Research Engineer advancing video/image generation models for AI characters, leading fine-tuning, novel architectures, data pipelines, and optimizations using PyTorch and multimodal techniques. Requires expertise in generation models and distributed training.
225k – 400k/yr
On-siteML Engineering
About the role
What You'll Do
Lead fine-tuning and continued training of video generation models, including image-to-video and joint audio-visual generation.
Design and experiment with novel model architectures for multimodal generation, including multimodal conditioning (voice, structured text, reference images).
Leverage techniques such as LoRA, RLHF, and full-parameter fine-tuning to improve model quality.
Design and build large-scale data pipelines and automated annotation workflows.
Explore model compression, inference acceleration, and serving optimizations for real-time video processing.
Who You Are
Strong passion for visual AI with hands-on problem-solving.
Proficient in PyTorch with end-to-end experience in data processing, model training, and deployment.
Solid understanding of video/image generation architectures (diffusion models, DiT, ControlNet, SOTA video models).
Experience with multimodal model training (audio, vision, language).
Experience with distributed training tools (FSDP, DeepSpeed).
Experience with large-scale data processing and dataset construction.
Nice to Have
Experience with joint audio-visual or speech-conditioned generation.
Experience with AIGC, video effects, character animation, or asset generation.
Familiarity with ML deployment (Kubernetes, Slurm, Docker, cloud platforms).
Conducts ML research on LLMs and audio models to enhance real-time voice agents' reasoning, latency, and conversational quality. Prototypes models, designs evaluations, and bridges research to production systems requiring strong PyTorch expertise and experimental mindset.
225k – 400k/yr
On-siteML Engineering
Research Scientist
LatentSan Francisco, CA
Owns end-to-end ML research initiatives developing novel architectures, training methods, and evaluation for clinical intelligence using longitudinal patient data. Requires strong ML foundation, PyTorch experience, and ability to drive ambiguous high-stakes problems to validated results.
225k – 300k/yr
On-siteML Engineering
Machine Learning Engineer
LatentSan Francisco, CA
Owns end-to-end production ML systems for clinical workflows, including training/fine-tuning LLMs for medical reasoning and question answering. Requires strong ML/software engineering, PyTorch experience, and ability to handle high-stakes ambiguity with real patient impact.
225k – 300k/yr
On-siteML Engineering
Research Engineer, Post-Training (All Industry Levels)
character.aiUnited States
Develops alignment algorithms, data pipelines, and sampling methods to optimize post-training AI models for performance and efficiency. Requires PhD or equivalent, ML expertise including reinforcement learning and transformers, and production code experience.
225k – 400k/yr
RemoteML Engineering
Machine Learning Infrastructure Engineer- Model Inference
AbridgeSan Francisco, CA
Builds and optimizes scalable ML inference infrastructure using Kubernetes and GPU resources to deploy production AI models with low latency. Collaborates with ML research and product teams on model serving, orchestration, and compute efficiency.