year

24-Mag•New York, NY

5d•Remote

About The Position

Lead end-to-end delivery of software engineering and coding data pipelines that support advanced AI training, research, and model evaluation. The role combines technical project leadership with operational execution, workflow automation, quality assurance, and cross-functional coordination. The successful candidate will turn complex technical requirements into structured delivery processes while maintaining high standards for data quality, efficiency, and project performance. Responsibilities include defining technical task specifications and quality frameworks; designing and improving coding-data workflows; monitoring pipeline performance; applying quantitative analysis to improve execution; and coordinating with engineering, research, operations, review, and project stakeholders. The scope and priorities may evolve as project requirements develop. Work must be completed without using confidential or proprietary information belonging to any employer, client, institution, or other third party. This is a full-time, fully remote opportunity available through 24-MAG LLC. Compensation is $300,000–$500,000 per year, with one opening. 24-MAG LLC connects experienced professionals with remote consulting opportunities across technical, evaluation, and project-based workstreams.

Requirements

Bachelor's degree in Computer Science, Software Engineering, Computer Engineering, or a closely related technical discipline.
At least 2 years of experience leading technical projects, operational initiatives, or complex data processing workflows.
Strong understanding of software engineering principles, programming fundamentals, and coding-related technical workflows.
Experience managing cross-functional technical activities involving engineering, operations, or quality assurance.
Strong understanding of data pipeline operations, task management, workflow coordination, and delivery processes.
Experience translating complex stakeholder requirements into clear technical specifications and scalable execution plans.
Familiarity with quality assurance methodologies, structured review criteria, and performance measurement frameworks.
Ability to develop, interpret, and improve operational metrics involving quality, throughput, efficiency, and delivery performance.
Strong analytical reasoning, quantitative problem-solving, and data-driven decision-making capabilities.
Experience identifying process inefficiencies and implementing workflow automation or operational improvements.
Excellent project management, organisational, and prioritisation skills.
Strong written and verbal communication skills and ability to work with technical and non-technical stakeholders.
Ability to take ownership of complex initiatives and manage execution across multiple workstreams.
Ability to work independently, exercise sound judgement, and adapt to changing project requirements.
Proactive approach to technical leadership, continuous improvement, and scalable operational execution.

Nice To Haves

Familiarity with AI training pipelines, human data operations, software engineering evaluation, or related research workflows.
Experience working in fast-paced technology environments or supporting rapidly evolving technical projects.

Responsibilities

Lead end-to-end delivery of coding and software engineering data pipelines supporting advanced AI development.
Coordinate technical project activities across engineering experts, reviewers, and operational stakeholders.
Translate project objectives into execution plans, deliverables, and quality requirements.
Establish delivery processes that support scalability, reliability, and operational efficiency.
Maintain alignment between technical specifications, project timelines, and quality standards.
Design and improve workflows for coding tasks, data generation, validation, and quality control.
Identify opportunities to automate repetitive processes and improve pipeline efficiency.
Develop scalable approaches to task allocation, workflow coordination, and operational reporting.
Evaluate process performance and implement improvements in software engineering data operations.
Support integration of automation into delivery and quality assurance workflows.
Define technical task specifications, evaluation criteria, and review methodologies for coding assignments.
Develop reference examples, golden samples, and structured quality assessment rubrics.
Establish standards for evaluating technical accuracy, completeness, and task compliance.
Clarify task requirements and resolve ambiguities with technical contributors.
Refine evaluation processes to improve consistency, reliability, and data quality.
Monitor quality, average handling time (AHT), throughput, and delivery performance metrics.
Analyse workflow data to identify bottlenecks and improvement opportunities.
Develop reporting approaches that provide visibility into pipeline performance.
Use quantitative analysis and operational insights to improve resource allocation and execution efficiency.
Lead continuous improvement initiatives informed by review findings, stakeholder feedback, and performance metrics.
Collaborate with software engineering professionals, technical reviewers, researchers, and project stakeholders.
Communicate technical requirements and operational priorities across multidisciplinary teams.
Coordinate feedback cycles to improve task design, review processes, and deliverables.
Support standardised operating procedures and knowledge-sharing practices.
Promote technical ownership, analytical problem-solving, automation, and consistent execution.