Staff Machine Learning Engineer (MLOps)

Location: Boston, MA; hybrid work model with two core office days, typically Tuesday, Wednesday, or Thursday.

Annual base pay: $183,500–$244,600.

Company

hirify.global is a high-tech home security company developing products and services focused on keeping homes secure.

What you will do

• Define the technical vision, architecture, and multi-quarter roadmap for the ML data and training platform.
• Architect scalable data and feature pipelines that produce reliable, versioned training datasets.
• Build distributed training infrastructure for classical ML, deep learning, and LLM fine-tuning, including GPU scheduling and utilization.
• Own experiment tracking, model registry, dataset and model lineage, CI/CD, and training automation to ensure reproducible workflows.
• Partner with data science, data engineering, product, and security leaders; establish data governance and platform standards across teams.
• Drive technology evaluations, AWS cost and reliability improvements, on-call support, incident postmortems, and mentorship for engineers.

Requirements

• 8+ years of experience in software engineering, data engineering, or a related field, including at least 4 years focused on ML infrastructure or MLOps.
• Proven technical leadership designing and shipping ML training platforms, data platforms, or large-scale distributed systems end to end, including cross-team initiatives without direct authority.
• Expert-level experience with AWS or similar public clouds, including compute, storage, networking, and container orchestration.
• Deep experience with CI/CD pipelines, Docker, Kubernetes, and infrastructure-as-code tools such as Terraform or CloudFormation.
• Strong Python proficiency and hands-on experience with machine learning frameworks such as PyTorch or TensorFlow.
• Hands-on experience with distributed training and large-scale data processing, such as Ray, Spark, or PyTorch Distributed.
• Deep understanding of experiment tracking, model registries, and data and model versioning, such as MLflow, Weights & Biases, or DVC.
• Excellent written and verbal communication skills, including technical design documentation and influencing senior technical and non-technical stakeholders.

Nice to have

• Experience handling PII data.
• Experience with workflow orchestration tools such as Airflow, Kubeflow, Flyte, or Step Functions.
• Experience with feature stores and data lakehouse technologies such as Feast, SageMaker Feature Store, Delta Lake, or Iceberg.
• Experience building pipelines for large unstructured datasets such as video, images, or audio, including labeling and curation workflows.
• Experience with event-driven or streaming data architectures such as Kafka or Kinesis.
• Knowledge of cloud security, IAM, data privacy, and compliance best practices for ML data and workloads.

Culture & Benefits

• Collaborative, inclusive, no-ego culture focused on customer empathy, innovation, agility, and continuous improvement.
• Hybrid flexibility with time split between the office and home.
• Comprehensive total rewards package, including wellness, medical, retirement, lifestyle, and other benefits.
• Free hirify.global system and professional home monitoring.
• Employee Resource Groups supporting networking, mentoring, development, and advocacy.