9d ago

AI Engineer

United States·Palo Altofull-timemid

Machine Learning EngineerData

3 views0 saves0 applied

Quick Summary

Key Responsibilities

collection, cleaning, labeling, alignment, augmentation. Optimize models for realtime serving with sub-200ms end-to-end latency: distillation, quantization, pruning, caching, streaming inference,

Requirements Summary

diffusion models, video generation, generative AI, computer vision, multimodal (audio+vision) AI, speech synthesis, neural rendering. Solid Python engineering; distributed training on multi-GPU / mul

Technical Tools

Machine Learning EngineerData

LiveX AI · AI Engineer (Machine Learning Engineer)
Palo Alto, CA · Full-time, on-site · Department: Engineering
Source: https://livex.ai/careers/ai-engineer (posted May 27, 2026)

About the Role

~1 min read

LiveX AI is building realtime, interactive AI avatars: digital humans that see, listen, and respond with natural speech and expressive motion. This engineer trains, fine-tunes, and serves the generative models behind these avatars across the full model lifecycle, from data curation and training diffusion, video, and multimodal models to low-latency inference for live streaming.

Responsibilities

~1 min read

→
Train and fine-tune generative models for talking-head synthesis, audio-driven facial animation, and full-body avatar video (diffusion models, DiT, 3D Gaussian Splatting, GAN-based, transformer-based video generators).
→
Design data pipelines for large-scale video, audio, and multimodal datasets: collection, cleaning, labeling, alignment, augmentation.
→
Optimize models for realtime serving with sub-200ms end-to-end latency: distillation, quantization, pruning, caching, streaming inference, custom CUDA/Triton kernels where needed.
→
Build and maintain training, evaluation, and deployment infrastructure on GPU clusters (PyTorch, DeepSpeed, FSDP, Ray, Kubernetes).
→
Define and track quality metrics (lip-sync accuracy, identity preservation, expression naturalness, latency, throughput) and drive measurable gains.
→
Work with backend, product, and research teams to ship avatar features and iterate on user feedback.
→
Keep up with research in generative video, multimodal AI, and neural rendering; bring useful techniques into the stack.

Requirements

~1 min read

3+ years of hands-on ML engineering, or strong research experience with production-quality code, with a record of shipping deep learning models.
Deep expertise in PyTorch and modern DL workflows; able to read, reproduce, and extend research papers.
Strong background in one or more of: diffusion models, video generation, generative AI, computer vision, multimodal (audio+vision) AI, speech synthesis, neural rendering.
Solid Python engineering; distributed training on multi-GPU / multi-node setups.
Proven realtime inference optimization: TensorRT, ONNX, Triton Inference Server, CUDA, mixed precision, streaming pipelines.
Comfortable in ambiguous, fast-changing environments; proactive, ownership-driven.
Fast-learning startup mentality; eager to push what interactive avatars can do.

Talking-head, audio-driven animation, lip-sync, or digital human systems; 3D Gaussian Splatting, NeRF, face reenactment, avatar synthesis.
Publications at top venues (NeurIPS, CVPR, ICCV, SIGGRAPH, ICLR, ICML) or notable open-source contributions.