Machine Learning Engineer

• You’ll design and optimize computer vision models that transform real patient images into realistic, medically accurate “after” results.
• You’ll work closely with product, design, and medical experts to train, fine-tune, and deploy models that help people make confident, informed decisions about their appearance and well-being.
• Evaluate and benchmark state-of-the-art models (segmentation, detection, SDXL/diffusion, LLM-based classifiers).
• Select the best architectures balancing accuracy, inference speed, efficiency, and fairness.
• Develop generative models for before/after treatment previews, ensuring realism, inclusivity, and clinical credibility.
• Design inference pipelines optimized for low latency and high throughput.
• Use model compression, quantization, and distillation to reduce compute costs while maintaining accuracy.
• Leverage AWS (Lambda, Step Functions, SageMaker, GPU pipelines) or equivalent to build cloud-scale systems.
• Build quality-control systems (blur detection, orientation/rotation correction, lighting checks).
• Implement multi-candidate generation + ranking for robustness.
• Ensure identity consistency and similarity verification across generated images.
• Develop fallback algorithms for resilience under failure modes.
• Package models for deployment into production at scale, with proper versioning and monitoring.
• Build CI/CD pipelines for continuous training, testing, and deployment.
• Define and track key performance metrics (latency, throughput, drift, fairness, user satisfaction).
• Work with product, engineering, and clinical teams to align on accuracy, safety, and inclusivity goals.
• Stay up-to-date with research, and bring the best of academia and industry into production.
• Act as a thought partner in shaping responsible AI practices within the product.
• Mentor a small pod of ML and Platform engineers as hiring ramps.

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