Benefits:

• Comprehensive health, dental, and vision insurance

• A modern office environment

• Industry events

Experience Requirements:

• Minimum of 5 years of hands-on experience in machine learning model development

Other Requirements:

• Expertise in fine-tuning, reinforcement learning, and prompt-engineering techniques

• Experience in working with multimodal or vision models

• Ability to communicate complex research findings in a clear and impactful manner

• A knack for solving problems creatively and effectively

Responsibilities:

• Lead cutting-edge research in AI & Agents, focusing on breakthroughs in long-term memory, reliability, planning, reasoning, and embodied AI, and transition these innovations into production and running models on device

• Spearhead and implement the latest research ideas and ship them to production, focusing on on-device LLMs & fast model inference to enhance AI agent capabilities and account for user privacy & security

• Collaborate with our product and engineering teams to transform research insights into practical, impactful solutions

• Stay abreast of the latest advancements in AI, especially around local models, LLMs, fine-tuning techniques, and inference and optimization

• Employ rigorous testing and iterative improvement processes to optimize enhance the model performance and reliability

Benefits:

• Be at the cutting edge of generative AI technology

• Enjoy the perks of full remote work and flexible hours

• Learn from top-tier leadership

• Join our dynamic, international team

• Chart a clear career trajectory

Education Requirements:

• Master's degree in Computer Science, AI, Deep Learning, or related field

Experience Requirements:

• Minimum 5 years of industry experience

Responsibilities:

• Develop and enhance note-creation processes

• Build interfaces for clinical researchers

• Design and conduct experiments

• Implement and refine Question Answering and Knowledge Retrieval technologies

Benefits:

• Competitive Total Compensation Package

• Merit-based Annual Raises

• Excellent Mentorship Program & Exceptional Training Programs

• Opportunities for Professional Growth & Advancement

• Comprehensive Benefits including Health, Dental, & Vision Insurance

Education Requirements:

• High School Diploma

Other Requirements:

• Must clear a current background check through the FBI and DOJ

• A current TB Test

• Reliable transportation and hold appropriate automobile insurance with limits that allow driving a personal vehicle for work (25/50k liability coverage)

Responsibilities:

• Full ownership of data science direction and capabilities

• Improving the product from the data layer up

• Bringing scientific rigor and statistical methods to product creation, development, and improvements

• Appreciation for user experience

Benefits:

• Stock options/Equity plan

• MacBook & equipment

• Hybrid & remote work

• Conferences & events attendance

• Book allowance

Benefits:

• Stock options/Equity plan

• MacBook & equipment

• Hybrid & remote work

• Conferences & events attendance

• Book allowance

Benefits:

• Stock options/Equity plan

• MacBook & equipment

• Hybrid & remote work

• Conferences & events attendance

• Book allowance

Benefits:

• Interesting tasks in a successful and growing startup

• Thorough and individual training in the team

• Nice colleagues

• Insights into future-oriented solutions for the tourism industry

• Redundant high-end caffeine supply

Education Requirements:

• Studying tourism or comparable training

Experience Requirements:

• Digital spirit

• Enthusiasm for new technologies

• At least 3 months of time

Responsibilities:

• Helping to train and improve the artificial intelligence

• Using human intelligence and tourism expertise

• Analyzing travel preferences and deriving recommendations

Experience Requirements:

• Interest for NLP, Machine Learning and Data Science

• Basic knowledge of Python

• Basic knowledge of MySQL or MongoDB

• Curiosity, Autonomy, Creativity

• Problem-solving attitude

Other Requirements:

• Working language is EN, knowledge of FR and/or NL is a clear plus. Partial remote work is possible but weekly presence in Brussels office is mandatory

Responsibilities:

• Learn and develop new algorithms and technologies to improve our matchmaking engine

• Develop evaluation processes for quality improvement

• Identify valuable data sources

• Transform large unstructured dataset into consumable information

Education Requirements:

• Bachelor’s or Master’s degree in Computer Science, Data Science, Applied Mathematics, Statistics, Linguistics, or a related field

Experience Requirements:

• 2+ years of experience in Data Science, focusing on NLP, prompt engineering, or deep learning

Other Requirements:

• Proficiency in Python and familiarity with AI frameworks and libraries (e.g., TensorFlow, PyTorch, Hugging Face)

• Strong understanding of NLP techniques and machine learning principles, including sentiment analysis, word embedding, text classification, and Transformer models (e.g., GPT, BERT)

• Experience with developing, optimizing, and evaluating prompts to improve AI model responses

• Experience with large language models (e.g., GPT-4, Llama3) and their applications in customer service or other domains

• Knowledge of data visualization tools and techniques to communicate complex data insights effectively

• Background in linguistics or computational linguistics

• Experience with agile development methodologies and big data platforms like Hadoop or Spark

• Excellent analytical, problem-solving, and communication skills

• Ability to work collaboratively in a team environment, manage multiple projects simultaneously, and document complex processes effectively

• Self-motivated with a passion for continuous learning and innovation in AI

Responsibilities:

• Prompt Engineering and Development

• Model Design and Implementation

• Benchmarking and Evaluation

• Collaboration and Communication

• Documentation and Research

Education Requirements:

• Bachelor’s or Master’s degree in Applied Mathematics, Statistics, Computer Science or another quantitative field

Experience Requirements:

• 2+ years of experience in Data Science or a related field, with a focus on building NLP/LLMs or Deep Learning models

Other Requirements:

• Strong programming skills in Python or other relevant programming languages

• Experience with machine learning libraries such as TensorFlow, PyTorch, or Keras

• Experience working with a variety of statistical models, including logistic regression, clustering, classification, SVMs, neural networks, Random Forest, CRF, Bayesian models, supervised/unsupervised learning, etc.

• Expertise in NLP techniques, including sentiment analysis, word embedding, part-of-speech (POS) tagging, topic modeling, text classification, machine translation, speech recognition, named entity recognition (NER), natural language generation (NLG), and other related techniques

• Experience with various deep learning techniques, including CNNs and RNNs, and a strong understanding of building and training these models for different applications. Familiarity with LMs and LLMs such as GPT, BERT, and Transformer models is highly desirable.

• Strong ability to rapidly comprehend and implement research papers related to AI, as well as remain informed of the latest advancements in NLP technologies

• Deep knowledge and experience in structured and unstructured data Information Extraction, Knowledge Information Retrieval, and Knowledge Representation

• Self-motivated and driven to satisfy intellectual curiosity through the pursuit of continuous learning and skill development

• Strong problem-solving and analytical skills

• Excellent communication and collaboration abilities

• Optional: Experience with big data platforms such as Hadoop or Spark

Responsibilities:

• Design and implement machine learning algorithms for NLP/LLMs or deep learning models at scale to meet business requirements

• Analyze large datasets and develop models that can be used to make predictions, generate responses or summaries, identify patterns etc.

• Develop and improve existing models to increase accuracy, efficiency, and effectiveness

• Work closely with cross-functional teams to identify and prioritize business problems, and develop solutions to address them

• Communicate findings and insights to stakeholders in a clear and concise manner

Experience Requirements:

• 3+ years of experience as a data scientist, preferably in a product development environment, with a focus on building NLP/LLMs or Deep Learning models

Other Requirements:

• Strong programming skills in Python or other relevant programming languages

• Experience with machine learning libraries (e.g., scikit-learn, TensorFlow, PyTorch)

• Deep understanding of statistical analysis, probability theory, and experimental design

• Experience with LLMs, deep learning, NLP and chatbot development

• Excellent communication skills, including the ability to explain complex concepts to technical and non-technical stakeholders

• Experience working with a variety of statistical models, including logistic regression, clustering, classification, SVMs, neural networks, Random Forest, CRF, Bayesian models, supervised/unsupervised learning, etc.

• Expertise in NLP techniques, including sentiment analysis, word embedding, part-of-speech (POS) tagging, topic modeling, text classification, machine translation, speech recognition, named entity recognition (NER), natural language generation (NLG), and other related techniques

• Experience with various deep learning techniques, including CNNs and RNNs, and a strong understanding of building and training these models for different applications. Familiarity with LMs and LLMs such as GPT, BERT, and Transformer models is highly desirable.

• Strong ability to rapidly comprehend and implement research papers related to AI, as well as remain informed of the latest advancements in NLP technologies

• Deep knowledge and experience in structured and unstructured data Information Extraction, Knowledge Information Retrieval, and Knowledge Representation

• Self-motivated and driven to satisfy intellectual curiosity through the pursuit of continuous learning and skill development

• Strong problem-solving and analytical skills

• Optional: Experience with large-scale data processing technologies (e.g., Hadoop, Spark) and distributed computing systems

Responsibilities:

• Design, develop and deploy machine learning models and algorithms for NLP/LLMs or deep learning models at scale to solve complex business problems

• Work with large datasets, perform data analysis and develop data pipelines to support model development

• Collaborate with Product & Engineering teams to integrate machine learning solutions into products and services

• Conduct experiments, test hypotheses, and perform statistical analysis to validate models and drive improvements

• Communicate findings and insights to stakeholders in a clear and concise manner

Education Requirements:

• Bachelor’s or Master’s degree in Computer Science, Data Science, or a related field

Experience Requirements:

• Proven 5+ years of experience as an AI Analyst or similar role with a focus on data analytics and AI integration

Other Requirements:

• Experience in developing and deploying generative AI models is an advantage

• Good understanding of Prompt writing and evaluation

• Proficiency in Python and relevant AI libraries/frameworks (e.g., TensorFlow, PyTorch)

• Strong understanding of machine learning algorithms and natural language processing

• Solid experience with SQL for data manipulation and analysis

• Strong analytical and problem-solving abilities to derive actionable insights from complex datasets

• Excellent communication skills to translate technical concepts for varied audiences

• Proven ability to work effectively in cross-functional teams, promoting innovation and continuous improvement

• Capability to thrive in a fast-paced, dynamic environment while proactively identifying AI-driven improvements in chatbot functionalities

Responsibilities:

• Requirement Analysis

• Data Exploration and Analysis

• Feasibility Assessment

• Model Selection and Design

• Implementation and Deployment