Senior Machine Learning Engineer

Regenerative AI builds self-adaptive AI systems that continuously monitor, regulate, and improve their own behavior during real-world operation. We are looking for a Senior Machine Learning Engineer to join our ML Systems team in Boston. You will design, build, and operate production ML systems with built-in feedback loops, runtime monitoring, and automated recalibration. This is a hands-on engineering role focused on system-level robustness, reliability, and continuous adaptation in deployment.

• Design and implement end-to-end ML pipelines: data ingestion, training, evaluation, and serving
• Build and maintain self-adaptive ML systems with runtime monitoring and drift detection
• Develop automated recalibration workflows that respond to performance degradation
• Create observability infrastructure for model health, latency, and prediction quality
• Own incident response and reliability for ML services in production
• Collaborate with platform engineers to integrate ML workloads into CI/CD pipelines
• Establish and enforce governance standards for model versioning, rollback, and auditability
• Optimize training and inference infrastructure for efficiency and cost
• Contribute to internal tooling that accelerates ML development velocity

• 5+ years of experience building and deploying ML systems in production environments
• Strong proficiency in Python and modern ML frameworks (PyTorch, Tensor Flow, or JAX)
• Hands-on experience with distributed training and GPU infrastructure
• Deep understanding of MLOps practices: CI/CD for ML, model versioning, feature stores
• Experience with monitoring and observability tools (Prometheus, Grafana, Datadog, or similar)
• Solid understanding of data pipelines and streaming systems (Kafka, Spark, Airflow)
• Track record of owning and operating ML services with high availability requirements
• Strong software engineering fundamentals and code quality standards

• Experience with online learning, adaptive models, or feedback-driven systems
• Background in model monitoring, drift detection, or anomaly detection
• Familiarity with control systems concepts or adaptive control
• Experience with Kubernetes, Docker, and cloud-native ML infrastructure
• Knowledge of reliability engineering practices (SLOs, error budgets, on-call)
• Experience building internal ML platforms or developer tools

Comprehensive health, dental, and vision insurance

Flexible PTO and remote-friendly work arrangements

Annual learning and development budget ($5,000)

Home office setup allowance

401(k) with company match

We're excited to learn more about you. Apply now and take the next step in your career with Regenerative AI.