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About the role

As a Machine Learning Engineer you will design, develop, and implement machine learning models and systems that can analyze and interpret data, make predictions, and automate decision-making processes. You will help us turning data into actionable insights and deploying machine learning solutions into our software.

Minimum qualifications

• Bachelor's in Computer Science, Engineering, or related field.
• 1-2 years of industry experience as a Machine Learning Engineer, with a focus on Large Language Models (LLMs).
• Proficiency in programming languages such as Python and experience with relevant libraries (TensorFlow, PyTorch, Hugging Face Transformers, etc.).
• Knowledge of text2SQL
• Strong understanding of natural language processing (NLP) concepts, techniques, and applications.
• Proven track record of developing and deploying LLMs for various NLP tasks (e.g., text generation, sentiment analysis, named entity recognition, etc.).
• Experience with model training, fine-tuning, and optimization using large-scale datasets.
• Solid understanding of machine learning fundamentals, deep learning architectures, and evaluation metrics.
• Knowledge of cloud platforms (AWS) and experience deploying models in production environments is a plus.
  

Preferred qualifications

• Experience in the payments, banking or accounting sector is a huge plus
• Experience with distributed computing and parallel processing for LLM training.
• Familiarity with continuous integration and deployment (CI/CD) pipelines for ML models.
• Knowledge of Docker, Kubernetes, and other containerization technologies.

Responsibilities

• Identifying opportunities where machine learning techniques can add value and solve problems.
• Defining clear objectives and success criteria for machine learning projects.
• Acquiring, cleaning, and preparing relevant datasets for training and evaluation.
• Exploring and analyzing data to gain insights and understand patterns.
• Selecting and transforming relevant features from the data to improve model performance.
• Choosing appropriate machine learning algorithms and architectures based on the problem and data characteristics.
• Implementing training pipelines and frameworks to train models on large datasets.
• Monitoring model performance and behavior in real-world scenarios.
• Detecting and addressing issues related to model drift, bias, or degradation over time.
  

Benefits:

• Well-funded and proven startup with large ambitions and competitive salaries.
• Entrepreneurial culture where pushing limits, creating and collaborating is everyday business.
• Open communication with management and company leadership
• Small, dynamic teams = massive impact
• 100% Remote Work (You choose where to work from)
• 500USD a year for you to invest in learning.

Simetrik considers qualified applicants for employment without regard to race, gender, age, color, religion, national origin, marital status, disability, sexual orientation, gender identity/expression, protected military/veteran status, or any other legally protected factor.

Join a team of incredibly talented people that build things, are free to create and love collaborating!