Lead AI Dataloop, Release Engineer

Jobtailor; Boston (MA); On-site; USD 180,000 - 260,000; Full time

Job summary

Jobtailor in Boston seeks a veteran data and infrastructure leader to define and execute a comprehensive data strategy for AI model training, simulation, and production deployment across safety-critical autonomous systems.

You will own end-to-end data pipelines, build data flywheels to improve model performance, and collaborate with teams operating large-scale GPU/TPU training clusters to ensure reliable, scalable training and simulation workflows.

Qualifications

• 10+ years of engineering experience.
• at least 4 years in a technical leadership role owning data infrastructure, MLOps, or AI platform engineering at scale.
• demonstrated experience building and operating data pipelines for AI/ML model training.
• hands-on experience integrating data systems with large-scale distributed training infrastructure (e.g., GPU/TPU clusters).
• deep understanding of simulation pipelines.
• experience working in or alongside safety-critical domains.
• strong systems-thinking mindset
• track record of building platforms and tooling that other engineering teams depend on day-to-day
• excellent cross-functional communication skills.

Responsibilities

• Define and execute a comprehensive data strategy that spans AI model training, simulation, and production deployment across safety-critical autonomous systems
• Own the end-to-end data pipeline ensuring the reliability and scale that training and simulation workflows demand
• Build and maintain data flywheels that continuously improve model performance
• Collaborate closely with teams provisioning and operating large-scale GPU/TPU training clusters
• Drive the design and integration of data pipelines for simulations
• Partner with safety, validation, and certification teams for data quality standards
• Lead, mentor, and grow a team of data and infrastructure engineers
• Define and track KPIs for data pipeline health, simulation fidelity, and model readiness

Job description

• Define and execute a comprehensive data strategy that spans AI model training, simulation, and production deployment across safety‑critical autonomous systems
• Own the end‑to‑end data pipeline ensuring the reliability and scale that training and simulation workflows demand
• Build and maintain data flywheels that continuously improve model performance
• Collaborate closely with teams provisioning and operating large‑scale GPU/TPU training clusters
• Drive the design and integration of data pipelines for simulations
• Partner with safety, validation, and certification teams for data quality standards
• Lead, mentor, and grow a team of data and infrastructure engineers
• Define and track KPIs for data pipeline health, simulation fidelity, and model readiness

Requirements

• 10+ years of engineering experience
• at least 4 years in a technical leadership role owning data infrastructure, MLOps, or AI platform engineering at scale
• demonstrated experience building and operating data pipelines for AI/ML model training
• hands‑on experience integrating data systems with large‑scale distributed training infrastructure (e.g., GPU/TPU clusters)
• deep understanding of simulation pipelines
• experience working in or alongside safety‑critical domains
• strong systems‑thinking mindset
• track record of building platforms and tooling that other engineering teams depend on day‑to‑day
• excellent cross‑functional communication skills