Applied Ai Researcher (Remote)

Applied Ai Researcher | Netvagas | Brazil

We are seeking an exceptional Applied AI Researcher to join us in shaping the future of Generative Artificial Intelligence (GenAI). We are looking for a researcher with a proven track record of translating cutting-edge research into practical product solutions. As a member of our Applied Research team, you will be responsible for designing, developing, and scaling novel algorithms and models capable of handling diverse modalities such as text, images, audio, video, and time series data. You will also develop new model architectures, carry out benchmark studies, and optimize the performance metrics of current technologies. This position offers excitng opportunities to work closely with cross-functional teams and external partners to drive innovations in enterprise-grade GenAI.

Duties And Responsibilities

Serve as the subject matter expert and lead developer for key GenAI technologies, encompassing various domains such as data pipelines, training and fine-tuning, reinforcement learning, model architecture development and optimization, multi-expert systems, and multimodal models and techniques;
Play a pivotal role in conceiving, implementing, and scaling pioneering technologies through all stages, from initial brainstorming and experimentatoon to validation and deployment;
Collaborate with cross-functional teams, including Product Technology, Engineering, Quality Assurance, and Customer Success, to seamlessly incorporate innovation and maintain our product technology leadership;
Participate in high-level technical discussions, contributing to technology assessment and roadmap planning;
Continuously stay abreast of emerging trends and advancements in of GenAI and associated fields, while disseminating appropriate research results at top-tier conferences and journals;
Mentor and guide junior team members and help building a strong culture of rapid innovation.

To be successful in this role, it is essential to have knowledge and/or experience in:

PhD degree in Computer Science, Machine Learning (ML), or a related field; or alternatively, BSc/MSc Degree and experience (3+ years post BSc graduation) as a practcing researcher;
Proven track record as a researcher working in the design and implementation of ML/AI models and algorithms aimed at solving complex, real-world problems;
A strong background in parallel/distributed computing (preferably on the cloud);

Core Technical Skills

Machine Learning: A solid understanding of machine learning algorithms, neural networks, and deep learning techniques. Familiarity with popular frameworks such as PyTorch and/or TensorFlow;
Probability Theory and Statistics: Knowledge of probability theory and statistical concepts. Including topics such as random variables, distributions, Bayesian methods, hypothesis testing, and confidence intervals;
Mathematics: Strong foundations in algebra, calculus, optimization, graph theory, and numerical methods;
Natural Language Processing (NLP): Expertise with techniques used in text preprocessing, tokenization, part-of-speech tagging, parsing, sentiment analysis, topic modeling, and word embeddings;
Computer Vision: Strong foundations in techniques used in tasks such as include object detection, segmentation, optical flow estimation, feature extraction, tracking, and image processing;
Data Wrangling and Preparation: Expertise in handling large datasets, data cleaning, normalizing, transforming, and preparing them for model training;
Model Evaluation and Interpretation: Ability to assess model performance, compare different models, interpret results, and identify potential issues. Understanding evaluation metrics, bias-variance tradeoff, overfitting, underfitting, regularization techniques, and hyperparameter tuning;
Programming Skills: Proficiency in programming languages such as Python and experience working with version control systems (e.g., Git) and collaborating on code repositories is crucial;
Proven track record of publications in top-tier conferences and journals.

It would be nice/it s a plus if you know/have experience in:

Masters or PhD in Computer science or related technical fields;
3+ years of hands-on experience as an applied ML/AI researcher;
Experience developing tools, libraries, and infrastructure for data preprocessing, model training/finetuning, and deployment of LLMs in research and production environments;
Experience with cloud computing platforms such as AWS, Azure, or GCP.

Benefits