Senior Machine Learning Engineer in the United States.

As a Senior Machine Learning Engineer, you will work on building and maintaining the core infrastructure to support the development, training, deployment, and operation of machine learning models and pipelines. You will collaborate with cross-functional teams to optimize processes, manage large datasets, and ensure the scalability of machine learning solutions. This is an exciting opportunity for an experienced professional to work in a fast-paced environment and contribute to innovative projects while further honing your technical expertise.

Accountabilities:

• Design, develop, and optimize the infrastructure required for the entire machine learning lifecycle, from model development to deployment and continuous retraining
• Work closely with data engineers to curate high-quality datasets and manage data pipelines that support machine learning initiatives
• Leverage AWS tools (such as SageMaker) to deploy and migrate models to cloud environments, ensuring optimal performance and scalability
• Implement automation best practices to streamline model deployment and improve the efficiency of the overall machine learning pipeline
• Collaborate with stakeholders to troubleshoot and resolve data-related technical issues, offering solutions to enhance workflows and data integration
• Optimize model scoring pipelines to improve performance, minimize latency, and address errors that may arise in production environments
  
  
  

Requirements

• 5+ years of experience in machine learning engineering, data science, or data engineering roles
• At least 3 years of hands-on experience in developing and implementing machine learning infrastructure and MLOps, specifically using AWS Sagemaker and Snowflake
• Proficiency in Python and SQL, with strong expertise in automating processes using AWS tools
• Extensive experience in building and optimizing data pipelines to support machine learning models
• Familiarity with monitoring systems and metrics for evaluating model performance and ensuring model health in production environments
• Prior experience with large language models (LLMs) and RAG (retrieval-augmented generation) applications is a plus
• A strong foundation in model monitoring, containerization, and orchestration within MLOps
  
  
  

Benefits

• Medical, dental, and vision insurance for you and your dependents
• Mental health support through Employee Assistance Program (EAP) and wellness platform subscriptions
• Flexible time off, including mental health days and volunteer days, alongside 14 paid company-wide holidays
• Paid parental leave, inclusive of adoptive parents
• Access to Employee Resource Groups (ERGs) to support a diverse and inclusive workplace
• 401(k) plan with employer matching and immediate vesting
• Additional benefits such as life insurance, accident insurance, disability coverage, and pet insurance
• Comprehensive spending accounts for healthcare, flexible spending, and commuting expenses
• All necessary work-from-home equipment provided (e.g., computer, keyboard, mouse, etc.)
  
  
  

Jobgether hiring process disclaimer:

This job is posted on behalf of one of our partner companies. If you choose to apply, your application will go through our AI-powered 3-step screening process, where we automatically select the 5 best candidates.

Our AI thoroughly analyzes every line of your CV and LinkedIn profile to assess your fit for the role, evaluating each experience in detail. When needed, our team may also conduct a manual review to ensure only the most relevant candidates are considered.

Our process is fair, unbiased, and based solely on qualifications and relevance to the job. Only the best-matching candidates will be selected for the next round.

If you are among the top 5 candidates, you will be notified within 7 days.

If you do not receive feedback after 7 days, it means you were not selected. However, if you wish, we may consider your profile for other similar opportunities that better match your experience.

Thank you for your interest!

Seniority level

• Seniority level

  Mid-Senior level

Employment type

• Employment type

  Full-time

Job function

• Job function

  Information Technology

• Industries

  Non-profit Organizations and Primary and Secondary Education

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