Lead Machine Learning Engineer

Job description

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Job details

Remote, Equity, Full Benefits, Own ML Architecture, Build Your Team!

This Jobot Job is hosted by Patrick Murphy

Are you a fit? Easy Apply now by clicking the Easy Apply button and sending us your resume.

Salary $180,000 - $230,000 per year

A Bit About Us

Supply Chain innovation is the name of the game! Using automation and machine learning to simplify information communication and move goods faster!

With over $3 Million in seed funding and a clear runway for success, this role offers ownership of the platform architecture, building a team, equity in the company, and more!

This role is fully remote!

Why join us?

  • Unlimited PTO
  • Equity in the company
  • Build your team
  • Own ML architecture
  • Work with amazing people!

Job Details

Machine Learning Expertise

  • Design, develop, and implement state-of-the-art machine learning models and algorithms to solve

complex problems.

  • Implement and enhance data preprocessing, feature engineering, and data visualization.
  • Evaluate and select appropriate machine learning frameworks, libraries, and tools.
  • Stay up-to-date with the latest developments in machine learning and AI.

Team Leadership

  • Lead a team of machine learning engineers and data scientists.
  • Provide guidance, mentorship, and technical support to team members.
  • Set clear goals, priorities, and deadlines for projects.
  • Collaborate with other departments to align ML efforts with business objectives.

Project Management

  • Manage end-to-end machine learning projects, including problem formulation, data collection,

model development, and deployment.

  • Allocate resources (people, time, and in some cases money) effectively to meet project deadlines.
  • Ensure the quality and reliability of ML models through testing and validation.
  • Track project progress, report on key metrics, and make data-driven decisions.
  • Create robust and complete end-to-end documentation (including procedures) for all ML activities.
  • Work with the CEO and other Sr. Members to create ML Policies and Best Practices.

Model Deployment and Maintenance

  • Lead the deployment of machine learning models into production environments.
  • Implement robust monitoring and maintenance procedures to ensure the ongoing reliability and

performance of deployed models.

  • Collaborate with DevOps and IT teams to integrate ML solutions into existing systems.

Research and Innovation

  • Stay abreast of cutting-edge ML research and technologies.
  • Experiment with new approaches and techniques to improve model performance.
  • Innovate and drive the adoption of AI/ML in new areas

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