Lead ML Engineer - Remote Data & MLOps

Muttdata; United States; Remote; USD 150,000 - 210,000; Full time

Benefits offered by this job

Remote-First Culture

Paid AWS & Databricks Certifications

Mutt Week: extra vacation

Job summary

Mutt Data, a remote-first startup, seeks a Machine Learning Engineer Lead to drive ML solutions for global clients. You will lead ML/AI projects, productize models, and guide ML engineers and data engineers through end-to-end lifecycles.

Share ownership of roadmaps, engage with customers to define scope, and advance MLOps practices, monitoring, and deployment. Strong Python and modern data architectures are essential.

Qualifications

• Demonstrated leadership and client-facing experience.
• In-depth ML/AI knowledge including models, time-series, and MLops concepts.
• Strong Python skills and experience with modern data architectures.

Responsibilities

• Project Management: engage with customers to define scope and timelines.
• Team Leadership: guide ML and data engineers across projects.
• Lead ML Model Productization following MLops best practices (orchestration, testing, monitoring, serving).
• Develop ML POCs for internal and client needs.
• Oversee ML model lifecycle, optimize performance, latency, and throughput.
• Translate business requirements into software solutions with trade-offs.
• Explore new ML engineering technologies to enhance value.
• Contribute to project roadmaps and estimates.
• Document and share AI/ML best practices within the company.
• Assist in technical interviews and hiring decisions.

Skills

Team Leadership

Client Interactions

ML/AI Proficiency

Python

Software Architecture

English Proficiency

Tools

Kubernetes

Docker

Airflow

MLflow

DBT

PyTorch

TensorFlow

Job description

Mutt Data, a remote-first startup, seeks a Machine Learning Engineer Lead to drive ML solutions for global clients. You will lead ML/AI projects, productize models, and guide ML engineers and data engineers through end-to-end lifecycles.

Share ownership of roadmaps, engage with customers to define scope, and advance MLOps practices, monitoring, and deployment. Strong Python and modern data architectures are essential.