Benefits:

401(k)

401(k) matching

Competitive salary

Dental insurance

Health insurance

Opportunity for advancement

Paid time off

Relocation bonus

Vision insurance

Wellness resources

Petlibro | San Jose, CA

About Petlibro

Petlibro is a design-thinking company creating products that nurture the intertwined lives of pets and their people. We launched with a philosophy that good design, in form and in function, can make a difference. Petlibro innovates with the latest technology to solve everyday problems for modern pet parents and revolutionize how we care for our pets.

Since 2019, Petlibro has grown into one of the best-selling pet tech brands globally. From smart feeders with app insights to ultra-filtered automatic fountains to pet-health-focused smart apps, our products are engineered to magnify the bond between your pet and you. We are now building AI-powered products and services for modern pet care.

Job Summary

We are looking for an AI Engineer to design, train, and deploy AI models that power intelligent pet care and animal monitoring products. You will work across the full model lifecycle, from data curation and fine-tuning to production serving and on-device deployment. The role spans computer vision, vision-language models (VLMs), large language models (LLMs), and agentic AI systems, with a strong focus on animal behavior understanding and multimodal reasoning.

Key Responsibilities

Fine-tune LLMs and VLMs for pet care and animal behavior domains using modern post-training methods (SFT, DPO, GRPO/DAPO)

Develop and evaluate computer vision models for animal detection, pose estimation, activity recognition, and health monitoring

Build multimodal AI pipelines that combine video, audio, weight sensor data, and other sensor modalities for real-time animal behavior analysis

Design and operate model serving infrastructure with high throughput and low latency (vLLM, TensorRT-LLM)

Implement RAG systems and agentic workflows for domain-specific knowledge retrieval and automated decision-making

Optimize models for on-device / edge deployment using quantization (GPTQ, AWQ, INT4/INT8), distillation, etc.

Curate and manage training datasets including synthetic data generation and annotation pipelines for animal imagery and video

Develop evaluation frameworks and benchmarks specific to animal AI tasks (behavior classification accuracy, false alert rates, etc.)

Collaborate with firmware and embedded engineers to ship models on resource-constrained hardware

Monitor model performance in production, set up drift detection, and maintain model versioning and rollback processes

Qualifications & Skills

Bachelor's or Master's degree in Computer Science, AI/ML, Electrical Engineering, or a related field

2+ years of hands-on experience training and deploying ML models in production

Strong proficiency in Python; familiarity with C++ for performance-critical paths

Deep experience with PyTorch and the Hugging Face ecosystem (Transformers, PEFT, TRL, Datasets)

Practical experience with the modern post-training pipeline: SFT, preference optimization, and RL-based methods

Understanding of modern model architectures: Mixture of Experts, long-context models, multimodal encoders

Experience with inference optimization: quantization, speculative decoding, KV-cache management, batching strategies

Familiarity with MLOps tooling: experiment tracking (W&B, MLflow), model registries, CI/CD for ML

Knowledge of cloud GPU infrastructure (AWS, GCP) and cost-efficient training/serving strategies

Strong problem-solving skills and ability to work in a fast-paced, cross-functional environment

Background in animal behavior, veterinary science, or bioinformatics is a strong plus

Nice to Have

Experience with video understanding models and temporal reasoning over long sequences

Hands-on experience building agentic AI systems or tool-using LLM workflows

Experience with structured outputs, function calling, and LLM-as-judge evaluation patterns

Familiarity with on-device ML frameworks (Core ML, TensorFlow Lite, ONNX Runtime, ExecuTorch)

Experience with synthetic data generation for training data augmentation

Understanding of distributed training (FSDP, DeepSpeed) for large-scale model training

Knowledge of AI safety, alignment techniques, and responsible AI practices

Experience with Docker, Kubernetes, and production deployment pipelines

Why Join Us

Work directly on AI that improves animal welfare and pet care. Your models have real-world impact on pets and their owners

Full-stack AI role, from research and training to production serving and on-device deployment

Access to GPU compute and cloud infrastructure for experimentation and training

Collaborative team that values shipping over slides

Competitive salary, equity, and benefits

We're building the future of intelligent pet care. If you want to push the boundaries of multimodal AI and apply it to a domain that genuinely matters, we want to talk.