Cantina
Machine Learning Engineer, Images
About this role
Cantina Labs seeks a Senior Machine Learning Engineer to develop and optimize image generation models powering lifelike AI bots. You'll design production pipelines for photorealistic character generation, improve model consistency and inference speed, and collaborate across teams to bring generative features from prototype to production.
What you'll do
- Evaluate emerging image generation and identity preservation research and models
- Develop and deploy image generation and analysis pipelines to production
- Fine-tune and optimize models for character consistency, prompt responsiveness, and latency
- Design experiments to benchmark performance and track quality metrics across pipeline iterations
- Monitor and resolve production issues affecting users
- Partner with cross-functional teams to translate product requirements into ML solutions
What they're looking for
- Image synthesis and generative AI (Stable Diffusion, DiT, ViT)
- Python programming and service deployment
- PyTorch and TensorFlow
- ML infrastructure and scaling (GCP, AWS, Azure, or Baseten)
- TensorRT and CUDA optimization
- Production machine learning systems
- Experimental design and metrics evaluation
- Cross-functional collaboration and communication
Benefits
- Competitive salary ($200,000–$265,000) and equity
- Medical, dental, and vision insurance (99.99% covered)
- 42 days paid time off (15 PTO, 10 sick, 15 holidays, 2 floating)
- Parental leave and fertility support
- 401(k) retirement plan and $500/month lifestyle spending account
- Complimentary lunch, snacks, and One Medical membership
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Cantina
Cantina builds a social platform powered by realistic AI characters that engage in live conversations with users. The company is hiring Software Engineers, Prompt Engineers, and Machine Learning Engineers to optimize real-time speech systems, design AI behavioral patterns, and develop advanced image generation models for lifelike character creation.
View all jobs at CantinaLikely interview questions
- Walk us through a recent project where you deployed an image generation model to production. What were the main challenges with inference latency and consistency, and how did you address them?
- Describe your experience fine-tuning diffusion models or similar generative architectures. What metrics did you track to measure quality improvements, and how did you balance multiple objectives like inference speed vs. output quality?