Senior ML Engineer

This is a fully remote position, open to applicants in Macedonia.

📋 Description

• Manage the entire production lifecycle of machine learning systems, which includes training, packaging, deployment, monitoring, and controlled rollouts.

• Design, construct, and continuously enhance training and retraining pipelines that involve data ingestion, labeling/QA loops, feature generation, experiment tracking, and reproducibility.

• Develop and refine high-performance inference services and deployment strategies.

• Lead inference optimization efforts utilizing ONNX/TensorRT, quantization, batching, and GPU/CPU tuning to achieve tangible improvements in latency and cost.

• Collaborate with DevOps/SRE teams to guarantee scaling, reliability, incident readiness, observability, and alerting.

• Manage model versioning, CI/CD, and release processes to ensure reproducibility and secure production delivery.

• Enhance data and model monitoring for performance, drift, and quality, implementing feedback loops for ongoing improvements.

• Articulate results and trade-offs to stakeholders, translating technical achievements into business value.

• Ensure model quality through comprehensive evaluation and production-grade validation that aligns with changing data and business requirements.

⛳️ Requirements

• Master’s, PhD, or equivalent experience in Computer Science, Engineering, Data Science, or a related field, with a solid foundation in mathematics, statistics, and machine learning.

• Over 6 years of experience in machine learning engineering and MLOps/production ML, or comparable experience.

• Demonstrated history of delivering high-impact systems in production environments.

• Expertise in deploying models to production, which includes scalable serving patterns, model packaging, inference optimization, observability, and safe rollout strategies.

• Experience in optimizing model performance and efficiency, including latency/throughput tuning, batching, quantization, ONNX/TensorRT, GPU/CPU optimization, memory, and cost tuning.

• Strong proficiency in Python programming.

• Practical experience with PyTorch, scikit-learn, ONNX, and TensorRT.

• Solid understanding of CI/CD practices for machine learning, including GitLab CI, Docker, and reproducible workflows.

• Experience with monitoring and reliability practices, encompassing metrics, logging, tracing, model/data drift, alerting, SLOs, and incident response protocols.

• Familiarity with Kubernetes, AWS, FastAPI, or similar Python serving frameworks, SageMaker, service meshes, feature stores, and model registries.

🏝️ Benefits

• Competitive salary and performance-based bonuses.

• Comprehensive health, dental, and vision insurance.

• Flexible work hours and the option for remote work.

• Opportunities for professional development and continuing education.

• Inclusive and collaborative work environment.