Machine Learning Engineer III, Data

🔥 1 hour ago

🗽 New York – Remote

🗽 New York – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

🚰 Data Engineer

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Dayforce

WebsiteLinkedInAll Job Openings

5001 - 10000 employees

👥 HR Tech

☁️ SaaS

🏢 Enterprise

💰 $1G Post-IPO Debt - Dayforce on 2024-03

HR Tech • SaaS • Enterprise

Dayforce is a cloud-based human capital management (HCM) platform (developed by Ceridian) that provides payroll, HR, workforce management, time & attendance, benefits administration, and talent management in a unified SaaS application for employers. It’s designed as an enterprise-focused, B2B solution for managing employee lifecycle and payroll compliance.

📋 Description

• Design and train high-performance computer vision models for automated damage detection, focusing on precision, recall, and model robustness. • Architect and maintain high-throughput, containerized microservices for model serving using REST/gRPC to ensure low-latency performance. • Collaborate with business stakeholders to translate complex inspection requirements into scalable, production-grade ML solutions. • Own the end-to-end model lifecycle, from experimentation and design to deployment and optimization in high-traffic environments. • Design and maintain robust data pipelines using Kafka to ensure high-fidelity inputs for model serving and inference. • Perform additional duties as assigned.

🎯 Requirements

• Graduate education (MS or PhD) in a computationally intensive domain or equivalent work experience. • 3+ years of prior computer vision experience • Advanced proficiency with Computer Vision frameworks (e.g., PyTorch, OpenCV, TensorFlow) and Python/SQL. • Experience designing and maintaining visual data annotation pipelines and evaluation frameworks for complex, real-world image datasets. • Experience optimizing high-latency models for real-time inference • Backend software engineering experience in the cloud (AWS / GCP) with a focus on microservices (docker) and the ML model development lifecycle. • Experience building and maintaining streaming data pipelines (e.g., Kafka) for real-time model serving.