Remote Machine Learning Engineer

Summary

The job description is for a Machine Learning Engineer position at Appsilon, a software house and consultancy specializing in machine learning. The role involves data preparation, exploratory data analysis, modeling, pipeline setup and improvements, handling meetings with clients, and presenting at conferences. The required qualifications include 2+ years of experience in a similar role, strong software engineering background, extensive Python knowledge, experience with PyTorch, data wrangling, machine learning pipelines, MLOps practices, understanding of advanced and classical machine learning, trained analytical thinking, good English skills, independence, reliability, curiosity, openness to remote work, and a willingness to closely collaborate.

Requirements

• 2+ years of experience in a similar role
• Great software engineering. background
• Extensive Python. knowledge
• Experience with PyTorch
• Experience in data wrangling
• Experience with machine learning pipelines. and experiment reproducibility
• Knowledge of MLOps practices
• Understanding of advanced machine learning (multi-modal training, the influence of batch preparation on training, learning rate schedule, leveraging feature maps for new tasks, etc.)
• Experience in classical machine learning (e.g., tree-based models) as well as deep learning (e.g., computer vision)

Responsibilities

• Preparing data
• Collect data from source (facilitate a transfer of a large static dataset or connect to a live database source)
• Transform data to prepare for modeling (e.g., resize images, convert formats, obtain labels)
• EDA - exploratory data analysis
• Understand relevant data properties (e.g., class distributions in the proposed train-test split are skewed)
• Data issues (e.g., images taken at night of fast-moving objects are blurred)
• Dataset characteristics (similarities and differences with known datasets)
• Visualize the findings
• Pick or design appropriate model architecture
• Pick or define a custom set of losses
• Run, monitor, and track models’ training
• Investigate models’ performance, identify strong and weak points
• Make sure the data and modeling pipelines are modular and reproducible
• Write code that can easily be returned to and reused in new contexts
• Setup cloud infrastructure needed for data storage, modeling, and serving the models
• Setup and help maintain MLOps practices
• Handle meetings with clients
• Effective communication at both technical and stakeholder levels
• Ability to drill down on client needs and pain points to collect or refine project requirements
• Present at conferences, meetups, webinars
• Some technical-expert-type involvement in sales

Benefits

• Competitive salary
• Generous paid time off
• Comprehensive sick leave
• Professional development budget
• Flexible remote work
• Training and conferences
• Health benefits
• Fitness perks
• Life insurance
• Personal assistant
• The opportunity to join academic projects with high impact