Senior Applied AI Research Engineer (R&D)

About the Role

Our client is seeking a highly skilled and research-driven Senior Applied AI Research Engineer to join our Research & Development (R&D) team.

We are looking for an engineer who combines strong theoretical foundations in Artificial Intelligence, Machine Learning, and Deep Learning with practical experience in researching, developing, training, and optimizing advanced AI models.

The ideal candidate will have a demonstrated ability to explore emerging research, understand complex algorithms, design experiments, and translate research concepts into functional AI implementations.

This role focuses on hands-on AI research, model development, and experimentation, particularly across areas such as Computer Vision, Large Language Models (LLMs), Supervised Fine-Tuning (SFT), Reinforcement Learning (RL), and advanced Deep Learning techniques.

Unlike traditional AI application development roles, this position emphasizes research and development at the model and algorithm level, rather than primarily integrating existing AI APIs or frameworks into applications.

AI Research & Experimentation

• Conduct applied research in Artificial Intelligence, Machine Learning, Deep Learning, Computer Vision, NLP, and emerging AI technologies.
• Review, analyze, reproduce, and implement methodologies from academic research papers and open-source research projects.
• Design and execute experiments to evaluate AI models, algorithms, training methodologies, and optimization techniques.
• Investigate existing approaches, identify limitations, and experiment with alternative architectures and algorithms to improve model performance.
• Conduct comparative benchmarking, performance evaluations, and ablation studies to validate research findings.
• Document research methodologies, experimental results, and technical findings to support knowledge sharing and continuous improvement.

Deep Learning & Model Development

• Design, develop, train, fine-tune, and optimize deep learning models using frameworks such as PyTorch or TensorFlow.
• Work with neural network architectures, including CNNs, Transformers, Vision Transformers, and other advanced deep learning architectures.
• Develop and optimize model training pipelines, including dataset preparation, preprocessing, augmentation, training, validation, and evaluation.
• Experiment with training strategies, loss functions, optimization algorithms, and model architectures to improve accuracy, efficiency, and generalization.
• Investigate and implement model optimization techniques to improve inference performance, computational efficiency, and resource utilization.

LLM Training, Fine-Tuning & Reinforcement Learning

• Research and implement techniques for adapting and fine-tuning Large Language Models and other foundation models.
• Explore model training and post-training techniques, including Supervised Fine-Tuning (SFT), parameter-efficient fine-tuning (LoRA/QLoRA), and preference optimization.
• Conduct research and experimentation with Reinforcement Learning algorithms such as PPO, DDPG, SAC, TRPO, or related approaches.
• Explore advanced LLM post-training and reinforcement learning methodologies, including RLHF, GRPO, and reward-based optimization.
• Evaluate models through relevant benchmarks, quantitative metrics, and structured experimentation.

Candidates are not expected to possess advanced expertise in every specialization, but should demonstrate strong hands-on expertise in at least one relevant research area.

Computer Vision & Applied AI

• Research and implement Computer Vision and image-processing techniques, including object detection, image classification, segmentation, OCR, and visual recognition.
• Experiment with advanced vision models, multimodal architectures, and transformer-based approaches.
• Develop and evaluate deep learning models for complex real-world problems involving visual, textual, or multimodal datasets.
• Research techniques for model compression, inference optimization, and deployment across different computing environments.

Research Implementation & Prototyping

• Translate research findings into functional prototypes, proof-of-concepts, and reusable AI components.
• Write efficient, maintainable Python code for model development, training, and experimentation.
• Collaborate with AI researchers, ML engineers, and software engineering teams to evaluate the practical applications of emerging research.
• Identify opportunities to improve existing models or introduce new methods through systematic experimentation.
• Maintain reproducibility of research experiments through appropriate documentation, version control, and experiment tracking.

Requirements

Required Qualifications & Experience

• Minimum 5 years of relevant professional experience in Artificial Intelligence, Machine Learning, Deep Learning, Applied AI Research, or related engineering disciplines.
• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Robotics, Mathematics, Electrical Engineering, or a related technical field.
• Demonstrated hands-on experience in AI/ML research, experimentation, model development, or algorithm implementation.
• Strong proficiency in Python and hands-on experience with deep learning frameworks such as PyTorch, TensorFlow, or JAX.
• Strong understanding of machine learning algorithms, deep learning architectures, neural networks, optimization techniques, and model evaluation.
• Practical experience training, modifying, fine-tuning, or optimizing machine learning or deep learning models.
• Experience in at least one of the following areas:
• Computer Vision and deep learning-based image analysis.
• Large Language Models, NLP, model training, or fine-tuning.
• Reinforcement Learning and algorithmic experimentation.
• Multimodal AI, transformer architectures, or advanced model optimization.
• Ability to understand academic research papers and implement or reproduce relevant algorithms and methodologies.
• Experience designing experiments, benchmarking model performance, and interpreting quantitative results.
• Strong analytical thinking, problem-solving skills, and the ability to independently investigate complex technical challenges.
• Proficiency in Git and familiarity with collaborative software development and research workflows.

Preferred Qualifications

• Master's degree or PhD in Artificial Intelligence, Machine Learning, Computer Science, Robotics, or a related research-intensive discipline.
• Previous experience working in an AI research laboratory, applied research team, or R&D-focused engineering environment.
• Experience implementing or reproducing methodologies from published AI/ML research papers.
• Hands-on experience with LLM fine-tuning, SFT, LoRA, QLoRA, RLHF, GRPO, or other post-training methodologies.
• Experience with Reinforcement Learning frameworks, algorithms, and experimental environments.
• Familiarity with Hugging Face Transformers, PEFT, TRL, Weights & Biases, or similar ML research tooling.
• Experience working with GPU-based model training, distributed training, inference optimization, or model compression.
• Experience with synthetic dataset generation, data augmentation, or multimodal model development.
• Contributions to AI/ML research publications, open-source projects, or independently implemented research experiments.
• Experience translating research concepts into functional prototypes or practical AI solutions.

What Makes an Ideal Candidate?

We are particularly interested in candidates who:

• Have a genuine interest in AI research and continuously explore emerging research developments.
• Can independently read a research paper, understand the proposed methodology, and implement a working version.
• Have personally trained, fine-tuned, modified, or optimized machine learning or deep learning models.
• Can demonstrate measurable improvements achieved through experiments, algorithm modifications, or model optimization.
• Possess strong engineering capabilities alongside research-oriented thinking.
• Are comfortable experimenting with unfamiliar architectures, debugging complex model-training challenges, and testing alternative approaches.
• Can bridge the gap between academic AI research and real-world technical implementation.

Important: This is a research-focused AI engineering role. While experience developing AI-powered applications, RAG systems, AI agents, and LLM integrations is valuable, candidates must demonstrate hands-on experience in model-level research, training, experimentation, or optimization.

Benefits

Join an R&D-focused engineering environment where you will have the opportunity to explore emerging AI technologies, experiment with advanced machine learning techniques, and contribute to the research and development of innovative AI capabilities.

The role offers opportunities to work across multiple AI research domains, collaborate with technically strong engineers, and translate complex research concepts into practical implementations.