H

Senior Machine Learning Engineer

hired • United States
Remote
This Job is No Longer Active This position is no longer accepting applications

We are hiring a Senior Machine Learning Engineer to lead the design, training, and deployment of ML systems in production. The role combines deep ML expertise with strong software engineering, owning models from research through scaled inference. Key requirements include 5+ years of experience, strong Python skills, and deep experience with PyTorch or TensorFlow.

Key Highlights
Lead design, training, and deployment of ML systems in production
5+ years of experience in machine learning engineering with production ownership
Fully remote, flexible work hours with competitive compensation
Key Responsibilities
Design, train, and deploy machine learning models at production scale
Lead architecture decisions for training, evaluation, and inference pipelines
Build evaluation frameworks and monitoring for model quality and drift
Partner with data engineering on training data pipelines and feature stores
Mentor junior ML engineers and contribute to ML practice
Stay current on research and bring proven techniques into production
Technical Skills Required
Python PyTorch Cloud ML Infrastructure
Benefits & Perks
Competitive salary with indicative range $150,000 – $250,000
Fully remote, flexible work hours
Performance-based bonus structure and annual learning & development stipend

Job Description

  • Role: Senior Machine Learning Engineer
  • Location: Remote
  • Employment Type: Full-time

Compensation: Competitive salary commensurate with experience, qualifications, and location. Indicative range: $150,000 – $250,000 (USD equivalent), plus benefits.

Role Overview

We are hiring a Senior Machine Learning Engineer to lead the design, training, and deployment of ML systems in production. The role combines deep ML expertise with strong software engineering — owning models from research through scaled inference.

Key Responsibilities

- Design, train, and deploy machine learning models at production scale

- Lead architecture decisions for training, evaluation, and inference pipelines

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