Machine Learning Engineer

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.