Resumen del rol

Machine Learning Engineer III, Data

Requisitos y responsabilidades

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Who we are:

  • Multiple medical plans including a high deductible, low cost health plan
  • Company-sponsored (paid) Short-Term Disability, Long-Term Disability, and Life Insurance
  • Comprehensive optional benefits such as Dental, Vision, Supplemental Life/AD&D, Legal/ID Protection, and Accident and Critical Illness Insurance
  • Generous paid time off options, including uncapped vacation days, the greater of 3 paid sick days or in accordance with the applicable state or local paid sick leave law, 6 paid company holidays, 2 floating holidays, parental leave, bereavement leave, jury duty leave, voting leave, and other forms of paid leave as required by applicable law or regulation
  • Employee Stock Purchase Program with additional opportunities to earn stock in the Company
  • Retirement planning through the Company’s 401(k)

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.
  • Perform additional duties as assigned.

Required Qualifications:

  • 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.

Preferred Qualifications:

  • Knowledge of ML frameworks and libraries, such as Kubeflow, Databricks, KServe and so on
  • Experience designing evaluation frameworks for complex visual data
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Señal de senioritySeniorNivel del candidato
StackAWS, Docker, GCPSkills principales
Ubicación5 países aceptadosElegibilidad

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