Amazon Robotics develops state-of-the art robotics for Amazon’s Fulfillment Centers. Come build the future of human-technology interaction with us. We are combining computer vision, mobile robots, advanced end-of-arm tooling, and a high degree of freedom of movement to solve real-world problems on a huge scale. Our team collaborates with a variety of customers across Amazon worldwide to conceive, develop, prototype, and deploy a wide range of robotic systems. Within Amazon Robotics, Global Operations - Artificial Intelligence (GO-AI) enables Amazon to accelerate and scale our machine learning transformation via human-in-the-loop support.
As an Associate II, ML Data Operations, you will be a vital Human-in-the-Loop expert in a non-technical role, responsible for executing data annotation tasks across highly diverse portfolios. Your work directly supports the training and validation of machine learning models for Amazon Robotics and Fulfillment Technologies, ensuring the quality and integrity of data which is critical for frontier AI improvements. Your work will involve analyzing processes such as packaging innovation, object manipulation, inventory storage, and sortation automation within fulfillment environments. This role requires strong attention to detail, the ability to make sound judgments using provided resources, and contributes to maintaining fulfillment center quality metrics through precise text/video/image annotation. This role is offered on a contractual basis, with the opportunity to transition into a full-time position based on performance and business requirements.
Basic qualifications
- Bachelor’s degree in any discipline with 0.6 - 5 years of experience working with data transcription and annotation.
- English proficiency (C1+ or > C1 fluency) with good business writing skills
- Demonstrate proficiency in generating high quality human insight data across a range of modalities, inclusive of text, image video and audio.
- Specializing in LLM annotation work using amazon internal generative AI tools.
- Proficient research skills with experience to write basic prompts to gather and synthesizing information from multiple sources
- Excellent organizational and time management skills to prioritize complex tasks effectively.
- Comfortable working in a collaborative environment with remote, multi-cultural teams, willing to share knowledge, and able to maintain individual productivity goals.
- Good communication skills with the ability to articulate complex ideas and provide clear explanations.
- Work in a flexible schedule/shift/work area, including weekends, nights, and/or holidays
Key job responsibilities
This is a non-technical operational role; you will deliver high-quality training data to improve and expand our Large Language Model (LLM) capabilities. That requires adhering to process oriented, not programming skills. The Associate II will perform precise annotation tasks (The process of labeling or tagging data for machine learning training), displays flexibility with the ability to transition immediately between the projects as per business requirements, proactively identifies day-to-day operational friction and bottlenecks within Standard Operating Procedures (SOPs) and tools, proposing logical conclusion and actionable changes to unblock workflows. The candidate is expected to demonstrate:
- Perform precise and consistent annotations across multiple data types (image, video, and text). This includes mastering techniques like object detection, semantic segmentation (pixel-level labeling), object tracking in video, and open-text evaluation.
- Proactively identify and correct errors, ensuring high data integrity and making obsessive precision crucial for model training.
- Write grammatically correct, creative, and technical texts in various styles, strictly adhering to complex project guidelines
- Make strong judgment to address ambiguous situations or incomplete information and, when guidelines fail, propose logical, consistent solutions that contribute to process improvement.
- Quickly learn and efficiently utilize various specialized annotation tools and platforms, adapting to new methodologies as required by evolving programs in domains like packaging, manipulation, storage, and sortation automation.
- Ability to scale and transition between 2-3 programs based on business needs and ability to train associates and provide peer feedback.
- Validate data based on specific annotation guidelines, ensuring the accuracy and quality of the collected information
- You will strive to enhance the productivity and effectiveness of the data generation by contributing to the development and continuous improvement of audit methodologies, checklists, and test frameworks.
A day in the life
We are looking for an Associate II, ML Data Operations to undertake the task of foundational labelling functions, such as dialogue evaluation on speech, text, audio, and video data – Success in this role requires strong concentration, effective multitasking with familiarity to write basic prompts, dive deep into use case and interpret and implement solutions. associate will be collaborating with a diverse team of specialists, sharing insights on "edge cases" where the data is ambiguous. Associates II work in a 24x7 environment with rotational shifts. Associates II would be working from HYD office with a 9-hour shift. The shift and break timings would be subject to change every 3-4 months or as per business requirement. In case an associate is working in night shift, night shift allowance will be provided as per applicable to Amazon’s work policy.
Weekly Offs: Rotational two-consecutive days off (it is a 5-day working week with 2 consecutive days off, not necessarily Saturday and Sunday) or as per business discretion.
Training Program: Selected candidates will participate in a structured one-week training program to develop essential capabilities and to ensure operational readiness prior to deployment
Weekly Offs: Rotational two-consecutive day off (it is a 5-day working week with 2 consecutive days off, not necessarily Saturday and Sunday) or as per business discretion.
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