Staff Computer Vision and Machine Learning Engineer

- 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.

- Write production-grade code for ML models as services and APIs.

- Collaborate with cross-functional teams, including data engineering and software development, to integrate computer vision and machine learning models into production systems.

- Build and maintain scalable data processing workflows and model deployment infrastructure.

- Debug and resolve model performance issues, track relevant metrics, and implement continuous improvements to ensure model accuracy and reliability.

- Keep up with the latest CV and ML tooling and communities.

- Lead the design and implementation of complex computer vision and machine learning models across various business units.

- Architect and develop scalable infrastructure for automated model training, hyperparameter tuning, and deployment.

- Mentor and guide junior engineers, collaborating closely with computer vision and machine learning engineers to optimize and refine models.

- Own the end-to-end systems for model monitoring, maintenance, and retraining to ensure high availability and performance.

- 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 (see below).

- 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.

- Nice to have: publication(s) in top CV c onference (CVPR, ICCV, ECCV, etc )

- 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.

Annual Salary $130,000.00 - $260,000.00 The above annual salary range is a general guideline. Multiple factors are taken into consideration to arrive at the final hourly rate/ annual salary to be offered to the selected candidate. Factors include, but are not limited to, the scope and responsibilities of the role, the selected candidate's work experience, education and training, the work location as well as market and business considerations.