Job description

What you will do: ACV's Machine Learning organization is looking for a talented Machine Learning Engineer IV to join our ML inspection team. In this role, you'll drive end-to-end computer vision solutions processing hundreds of thousands of vehicle inspections annually into reliable, actionable insights, directly reducing inspection turnaround time, improving valuation accuracy, and scaling the capabilities of our inspection platform. You'll design and train damage detection models while architecting the high-throughput serving infrastructure needed to keep those models performant under real production loads. As ACV continues to grow, you'll play a direct role in ensuring our inspection capabilities remain accurate, efficient, and resilient at scale. This role goes beyond executing on a defined roadmap. You'll identify opportunities, shape solutions end-to-end, and take ownership of outcomes. You connect the dots between stakeholder needs and what's technically feasible, bringing recommendations grounded in both theory and practical constraints. When you hear a narrow question, you think about the broader system it lives in and build toward that. The core responsibilities of this role are: 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. Required Qualifications: Graduate education (MS or PhD) in a computationally intensive domain or equivalent work experience. 5+ 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. Preferred Qualifications: Knowledge of ML frameworks and libraries, such as Kubeflow, Databricks, KServe and so on Experience designing evaluation frameworks for complex visual data Experience leading technical design reviews #LI-AM3

Requirements

Must-haves

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.
Graduate education (MS or PhD) in a computationally intensive domain or equivalent work experience.
5+ 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.

Nice-to-haves

Knowledge of ML frameworks and libraries, such as Kubeflow, Databricks, KServe and so on
Experience designing evaluation frameworks for complex visual data
Experience leading technical design reviews

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

machine learningmlrestpythonsqlawsgcpdocker

Platform: dayforce
Effort: medium

Benefits

Benefits detected in the listing.

Vision