AI Engineer

Embedding VC

. Train and fine-tune generative models for talking-head synthesis, audio-driven facial animation, and full-body avatar video .

Posted 10/23/2026full-timePalo Alto • California • United StatesMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in training and optimizing generative models for video and audio applications, with a strong focus on real-time inference and quality metrics. Proficient in building and maintaining infrastructure for deep learning workflows and collaborating across teams to enhance avatar technology.

Highest-signal resume keywords

Deep Learning EngineeringPyTorch ExpertiseReal-Time Inference OptimizationDistributed TrainingGenerative AI Knowledge

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

Machine Learning EngineeringPython ProgrammingModel OptimizationData Pipeline DesignQuality Metrics DefinitionNeural RenderingVideo GenerationSpeech SynthesisDiffusion ModelsMultimodal AI

Soft Skills

Proactive OwnershipFast-LearningAdaptability

Tools & Technologies

CUDATriton Inference ServerTensorRTONNXMulti-GPU Setup

Industry Keywords

Generative ModelsTalking-Head SynthesisAudio-Driven Facial AnimationAvatar TechnologyDeep Learning Workflows

Tech Stack

Tools & technologies

Node.jsPythonPyTorch

About the role

Key responsibilities & impact

Train and fine-tune generative models for talking-head synthesis, audio-driven facial animation, and full-body avatar video
Design data pipelines for large-scale video, audio, and multimodal datasets, including collection, cleaning, labeling, alignment, and augmentation
Optimize models for real-time serving with sub-200ms end-to-end latency using distillation, quantization, pruning, caching, streaming inference, and custom CUDA/Triton kernels where needed
Build and maintain training, evaluation, and deployment infrastructure on GPU clusters
Define and track quality metrics including lip-sync accuracy, identity preservation, expression naturalness, latency, and throughput
Collaborate with backend, product, and research teams to ship avatar features and iterate based on user feedback
Monitor research in generative video, multimodal AI, and neural rendering and apply useful techniques to the technology stack

Requirements

What you’ll need

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 deep learning workflows
Ability 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 skills
Experience with distributed training on multi-GPU and multi-node setups
Proven real-time inference optimization experience with TensorRT, ONNX, Triton Inference Server, CUDA, mixed precision, and streaming pipelines
Comfortable in ambiguous, fast-changing environments; proactive and ownership-driven
Fast-learning startup mentality; eager to advance interactive avatar capabilities

Benefits

Comp & perks

Equity
Benefits
Access to substantial GPU compute