Staff Computer Vision and Machine Learning Engineer

GEICO GEICO · Insurance · Bethesda, MD +2

Staff Computer Vision and Machine Learning Engineer to lead the design, development, and deployment of advanced CV/ML models, mentor junior engineers, and drive the full lifecycle of model development. This role involves hands-on engineering, leadership, and integrating models into production systems, focusing on scalability, performance, and reliability.

What you'd actually do

  1. Design and implement computer vision and machine learning models and components that solve real-world business problems in close collaboration with Product, business units, and Data Science teams.
  2. Write production-grade code for ML models as services and APIs.
  3. Collaborate with cross-functional teams, including data engineering and software development, to integrate computer vision and machine learning models into production systems.
  4. Build and maintain scalable data processing workflows and model deployment infrastructure.
  5. Debug and resolve model performance issues, track relevant metrics, and implement continuous improvements to ensure model accuracy and reliability.

Skills

Required

  • B.S. in computer science, computer & electrical engineering or related discipline, M.S. in computer vision, machine learning, Computer Science, Statistics, Mathematics, or a related quantitative field or equivalent work experience in CV domain
  • 6+ years of experience applying computer vision and machine learning techniques such as ensemble learning, deep learning, reinforcement learning, NLP, or related approaches.
  • Direct work experience in CV discriminative models (detection, segmentation), CV foundation models, VLM, MLLMs, generative tools (diffusers).
  • 6+ years of experience with SQL, Spark (or equivalent), and Python, computer vision, and machine learning frameworks such as TensorFlow, PyTorch, and Scikit-learn.
  • 4+ years of experience working with cloud platforms and environments such as AWS, Microsoft Azure, Databricks and/or Snowflake, and Kubernetes.
  • 4+ years of experience applying computer vision and machine learning techniques in a production environment for business solutions.
  • Strong foundation in advanced computer vision and machine learning algorithms, including supervised and unsupervised learning techniques, as well as familiarity with generative models.
  • Proficiency in statistical modeling, including probability theory and hypothesis testing, to interrogate, analyze, and interpret data effectively.
  • Strong programming skills, including proficiency in Python and experience with computer vision and machine learning frameworks such as TensorFlow, Keras, and PyTorch.
  • Familiarity with software development best practices, including CI/CD pipelines, containerization such as Docker, and orchestration such as Kubernetes.
  • Deep understanding of MLOps practices, including model versioning, A/B testing, and continuous deployment.
  • Deep understanding of cloud computing platforms such as Azure, AWS, or GCP, distributed systems, and large-scale data processing technologies such as Spark and Kafka.
  • Proven experience leading computer vision and machine learning projects, managing stakeholders, and scaling computer vision and machine learning solutions in production environments.
  • Excellent communication skills, with the ability to present complex technical topics to both technical and non-technical audiences.
  • Exceptional problem-solving and analytical skills with a focus on practical, business-oriented outcomes.

Nice to have

  • publication(s) in top CV conference (CVPR, ICCV, ECCV, etc)

What the JD emphasized

  • production environment
  • production systems
  • production-grade code
  • production environments

Other signals

  • design, development, and deployment of advanced computer vision and machine learning models
  • building scalable computer vision and machine learning models
  • driving the full lifecycle of computer vision and machine learning model development
  • technical lead for a team of Computer Vision and Machine Learning engineers
  • hands-on engineering work and leadership responsibilities
  • production-grade code for ML models as services and APIs
  • integrate computer vision and machine learning models into production systems
  • scalable data processing workflows and model deployment infrastructure
  • Debug and resolve model performance issues, track relevant metrics, and implement continuous improvements
  • Lead the design and implementation of complex computer vision and machine learning models
  • Architect and develop scalable infrastructure for automated model training, hyperparameter tuning, and deployment
  • Own the end-to-end systems for model monitoring, maintenance, and retraining