Machine Learning Engineer

You’ll join a modular, data-intensive platform that optimizes large-scale operations (e.g., energy/transport scheduling) via mathematical optimization. The system streams high-quality telemetry, schedules, costs, and alerts – creating ground for ML that enhances forecasting, risk assessment, and decision support. No ML is in production yet; you’ll help design and ship the first wave.

Responsibilities

• Translate business/optimization needs into ML problems

• Design and own end-to-end data/feature pipelines

• Build, validate, and productionize models for forecasting, classification, and anomaly detection on operational data

• Define evaluation metrics and experimentation loops (offline simulation, A/B tests, post-deployment monitoring)

• Integrate models into microservices and optimization workflows (batch/real-time inference, APIs, containers)

• Establish MLOps foundations: versioning, CI/CD, monitoring, and drift detection.

• Collaborate closely with domain/optimization engineers and stakeholders to ensure models drive measurable impact

Personal Profile Overview

• Comfortable owning the full ML lifecycle (problem framing, data, model, deploy, observe)

• Degree in Data Science, Computer Science, Software Engineering or related field

• Stability in previous employment history with a tendency to remain with employers for extended periods

• Experience in managing diverse project activities (not just coding, but also requirements analysis, preparing estimations)

• Clear and effective communication skills, both verbal and written, and ability to convey ideas, information, and messages accurately and efficiently

• Proficiency in fostering effective collaboration and teamwork activities

• Ability to analyze information, assess situations, and make decisions based on sound reasoning and logical evaluation

• Focus on delivering exceptional customer experiences and prioritizing customer satisfaction

• Analytical thinking, problem-solving abilities, and strategic approach to technical challenges

• Transparency in sharing information within a team and company

• Willingness to acquire new knowledge and insights to enhance professional growth and performance

Required skills

• 3+ years of hands-on experience in applied machine learning, particularly with structured and time-series data

• Experience developing models for forecasting, classification, or anomaly detection in real-world production systems

• Strong experience with time-series modeling and feature engineering, including resampling, time-aligned joins, and handling irregular or sparse data

• Proficiency in error analysis and debugging ML models, including use of metrics like MAE, RMSE, F1, ROC-AUC, and calibration curves

• Understanding of model explainability tools and techniques (e.g., SHAP, permutation importance, feature contribution tracking)

• Experience with ML versioning and reproducibility tools (e.g., MLflow, DVC, Weights & Biases)

• Familiarity with cloud-based ML infrastructure (AWS/GCP/Azure).

• English Upper-Intermediate (B2)

As a plus

• Experience with energy/EV charging domain

• Experience with reinforcement learning in optimization settings

We offer

• Fuel your professional growth with paid online courses, conferences, certifications, English classes, a corporate library, and leadership program

• Thrive in a culture of trust and cooperation with no time trackers and minimal bureaucracy

• Enjoy 20 business days of paid vacation, plus state holidays to prioritize your well-being

• Experience an open-door culture, transparent communication, and top management at a handshake distance

• Enjoy comfortable office vibes with no open space policy, relaxing sports areas, a spacious bar/kitchen, and more

• Achieve balance with our hybrid/fully remote work model

• Receive fair and competitive compensation

• Fuel your productivity and foster a sense of community with complimentary daily lunches

• Participate in meaningful initiatives supporting Ukraine’s victory

• Take flexible sick leave without burdensome documentation and access parental benefits

• Choose from comprehensive medical insurance or a sports compensation package

• Have fun with regular team-building activities, corporate events and celebrations, and unique initiatives like Week in Lviv