Lead AI Dataloop and Release Engineer

Merlinlabs; Boston (MA); On-site; USD 180,000 - 250,000; Full time

Benefits offered by this job

Health insurance

Dental insurance

Life insurance

Unlimited vacation

401(k) with matching

Job summary

Merlin Labs in Boston, MA is seeking a senior data/platform leader to define and own the data strategy for safety-critical autonomous systems, spanning training, simulation, and production deployment.

You will build and maintain end-to-end data pipelines and oversee large-scale GPU/TPU training, mentoring a team of data and infrastructure engineers while partnering with safety, validation, and certification teams.

Qualifications

• 10+ years of engineering experience with at least 4 years in a technical leadership role owning data infrastructure, MLOps, or AI platform engineering at scale.
• Experience building and operating data pipelines for AI/ML model training, including dataset management, labeling workflows, and data versioning at production scale.
• Hands-on experience integrating data systems with large-scale distributed training infrastructure (e.g., GPU/TPU clusters, job orchestration, experiment tracking).
• Deep understanding of simulation pipelines—data-driven, physics-based, or sensor-realistic simulators—and how data quality impacts fidelity and model transferability.
• Experience in safety-critical domains (autonomous vehicles, aviation, robotics) with rigor, traceability, and validation.
• Strong systems-thinking around data quality, pipeline reliability, and infrastructure design.
• Track record of building platforms and tooling relied upon by multiple teams with reliability and developer experience.
• Excellent cross-functional communication translating data strategy to product, safety, and exec stakeholders.

Responsibilities

• Define and execute a comprehensive data strategy spanning AI model training, simulation, and production deployment across safety-critical autonomous systems.
• Own the end-to-end data pipeline—from raw collection and labeling through curation, versioning, and delivery—ensuring reliability and scale.
• Build and maintain data flywheels that improve model performance by closing the loop between deployed behavior and training iterations.
• Collaborate with teams provisioning and operating large-scale GPU/TPU training clusters to align data delivery with compute capacity and schedules.
• Drive data pipelines that feed data-driven, physics-based, and high-fidelity simulators for realistic validation.
• Partner with safety, validation, and certification teams to meet aviation/automotive regulatory data quality standards.
• Lead and mentor a team of data and infrastructure engineers, setting technical direction and fostering rigor and ownership.
• Define and track KPIs for data pipeline health, simulation fidelity, and model readiness; communicate progress to senior leadership.

Skills

Data strategy

MLOps

Data pipelines

Distributed training

Leadership

Education

Advanced degree in CS/ML/related

Tools

GPU/TPU clusters

Experiment tracking

Data versioning

Job description

About Merlin

Merlin is a venture-backed aerospace startup building a non-human pilot to enable both reduced crew and uncrewed flight. Backed by some of the world’s leading investors, Merlin is scaling alongside our customers to build autonomy solutions that address aviation’s biggest challenges.

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—from raw collection and labeling through curation, versioning, and delivery—ensuring reliability and scale that meet training and simulation workflow demands.
• Build and maintain data flywheels that continuously improve model performance by closing the loop between deployed system behavior and future training iterations.
• Collaborate closely with teams provisioning and operating large-scale GPU/TPU training clusters to align data delivery with compute capacity and training schedules.
• Drive the design and integration of data pipelines that feed data-driven, physics-based, and high-fidelity simulators, ensuring simulated environments are realistic enough to support confident AI model validation.
• Partner with safety, validation, and certification teams to establish data quality standards and traceability practices that satisfy regulatory requirements in aviation and/or automotive domains.
• Lead, mentor, and grow a team of data and infrastructure engineers, setting technical direction and fostering a culture of rigor, ownership, and continuous improvement.
• Define and track KPIs for data pipeline health, simulation fidelity, and model readiness, using these metrics to prioritize investments and communicate progress to senior leadership.

Qualifications

• 10+ years of engineering experience, with 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, including dataset management, labeling workflows, and data versioning at production scale.
• Hands‑on experience integrating data systems with large-scale distributed training infrastructure (e.g., GPU/TPU clusters, job orchestration, experiment tracking).
• Deep understanding of simulation pipelines—including data-driven, physics-based, or sensor-realistic simulators—and how data quality directly impacts simulator fidelity and model transferability.
• Experience working in or alongside safety-critical domains (autonomous vehicles, aviation, robotics, or similar) with an understanding of rigor, traceability, and validation in that context.
• Strong systems‑thinking mindset: reason about data quality, pipeline reliability, and infrastructure design as interconnected constraints, not isolated problems.
• Track record of building platforms and tooling that other engineering teams depend on day-to-day, with a high bar for reliability, documentation, and developer experience.
• Excellent cross-functional communication skills—able to translate technical data strategy into clear priorities for product, safety, and executive stakeholders.

Technical and Domain Expertise

• Experience with domain randomization, synthetic data generation, or sensor simulation techniques used to bridge the sim-to-real gap in autonomous systems.
• Familiarity with aviation-specific standards (e.g., DO‑178C, DO‑254) or automotive safety frameworks (e.g., ISO 26262, SOTIF) as they relate to data and software validation.
• Prior experience building or scaling data flywheel systems—closed-loop pipelines that feed real-world deployment signals back into training and labeling workflows.
• Hands‑on background with perception, planning, or control model development in autonomous vehicles or UAV/UAS systems.
• Experience with formal data governance, lineage tracking, or provenance tooling in regulated environments.
• Contributions to open-source tooling in the MLOps, data engineering, or simulation space.

Location

Merlin HQ, Boston, MA.

Benefits

Merlin Labs offers a comprehensive benefits package, including health, dental, and life insurance, unlimited vacation, and a 401(k) plan with matching contributions.

Equal Opportunity

Merlin Labs is an equal opportunity employer and values diversity. We do not discriminate on the basis of race, religion, color, national origin, genetic information, sex (including pregnancy), gender, gender identity and expression, sexual orientation, age, marital status, military service or obligation, disability status, or any other characteristic protected by law. All job offers are contingent upon the candidate passing background and reference checks.

Visa & Work Authorization

At this time, we are unable to provide visa sponsorship or consider candidates who require visa transfers. Applicants must be authorized to work in the United States without the need for visa sponsorship now or in the future.