Machine Learning Software Engineer II

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

📋 Description

• Oversee the transition of machine learning models from conceptual prototypes to scalable and high-performance production systems.

• Design and implement ML solutions using AWS ECS (Elastic Container Service) for containerized workloads and AWS Lambda for serverless, event-driven inference pipelines.

• Enhance PyTorch models for production deployment by converting them to ONNX formats.

• Utilize advanced inference optimization techniques such as quantization, pruning, and ONNX Runtime, along with memory-efficient attention mechanisms like Flash Attention, to reduce latency and increase throughput.

• Advocate for infrastructure best practices in machine learning systems, establishing dependable CI/CD pipelines while ensuring secure, robust, and reproducible deployments across the AWS ecosystem.

• Create, develop, and assess algorithms that yield descriptive, diagnostic, predictive, and prescriptive insights from both structured and unstructured datasets.

• Produce clean, efficient, and thoroughly tested code, conducting rigorous testing, debugging, and documentation to facilitate seamless installation and long-term maintenance.

• Engage in research discussions, gather requirements, and contribute to system design alongside domain experts to develop customized scoring and ML solutions.

⛳️ Requirements

• 2–5 years of professional experience in Machine Learning Engineering, Software Engineering, or Data Science, demonstrating a successful history of architecting and deploying models in production.

• Extensive, hands-on experience with the AWS ecosystem, particularly AWS ECS and Lambda.

• Strong understanding of containerization (Docker) and event-driven architectures.

• High proficiency in modern programming languages relevant to ML (such as Python, C++, Java) and familiarity with industry-standard coding practices.

• Practical experience with PyTorch and other machine learning libraries (e.g., Scikit-Learn, TensorFlow).

• In-depth knowledge of model optimization processes, including PyTorch to ONNX conversions, ONNX Runtime, and scaling attention mechanisms (like Flash Attention).

• Experience with large-scale computing frameworks, data analysis systems, and both relational and non-relational databases.

🏝️ Benefits

• Equal Opportunity Employer

• Cultivating a culture that recognizes and celebrates diverse backgrounds, ideas, and experiences.