Machine Learning Engineer

November 11

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Hatch Innovations

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Artificial Intelligence • API • Web 3

Hatch Innovations is your partner in business technology solutions. We specialize in crafting creative technology solutions, integrating artificial intelligence and blockchain into your infrastructure to meet your unique needs. Our longstanding collaborations with industry leaders like Electronic Arts and Epic Games underscore our commitment to delivering strategic and creative technology solutions that enhance brand interaction and improve decision-making processes.

51 - 200 employees

Founded 2011

🤖 Artificial Intelligence

🔌 API

🌐 Web 3

📋 Description

• Production Deployment: Take cutting-edge models and prototypes developed by Data Scientists and Researchers and transform them into production-ready, high-availability services (e.g., APIs, microservices). • ML Pipeline Development: Architect and build automated, scalable pipelines for the entire ML lifecycle, including data ingestion, feature engineering, model training, validation, testing, and deployment. • Performance Optimization: Optimize models and serving infrastructure for low latency, high throughput, and efficient resource utilization at scale. • MLOps & Observability: Implement comprehensive MLOps practices, including monitoring tools for model drift, data quality, and service health; build systems for automated retraining; and ensure robust logging and observability. • Infrastructure: Work closely with engineering teams to leverage and integrate models into our core services and applications. • Maintainability & Quality: Write clean, well-tested, and maintainable code, adhering to software engineering best practices and contributing to code reviews.

🎯 Requirements

• 3+ years of professional experience in a software engineering or machine learning engineering role. • Strong foundational skills in Software Engineering and Systems Design (Golang, Python, or similar). • Proven experience building and operating robust data and ML pipelines using tools like Apache Airflow, Kubeflow, or similar orchestration platforms. • Familiarity with deep learning frameworks such as TensorFlow or PyTorch. • Expertise with cloud platforms (e.g., AWS, GCP, Azure) and their managed ML services. • Experience with containerization technologies (Docker) and orchestration platforms (Kubernetes). • Strong understanding and experience with CI/CD pipelines for both software and ML model deployment. • Familiarity with ML lifecycle management tools like MLflow, Weights & Biases, or DVC. • Bachelor's degree in Computer Science, Engineering, or a related quantitative field, or equivalent practical experience.

🏖️ Benefits

• Health Spending Account • Disability insurance • Life insurance • Paid time off • Work from home