Cognitiv is looking to revolutionize the advertising industry by building and scaling the next generation of their ML infrastructure. They are transitioning from a legacy platform to a more modern, automated, and highly scalable system to power their deep learning and real-time advertising platform.
Requirements
- Strong coder: You write clean, maintainable, and scalable code in Python.
- Hands-on builder: You have experience with ML pipelines, MLOps tools, or automation frameworks and thrive on improving workflows.
- Deep learning practitioner: You’ve trained models with PyTorch (bonus if PyTorch Lightning) and are curious about deploying large language models (LLMs).
- Cloud-native thinker: You’re familiar with AWS services, containerization, and orchestration tools like Docker and Airflow.
- Python
- PyTorch
- PyTorch Lightning
Responsibilities
- Design, automate, and optimize ML workflows including data ingestion, model training, deployment, and performance monitoring.
- Build and maintain scalable, cloud-native pipelines that support large-scale experimentation and high-volume model training and scoring.
- Own core components of our MLOps stack and drive improvements around reliability, scalability, and ease of use.
- Write production-grade Python code, participate in code reviews, and ensure high-quality engineering standards.
- Enhance our observability, logging, and alerting infrastructure to improve operational resilience and reduce time-to-detection.
- Propose and experiment with new tools or workflows to help modernize our ML lifecycle and platform delivery.
- Own the end-to-end ML lifecycle — from ingesting client data to developing, deploying, and monitoring models in production
Other
- In-office teammate: You’re available to collaborate in-person MTW in Bellevue WA or San Mateo CA.
- Collaborative engineer: You enjoy problem-solving with cross-functional partners and communicate clearly across teams.
- Growth-driven: You’re eager to take ownership, deepen your technical expertise, and deliver high-impact work.
- Partner with cross-functional teams (Product, Engineering, ML Research) to align automation efforts with business needs.
- Location: Bellevue (hybrid: 3 days in-office, 2 days remote)
