Artificial Intelligence Engineer

Neuraci Labs is hiring an Artificial Intelligence Engineer to research, develop, train, and deploy proprietary foundation models and predictive systems. The role focuses on advanced deep learning, generative AI, knowledge graphs and retrieval systems, and scalable production ML infrastructure.

Responsibilities

Design, implement, train, and optimize neural architectures, including Graph Neural Networks (GNNs), Graph Transformers, CNNs, and attention-based Transformers

Develop multimodal representation learning frameworks integrating structured, unstructured, relational, and high-dimensional datasets

Implement self-supervised, contrastive, transfer, and multi-task learning approaches

Explore geometric deep learning, equivariant neural networks, and domain-specific foundation model architectures

Research and implement generative architectures, including diffusion models, score-based generative models, VAEs, and generative Transformers

Develop predictive and generative models for complex structured data, graphs, and continuous representations

Work with sparse, noisy, and heterogeneous datasets, addressing generalization, uncertainty, and data limitations

Explore probabilistic modeling, causal inference, and uncertainty-aware machine learning

Design knowledge graph architectures and graph-based representation learning pipelines

Develop Retrieval-Augmented Generation (RAG), GraphRAG, hybrid retrieval, semantic search, and reranking systems

Integrate LLMs, embeddings, vector databases, and structured knowledge for evidence-grounded reasoning

Implement retrieval evaluation, source attribution, factual consistency checks, and hallucination mitigation

Build scalable training pipelines using PyTorch and GPU-accelerated computing

Implement custom loss functions, optimization techniques, and distributed training strategies

Establish rigorous model evaluation frameworks, including ablation studies, cross-validation, calibration, and out-of-distribution testing

Optimize model performance, memory utilization, training efficiency, and inference latency

Develop reproducible ML pipelines, experiment tracking, model versioning, and production inference systems

Build scalable model-serving APIs and integrate trained models into larger AI platforms

Implement research papers, benchmark alternative architectures, and document experimental findings

Collaborate with interdisciplinary researchers and engineers to develop proprietary AI capabilities

Preferred

Experience with geometric deep learning, equivariant GNNs, Graph Transformers, or 3D representation learning

Experience with diffusion models, score-based generative modeling, or other advanced generative architectures

Hands-on experience with RAG, GraphRAG, knowledge graphs, vector databases, or neural-symbolic AI

Knowledge of multimodal foundation models, cross-modal attention, and domain adaptation

Experience with causal inference, Bayesian methods, uncertainty quantification, or probabilistic modeling

Familiarity with PyTorch Geometric, DGL, Hugging Face, DeepSpeed, FSDP, or equivalent frameworks

Exposure to scientific machine learning, computational biology, molecular modeling, or other research-intensive domains

Research publications, open-source contributions, or demonstrated experience developing original model architectures

Geometric deep learning

Equivariant GNNs

Graph Transformers

3D representation learning

Diffusion models

Score-based generative modeling

Generative architectures

Retrieval-augmented generation

GraphRAG

Knowledge graphs

Vector databases

Neural-symbolic AI

Multimodal foundation models

Cross-modal attention

Domain adaptation

Causal inference

Bayesian methods

Uncertainty quantification

Probabilistic modeling

PyTorch Geometric

DGL

Hugging Face

DeepSpeed

Fully Sharded Data Parallel

Scientific machine learning

Computational biology

Molecular modeling

Research-intensive domains

Research publications

Open-source contributions

Original model architecture development

Benefits

Full-time, remote opportunity

Company

Machine learning for human smell perception

New York, New York, USA

http://neuracilabs.com/

Founded in 2025

2-10 employees

Qualifications

4–8+ years of hands-on experience in AI/ML engineering, deep learning, or applied AI research

Advanced proficiency in Python and PyTorch , with strong software engineering fundamentals

Solid experience implementing and training deep learning architectures, particularly GNNs, Transformers, or CNNs

Strong understanding of representation learning, neural network optimization, and model generalization

Experience with large-scale datasets, GPU-based model training, and ML experimentation

Strong mathematical foundations in linear algebra, probability, statistics, and numerical optimization

Experience with model evaluation, benchmarking, debugging, and performance optimization

Familiarity with cloud infrastructure, Docker, Git, and production ML workflows

Ability to independently understand and implement research papers

Design and develop original AI models beyond standard API integrations

Understand the mathematical foundations and architectural trade-offs behind deep learning systems

Work independently on complex research and engineering challenges

Translate scientific research into robust, scalable AI implementations

Take ownership of the complete model lifecycle, from experimentation and validation to deployment

Experience with geometric deep learning, equivariant GNNs, Graph Transformers, or 3D representation learning

Experience with diffusion models, score-based generative modeling, or other advanced generative architectures

Hands-on experience with RAG, GraphRAG, knowledge graphs, vector databases, or neural-symbolic AI

Knowledge of multimodal foundation models, cross-modal attention, and domain adaptation

Experience with causal inference, Bayesian methods, uncertainty quantification, or probabilistic modeling

Familiarity with PyTorch Geometric, DGL, Hugging Face, DeepSpeed, FSDP, or equivalent frameworks

Exposure to scientific machine learning, computational biology, molecular modeling, or other research-intensive domains

Research publications, open-source contributions, or demonstrated experience developing original model architectures

4–8+ years of hands-on experience in AI/ML engineering, deep learning, or applied AI research.

Required skills

Represents the skills you have, click on the tags, PyTorch, Graph Neural Networks (GNNs), Transformers, Convolutional Neural Networks (CNNs), Representation Learning, Linear Algebra, Probability and Statistics, Numerical Optimization, GPU-Based Model Training, Docker

Experience

4+ years