Mid/Senior Machine Learning Engineer

Role and Responsibilities:

• Develop, test, and deploy scalable, low-latency machine learning solutions and pipelines, considering various factors such as data characteristics, problem complexity, and computational resource availability.
• Research and explore the latest advancements in machine learning platform technologies, pushing the limits of what is achievable with ML, while staying current with industry trends and developments.
• Experiment with and prototype new ML platforms tailored to specific environments, creating rapid prototypes and proof-of-concepts.
• Automate ML pipelines using CI/CD principles, promoting consistency, reproducibility, and agility across the development lifecycle.
• Ensure model performance on unseen datasets, guaranteeing that it generalizes effectively without overfitting.
• Conduct thorough testing to identify and resolve potential issues, including bias or fairness concerns.
• Optimize model deployment processes, including unit, integration, and stress testing, ensuring high engineering quality.
• Design and build the next-generation machine learning infrastructure to support the simultaneous operation of thousands of model training pipelines and billions of daily batch predictions.
• Work closely with internal ML teams (such as Data Scientists and MLOps teams) to enhance codebase quality and overall product health.

Technologies Utilized:

• Programming Languages: Python, Go
• Machine Learning Frameworks: TensorFlow, PyTorch
• Cloud Platforms: AWS
• Big Data Tools: Spark, Snowflake
• CI/CD and Orchestration Tools: Github Actions, Airflow
• Monitoring Tools: Grafana

Skills and Qualifications:

• Education: Degree in Computer Science or related field.
• Experience: Minimum of 2 years of proven industry experience.
• Programming Skills: Proficient in Python, Go, or other object-oriented programming languages.
• Strong understanding of data structures, algorithms, and software engineering principles.
• Knowledge of mainstream ML libraries (e.g., TensorFlow, PyTorch, Spark ML and/or cloud solutions (e.g., AWS, Sagemaker).
• Familiarity with CI/CD (e.g., Github Actions, Airflow) and big data tools (e.g., MapReduce, Spark, Flink, Kafka, Docker, Kubernetes).
• Database Skills: Experience in SQL and database management, including SQL query optimization.
• Testing Expertise: Experience with unit testing frameworks.