AI Summary
Develop and productionize ML models and pipelines for analytics, recommendation, and intelligent automation. Build and maintain model-serving infrastructures with containerization. Optimize model architecture and inference to achieve operational SLAs.
Key Highlights
Develop production-grade machine learning systems for AI-driven SaaS/Enterprise ML sector
Work end-to-end on model development, deployment, and observability
Build and maintain model-serving infrastructures with containerization
Optimize model architecture and inference for operational SLAs
Key Responsibilities
Design, implement, and productionize ML models and pipelines for supervised and unsupervised tasks
Build and maintain model-serving infrastructures (REST/gRPC) with containerization to meet latency, throughput, and cost targets in cloud environments
Develop CI/CD, automated retraining, canary/A-B rollout strategies, and monitoring for model performance, drift detection, and data quality
Optimize model architecture and inference (quantization, batching, sharding) to achieve operational SLAs and efficient resource utilization
Collaborate closely with Data Scientists, Product, and Platform teams to translate ML prototypes into reliable microservices and data pipelines
Document architectures, establish engineering best practices for reproducibility, observability, and security, and mentor junior engineers
Technical Skills Required
Benefits & Perks
Fully remote U.S. role with flexible schedules
Competitive compensation
Professional development support
Job Description
Primary title: Senior Machine Learning Engineer
Operating in the AI-driven SaaS / Enterprise ML sector, this remote role develops production-grade machine learning systems that power analytics, recommendation, and intelligent automation for U.S. customers. You will work end-to-end on model development, deployment, and observability to deliver scalable, low-latency ML services.
Role & Responsibilities
- Design, implement, and productionize ML models and pipelines for supervised and unsupervised tasks, ensuring reproducible training and predictable inference behavior.
- Build and maintain model-serving infrastructures (REST/gRPC) with containerization to meet latency, throughput, and cost targets in cloud environments.
- Develop CI/CD, automated retraining, canary/A-B rollout strategies, and monitoring for model performance, drift detection, and data quality.
- Optimize model architecture and inference (quantization, batching, sharding) to achieve operational SLAs and efficient resource utilization.
- Collaborate closely with Data Scientists, Product, and Platform teams to translate ML prototypes into reliable microservices and data pipelines.
- Document architectures, establish engineering best practices for reproducibility, observability, and security, and mentor junior engineers.
Interested in remote work opportunities in Machine Learning & AI? Discover Machine Learning & AI Remote Jobs featuring exclusive positions from top companies that offer flexible work arrangements.
- Must-Have
- Python
- PyTorch
- TensorFlow
- scikit-learn
- SQL
- Docker
- AWS
- MLOps
- Preferred
- Hugging Face Transformers
- Kubernetes
- MLflow
Browse our curated collection of remote jobs across all categories and industries, featuring positions from top companies worldwide.
- Fully remote U.S. role with flexible schedules and asynchronous collaboration.
- Fast-paced, product-focused engineering culture that values ownership, measurable impact, and continuous learning.
- Competitive compensation, professional development support, and opportunities to influence ML platform design.
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