Machine Learning Engineer
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.
- 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.
- 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.
- 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.
- 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!