SuperAnnotate

Research Engineer

• Take a research direction and independently identify supporting resources – papers, benchmarks, blog posts – then implement or reimplement the relevant methods.
• Build and own the process to reproduce prior work internally and identify ways to improve on it.
• Own projects (for example, an RL/agentic environment build for a partner or a novel multimodal benchmark) end to end, including scoping, MVP implementation, and validation.
• Partner with strategic project leads and technical leads to translate ambiguous requirements into a concrete, testable research plan.
• Validate ideas through hands-on implementation, including annotating, evaluating, or sourcing data.
• Turn research directions into tangible outputs – a paid customer dataset, a customer pilot, an internal dataset, or a paper/blog post for publication or conference presentation.
• Bring an ML perspective to new opportunities — assessing technical feasibility of incoming requests and helping shape proposals where research depth is needed.

Associate

• Own and drive the end-to-end execution of complex LLM, multimodal, and annotation workflows, ensuring adherence to project requirements, quality standards, delivery timelines, and operational priorities.
• Translate project requirements into clear execution strategies, task structures, and actionable guidance for Associates, annotators, and QA specialists, including handling ambiguous guidelines and edge cases.
• Manage workforce allocation across projects by assigning, prioritizing, and rebalancing tasks based on skill fit, availability, performance trends, delivery constraints, and operational risk.
• Drive daily operations by monitoring task progress, throughput, quality metrics, and delivery health; proactively identify risks, resolve blockers, and ensure on-time project completion.
• Serve as a key operational point of contact for Associates, annotators, and QA specialists, providing guidance, clarifications, and execution support.
• Monitor quality and operational performance, conduct root-cause analysis of systemic issues, identify recurring error patterns, and recommend process improvements and risk mitigation strategies.
• Escalate complex operational, contractor, and project-related issues to stakeholders with clear context, impact assessment, and proposed resolutions; act as an escalation point for Associates when deeper analysis or cross-functional coordination is required.
• Manage post-onboarding contractor issues, including execution blockers, platform access, workflow questions, performance concerns, and payment-related inquiries, coordinating with Support, Engineering, Finance, Talent, and other stakeholders to ensure timely resolution.
• Maintain accurate documentation, reporting, and issue tracking related to project execution, quality outcomes, operational metrics, contractor support, and key learnings.
• Contribute to the continuous improvement of SOPs, operational playbooks, communication templates, and workflow processes to support scalable and efficient AI data operations.
• Support and mentor AI Workforce Experience Associates by sharing best practices, providing operational guidance, and assisting with complex issue resolution.
• Collaborate cross-functionally to build a scalable, reliable, and contractor-friendly AI data operations environment that delivers high-quality outcomes for global enterprise clients.

Research Engineer

Workforce Experience Associate – Specialist