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This listing was closed on Jul 25, 2026.
Browse Open JobsMLOps Engineer | Remote | $70 –$110/hr
Summary by AIEngineer.careers
MLOps Engineer | Remote | $70 –$110/hr is a MLOps Engineer role (contract). with Call For Referral. in REMOTE, SAN FRANCISCO, US. Compensation shown: $190K–$296K (est.). Imported listing (source: linkedin.com). Apply on the employer's site (linkedin.com).
Imported listing (source: linkedin.com) · Apply on linkedin.com
REMOTE · SAN FRANCISCO · US
$190K–$296K (est.)
Contract
MID
The sections below reproduce the third-party job description for reference. AIEngineer.careers does not write or control this text.
Imported job description
Sourced from linkedin.com
This role focuses on advancing AI systems through large-scale ML infrastructure and training optimization. The MLOps Engineer will support GenAI initiatives, design solutions, and contribute to scalable training environments.
Responsibilities
- Support AI research and engineering teams in improving ML infrastructure and training systems
- Design advanced MLOps and ML systems tasks with accurate, structured technical solutions
- Evaluate ML systems outputs and provide detailed technical feedback
- Develop evaluation rubrics and frameworks for distributed systems, training pipelines, and kernel-level optimization
- Collaborate with domain experts to maintain consistency and quality across AI training workflows
- Contribute to improvements in large-scale model training performance and infrastructure reliability
Requirements
- 2+ years of professional experience in ML infrastructure, MLOps, or ML systems engineering
- Hands-on production experience with JAX and/or PyTorch at scale
- Experience writing or optimizing GPU kernels using Pallas or Triton
- Strong understanding of ML training systems and distributed infrastructure
CF
Call For Referral
Kolkata · IN · 11+ employees
Call For Referral is an online platform that connects job seekers with verified employees at top companies to streamline the job referral process. The company aims to simplify and democratize referrals, helping candidates get noticed by hiring managers through trusted internal networks.
