Senior ML Engineer

Responsibilities

Take ownership of the end-to-end machine learning (ML) delivery cycle, including building, testing, deploying, and supporting solution components
Lead the design of complex ML systems from scratch, considering architectural aspects, user needs, and non-functional requirements
Transform business goals into data science (DS) problems and define relevant proxy metrics and non-functional requirements
Discover and verify business scenarios that can be solved with technical tools and solutions, contributing significantly to the experiment design process
Manage issues from root cause to resolution, providing feedback to improve engineering design and prevent future issues
Create and maintain DS-powered services in a production environment, collaborating with other teams and contributing to the backend systems and infrastructure
Drive automation and track performance and efficiency metrics
Mentor and onboard junior team members, supporting a culture of continuous learning and best practices
Communicate complex technical messages clearly and concisely to diverse audiences
Proactively identify and report potential security, risk, and control issues
Drive continuous improvement and innovation that leads to business impact

Comprehensive experience autonomously implementing and leading ML projects, with a proven track record of successes and lessons learned
Expert-level proficiency in classic machine learning, deep learning, and advanced mathematics
Strong practical knowledge of MLOps instruments for managing the ML model lifecycle
Solid software system design skills to contribute to overall architecture, and the ability to design ML systems from scratch
In-depth experience with event systems and deployment environments, and the ability to maintain services in production
Proficiency in Python and its frameworks for streaming, batch, and async data processing
Common knowledge of technologies for backend integration (e.g., Golang)
A strong grasp of concepts like Concept Drift and its impact on model performance in production
A strong understanding of data preparation and calculations at all stages of the ML pipeline

Stable salary, official employment
Health insurance
Hybrid work mode and flexible schedule
Relocation package offered for candidates from other regions
Access to professional counseling services including psychological, financial, and legal support
Discount club membership
Diverse internal training programs
Partially or fully payed additional training courses
All necessary work equipment