OVA.Work
Posted 98 days agoVerified live 2d ago
AI Research Scientist
Brief overview
New York, NYHybrid
MastersOr in progress
Strong programming skills in PythonExcellent analytical and problem-solving skillsPythonSQLGitSupervised and unsupervised learningReinforcement learningRepresentation learningTransfer learningSelf-supervised learningDeep neural networksPyTorchTensorFlowLinear algebraCalculusProbabilityStatistics
About the company
OVA.Workova.work
OVA is the most advanced Automated, Intelligent, intuitive On-boarding platform for Staffing Firms of all sizes.
Job description
Summary
OVA.Work is seeking an AI Research Scientist to conduct cutting-edge research in artificial intelligence and machine learning. The successful candidate will develop algorithms, publish research, build prototypes, and translate research into production-ready AI solutions.
Responsibilities
- Conduct research in machine learning, deep learning, and artificial intelligence
- Design and develop Client AI algorithms and model architectures
- Build, train, fine-tune, and evaluate state-of-the-art AI models
- Read, analyze, and implement research papers
- Develop proof-of-concept (PoC) systems for emerging AI technologies
- Publish research findings in conferences or journals (preferred)
- Collaborate with engineering teams to transition research into production
- Evaluate model performance using appropriate benchmarks and metrics
- Stay current with advancements in AI research and emerging technologies
- Mentor junior researchers and engineers when appropriate
Skills
- Master's or Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Mathematics, or a related field
- Strong background in machine learning, deep learning, and statistical modeling
- Experience conducting research and implementing research ideas
- Strong programming skills in Python
- Excellent analytical and problem-solving skills
- Python
- SQL
- Git
- Supervised and unsupervised learning
- Reinforcement learning
- Representation learning
- Transfer learning
- Self-supervised learning
- Deep neural networks
- PyTorch
- TensorFlow
- Linear algebra
- Calculus
- Probability
- Statistics
- Optimization
- Numerical methods
- Large Language Models (LLMs)
- Natural Language Processing (NLP)
- Computer Vision
- Generative AI
- Diffusion models
- Vision-Language Models (VLMs)
- Multimodal AI
- Reinforcement Learning
- Graph Neural Networks (GNNs)
- Time-series modeling
- Prompt engineering
- Fine-tuning
- Retrieval-Augmented Generation (RAG)
- Agentic AI systems
- Synthetic data generation
- AI evaluation frameworks
- Linux
- Docker
- Distributed training
- GPU computing (CUDA)
- High-performance computing (HPC)
- AWS
- Microsoft Azure
- Google Cloud Platform (GCP)
- Strong research and analytical thinking
- Scientific writing and documentation
- Collaboration across multidisciplinary teams
- Curiosity and continuous learning
- Presentation and communication skills
- Mentoring and knowledge sharing
- C++
- JAX
- Kubernetes
- Ph.D. with publications in leading AI conferences or journals
- Experience training large-scale foundation models
- Contributions to open-source AI projects
- Familiarity with distributed machine learning and model optimization
- Experience with AI benchmarking and reproducible research
- MLOps and model deployment
- Explainable AI (XAI)
- Responsible AI and AI safety
- Federated learning
- Edge AI
- Quantum machine learning (research-oriented)
- Knowledge graph applications
- Vector databases and semantic search
Qualifications
Must Haves
- Master's or Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Mathematics, or a related field
- Strong background in machine learning, deep learning, and statistical modeling
- Experience conducting research and implementing research ideas
- Strong programming skills in Python
- Excellent analytical and problem-solving skills
- Python
- SQL
- Git
- Supervised and unsupervised learning
- Reinforcement learning
- Representation learning
- Transfer learning
- Self-supervised learning
- Deep neural networks
- PyTorch
- TensorFlow
- Linear algebra
- Calculus
- Probability
- Statistics
- Optimization
- Numerical methods
- Large Language Models (LLMs)
- Natural Language Processing (NLP)
- Computer Vision
- Generative AI
- Diffusion models
- Vision-Language Models (VLMs)
- Multimodal AI
- Reinforcement Learning
- Graph Neural Networks (GNNs)
- Time-series modeling
- Prompt engineering
- Fine-tuning
- Retrieval-Augmented Generation (RAG)
- Agentic AI systems
- Synthetic data generation
- AI evaluation frameworks
- Linux
- Docker
- Distributed training
- GPU computing (CUDA)
- High-performance computing (HPC)
- AWS
- Microsoft Azure
- Google Cloud Platform (GCP)
- Strong research and analytical thinking
- Scientific writing and documentation
- Collaboration across multidisciplinary teams
- Curiosity and continuous learning
- Presentation and communication skills
- Mentoring and knowledge sharing
Nice to Haves
- C++
- JAX
- Kubernetes
- Ph.D. with publications in leading AI conferences or journals
- Experience training large-scale foundation models
- Contributions to open-source AI projects
- Familiarity with distributed machine learning and model optimization
- Experience with AI benchmarking and reproducible research
- MLOps and model deployment
- Explainable AI (XAI)
- Responsible AI and AI safety
- Federated learning
- Edge AI
- Quantum machine learning (research-oriented)
- Knowledge graph applications
- Vector databases and semantic search
