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Full-Time

Programmeuses

Programmeurs ML Ops/ ML Ops Engineer, Montreal

Confirmed live in the last 24 hours

Coactive

Coactive

11-50 employees

Unlocks insights from visual data using AI

Data & Analytics
AI & Machine Learning

Compensation Overview

$140k - $180kAnnually

+ Equity Grants

Mid, Senior

Montreal, QC, Canada

This is a hybrid position based in Montreal, Quebec.

Category
Applied Machine Learning
Deep Learning
AI & Machine Learning
Required Skills
Datadog
Kubernetes
Microsoft Azure
Python
NoSQL
Tensorflow
Pytorch
Apache Spark
SQL
Apache Kafka
Docker
AWS
Jenkins
Development Operations (DevOps)
CircleCI
Google Cloud Platform
Requirements
  • Bachelor's or Master’s degree in Computer Science, Engineering, or a related field.
  • 5+ years of experience in ML Ops, DevOps, or related roles.
  • Strong programming skills in Python.
  • Proficiency with ML frameworks such as MLFlow, TensorFlow, PyTorch, or scikit-learn.
  • Hands-on experience using big data technologies such as Spark and Kafka.
  • Experience with cloud platforms such as AWS, Google Cloud, or Azure.
  • Solid understanding of containerization and orchestration technologies like Docker and Kubernetes.
  • Experience with data engineering and ETL processes.
  • Experience of database systems, both SQL and NoSQL.
  • Familiarity with CI/CD tools such as Github Actions, Jenkins, GitLab CI, or CircleCI.
  • Experience with monitoring tools like Datadog, NewRelic, or similar.
  • Strong problem-solving skills and attention to detail.
  • Excellent communication and collaboration abilities.
  • Experience with model interpretability and explainability tools.
Responsibilities
  • Design and implement scalable and reliable ML pipelines.
  • Automate the deployment and monitoring of ML models.
  • Collaborate with ML Engineers, Applied ML Researchers and software engineers to integrate ML models into applications.
  • Optimize model performance and scalability.
  • Enable Research scientists with experimentation, validation and productionisation of new models.
  • Monitor and troubleshoot production ML systems to ensure high availability and reliability.
  • Implement and enforce best practices for ML Ops, including versioning, testing, and validation.

Coactive.ai specializes in extracting analytics and insights from unstructured image and video data. The platform connects to clients' visual data through secure data lake connections or allows uploads via an API. Users can query their visual data using SQL and other familiar big data tools, making it accessible without requiring deep technical skills. Coactive addresses the challenge of incomplete metadata by using advanced data-centric AI and deep learning techniques to generate accurate metadata, facilitating easier analysis. The company likely operates on a subscription or usage-based pricing model, providing businesses with essential tools to interpret visual data for informed decision-making.

Company Stage

Series B

Total Funding

$39.3M

Headquarters

San Jose, California

Founded

N/A

Growth & Insights
Headcount

6 month growth

223%

1 year growth

223%

2 year growth

320%
Simplify Jobs

Simplify's Take

What believers are saying

  • Securing $30M in Series B funding indicates strong investor confidence and provides substantial resources for growth and innovation.
  • Partnerships with major entities like Comcast, NBCUniversal, and Sky through the LIFT Labs Generative AI Accelerator highlight Coactive's potential for high-impact collaborations.
  • The backing from prominent investors like Andreessen Horowitz and Bessemer Venture Partners further validates Coactive's market potential and technological prowess.

What critics are saying

  • The niche focus on visual data analytics may limit market size compared to more generalized data analytics platforms.
  • Rapid growth and scaling could strain resources and operational capabilities, potentially impacting service quality.

What makes Coactive unique

  • Coactive.ai specializes in extracting insights from unstructured visual data, a niche often overlooked by broader data analytics platforms.
  • The platform's ability to generate precise metadata using advanced deep learning techniques sets it apart from competitors who struggle with incomplete or inaccurate metadata.
  • By allowing users to query visual data using familiar tools like SQL, Coactive reduces the technical barrier to entry, making advanced analytics accessible to non-experts.

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