Senior Machine Learning Engineer

Regenerativeaitool

Boston (MA)

Hybrid

USD 120,000 - 150,000

Full time

14 days+

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Benefits offered by this job

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

Job summary

Regenerative AI in Boston is seeking a Senior Machine Learning Engineer to design and operate production ML systems with feedback loops. You will work on building and maintaining self-adaptive ML systems with monitoring and recalibration features.

Ideal candidates will have over 5 years of experience in ML systems and expertise in Python and modern frameworks. The position offers competitive salary, health benefits, and a flexible work environment.

Qualifications

5+ years of experience building and deploying ML systems in production environments.
Strong proficiency in Python and modern ML frameworks.
Hands-on experience with distributed training and GPU infrastructure.

Responsibilities

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.

Skills

Python proficiency

ML frameworks (PyTorch, TensorFlow, or JAX)

Distributed training

MLOps practices

Monitoring tools (Prometheus, Grafana, Datadog)

Data pipelines (Kafka, Spark, Airflow)

Software engineering fundamentals

Tools

Kubernetes

Docker

Cloud-native ML infrastructure

Job description

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.

What You'll Do

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

Qualifications

5+ years of experience building and deploying ML systems in production environments
Strong proficiency in Python and modern ML frameworks (PyTorch, TensorFlow, 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

Nice to Have