MLOps Engineer, JAX, PyTorch, Pallas/Triton

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Weekday (YC W21)

WebsiteLinkedInAll Job Openings

11 - 50 employees

Founded 2021

💼 Consulting

👥 HR Tech

☁️ SaaS

Consulting • HR Tech • SaaS

Weekday is a modern recruitment platform that combines AI technologies with a vast database of potential candidates, aiming to streamline the hiring process for companies in India. They offer various services, including a proactive outreach approach that helps employers connect with top talent, as well as tools for candidates to easily apply for jobs. Weekday's emphasis on candidate engagement through multiple channels, including email, WhatsApp, and phone calls, sets it apart in the competitive landscape of recruitment agencies.

📋 Description

• Partner with research and engineering teams to strengthen AI model capabilities in MLOps, ML infrastructure, and large-scale training systems. • Design challenging, real-world MLOps and machine learning systems tasks that reflect production engineering scenarios. • Develop accurate, well-documented solutions to complex ML infrastructure and training pipeline problems. • Review and evaluate technical tasks and AI-generated solutions, providing clear and actionable written feedback. • Create detailed evaluation rubrics and scoring frameworks for topics including: • - Distributed training architectures • - ML pipeline design • - Infrastructure optimization • - Kernel-level programming • - Performance tuning • Collaborate with fellow subject matter experts to maintain consistency, quality, and technical accuracy across training datasets. • Contribute domain expertise to improve the reasoning capabilities of advanced AI systems.

🎯 Requirements

• Minimum 2 years of professional experience in MLOps, Machine Learning Infrastructure, or ML Systems Engineering within a recognized technology organization. • Hands-on production experience with JAX and/or PyTorch in large-scale machine learning environments. • Practical experience developing or optimizing custom GPU kernels using Pallas (JAX) or Triton. • Strong understanding of distributed training systems, model optimization, and scalable ML infrastructure. • Demonstrated career growth and increasing technical responsibility. • Availability to work 40 hours per week during standard weekday business hours. • Excellent written communication skills with the ability to clearly explain technical concepts and architectural decisions. • Experience designing and optimizing large-scale ML training pipelines. • Knowledge of distributed computing and GPU performance optimization. • Familiarity with evaluation methodologies for AI models and ML systems. • Experience collaborating with research teams on advanced machine learning projects. • Passion for advancing AI infrastructure and frontier model development.