Machine Learning Engineer — Overview
Title
Machine Learning Engineer
Quick Summary
CortexBay Analytics is seeking a Machine Learning Engineer to design, train, and productionize models that power data-driven products. The role spans feature pipelines, experimentation, model lifecycle management, and scalable inference services. The ideal candidate pairs solid engineering practices with an experiment-first mindset and is comfortable collaborating with data engineers, product managers, and platform teams.
Project Category or Industry
Applied machine learning and data platforms within cloud software and analytics.
Type
Full-time employment.
Experience Level
Entry-level and early-career candidates are strongly encouraged to apply. Mentorship and structured onboarding are provided. Mid-level and senior applicants may assume broader ownership of roadmap initiatives.
Duration
Permanent, ongoing role.
Location
Remote-first with teammates across Europe and Asia; preference for at least 4 hours overlap with UTC+1 to UTC+6. Optional coworking hubs in Berlin and Dhaka.
Salary
USD 78,000–135,000 annually, depending on location and experience, plus performance bonus and equity.
Payment Mode
Monthly salary via global payroll with local currency options through an employer-of-record partner.
Hiring Company Name
CortexBay Analytics
Required Skills or Tools
Proficiency in Python, hands-on experience with ML frameworks, familiarity with data modeling and SQL, and comfort deploying services on modern cloud and container platforms.
Job Details
Project Description
You will help build the learning systems behind CortexBay’s analytics products, transforming raw event and transactional data into accurate, reliable predictions. Your work will include creating robust data ingestion and feature pipelines, training and validating models, and deploying low-latency inference endpoints that integrate with customer-facing APIs and internal decision engines.
Core Responsibilities and Expected Deliverables
Own end-to-end model development: problem framing, feature engineering, training, evaluation, and deployment.
Build production-grade data and feature pipelines with monitoring and data quality checks.
Package models for scalable inference, implement A/B or shadow tests, and track online metrics.
Write clear documentation, dashboards, and runbooks to support reliable operations.
Deliver measurable improvements to precision/recall, latency, cost per inference, and model drift resilience.
Required Experience and Preferred Qualifications
Solid foundations in probability, statistics, and linear algebra; experience with supervised learning and basic unsupervised techniques.
Demonstrated projects or internships in ML, preferably with models shipped to a staging or production environment.
Preferred: exposure to experiment design, feature stores, distributed training, and model compression or quantization.
Bachelor’s degree in computer science, engineering, mathematics, or similar—or equivalent practical experience.
Tools or Platforms to Be Used
Modeling: PyTorch or TensorFlow; scikit-learn; XGBoost or LightGBM where appropriate.
Experimentation and tracking: MLflow or Weights & Biases.
Data: SQL, dbt, Apache Airflow; warehouses such as BigQuery, Snowflake, or Redshift.
Serving: ONNX Runtime or TensorRT, FastAPI, Triton Inference Server.
Infrastructure: Docker, Kubernetes, Terraform; AWS or GCP; observability with Prometheus, Grafana, and OpenTelemetry.
Version control and CI: GitHub, GitHub Actions.
Language Requirement
English for daily collaboration; additional languages are appreciated for regional stakeholder communication.
Communication Style
Asynchronous-first via Slack and email; design reviews and sprint ceremonies on Zoom; technical proposals and ADRs maintained in GitHub.
Time Commitment or Working Window
Approximately 40 hours per week with core collaboration between 9:00–13:00 UTC; flexibility outside sprint ceremonies and production releases.
Payment Terms
Fixed monthly salary with an annual performance review. A short, paid technical exercise may precede the final interview stage.
Evaluation Criteria
Portfolio and repository quality, clarity of experimentation and documentation, ability to reason about trade-offs among accuracy, latency, and cost, system design for production ML, and communication skills. References may be requested.
Other Requirements
Standard NDA and data protection addendum; adherence to secure coding and data governance practices; light time-tracking for compliance and customer reporting where applicable.
About the Company
CortexBay Analytics is a remote-first analytics and ML company that helps businesses operationalize machine learning with reliable, observable systems. We focus on practical models that deliver measurable impact and are straightforward to run at scale. Our team is distributed across Europe and Asia with a small administrative presence in Berlin, Germany. Learn more at www.cortexbay.com and contact us at careers@cortexbay.com.