ML Engineer - Privacy-First Generative Synthetic Data

Secludy, Inc.; San Francisco (CA); Ibrido; USD 140.000 - 210.000; Tempo pieno

Descrizione del lavoro

Secludy, Inc. in San Francisco is building a privacy-preserving AI platform and seeks an ML engineer to join as the first engineering hire at a seed-stage startup.

You will turn research into production-ready infrastructure for synthetic data with provable privacy guarantees, partnering with founders to ship high-impact solutions. You will optimize distributed training on Kubernetes, own the deployment pipeline, and drive performance while debugging complex failures.

Competenze

• 3 to 6 years in ML engineering or applied research.
• Strong PyTorch experience and ability to write a custom training loop.
• Experience with generative models (diffusion, VAEs, transformers) beyond inference.
• Track record of taking experimental code to production.
• Comfort with tabular, text, or time-series data.

Mansioni

• Accelerate research loop by turning mathematical hypotheses into testable code.
• Productionize the engine and own the deployment pipeline.
• Orchestrate distributed training and generation across GPU clusters with Kubernetes.
• Build logging that explains why a generation job failed.
• Diagnose failures spanning infrastructure and model training.

Conoscenze

PyTorch

Custom training loop

Gen models (diffusion/VAEs/transformer

Production deployment

Diagnostics & debugging

Strumenti

Kubernetes

Docker

Descrizione del lavoro

Secludy, Inc. in San Francisco is building a privacy-preserving AI platform and seeks an ML engineer to join as the first engineering hire at a seed-stage startup.

You will turn research into production-ready infrastructure for synthetic data with provable privacy guarantees, partnering with founders to ship high-impact solutions. You will optimize distributed training on Kubernetes, own the deployment pipeline, and drive performance while debugging complex failures.