About the role

The ML Engineer (Studio Applications) focuses on deploying and maintaining machine learning models that support LoglineAI’s production workflows. The role is primarily applied and production-focused rather than research-oriented.

The engineer will integrate third-party and open-source models for image and video generation, enhancement, and quality control into the platform. They will fine-tune models where necessary, manage inference services, and work with pipeline engineers and any research staff to ensure models perform reliably in real use cases.

Responsibilities:

• Integrate third-party and open-source models (image/video generation, super-resolution, artifact detection) into LoglineAI’s platform.
• Fine-tune or adapt models for specific needs such as style preservation, character identity, motion and camera control, and cleanup.
• Design and operate inference services, including model serving, autoscaling, A/B testing, and performance monitoring.
• Collaborate with Pipeline Engineers on model selection, parameter choices, and trade-offs between cost and quality.
• Work with research-oriented staff to convert prototypes into stable, monitored production services.

Requirements:

• 5–10+ years of experience in machine learning or computer vision, with strong Python and PyTorch/TensorFlow skills.
• Proven experience deploying models to production (batch or real-time inference).
• Experience with diffusion or other generative models, ideally including video or sequence-aware work.
• Familiarity with Docker/Kubernetes, experiment tracking (e.g., Weights & Biases, MLflow), and ML observability practices.
• Ability to read and implement methods from research papers at a practical level.

Ideal Candidate comes from applied CV/ML product teams or ML platform/MLOps environments, working in Machine Learning Engineer, Computer Vision Engineer, Applied Scientist, or Senior Data Scientist focused on ML-heavy, GPU-driven systems.