Language: English

As a Software Engineering evaluator, you will create cutting-edge datasets for training, benchmarking, and advancing large language models, collaborating closely with researchers. This includes curating code examples, providing precise solutions, and making corrections in Python, JavaScript (including ReactJS), C/C++, Java, Rust, and Go; evaluating and refining AI-generated code for efficiency, scalability, and reliability; and working with cross-functional teams to enhance enterprise-level AI-driven coding…

As a Delivery Leader for our LLM Training business, you will play a pivotal role in leading our efforts to develop high-quality, foundational LLMs. This position requires managing large teams of software engineers and data scientists dedicated to performing various Supervised Fine Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF) tasks. Your leadership will ensure the delivery of superior SFT and RLHF datasets, achieving optimal outcomes in terms of quality, throughput, and cost. Main tasks listed: Lead and manage large teams (20+) of Python, JavaScript, Java (and other long tail of languages) developers to execute technical tasks effectively; Collaborate closely with researcher clients, ensuring their satisfaction with the delivered work; Maintain rigorous review processes to ensure the highest quality of datasets; Oversee the transition of projects from initiation to stable states, taking full responsibility for delivery.

We are seeking creative and detail-oriented professionals with strong visual interpretation skills to help advance large multimodal AI systems. In this role, you will work on annotating and analyzing videos or images or both, capturing intricate details such as people, objects, environments, relationships, emotions, and storytelling flow. Main tasks listed: Analyze short video clips and images to identify and annotate key visual and narrative components, including subjects, settings, actions, and emotions; Describe temporal sequences, scene transitions, and interactions in videos with high accuracy; Label complex visual cues such as mood, lighting, and spatial relationships; Follow detailed annotation guidelines to ensure consistency, clarity, and data quality.

We are seeking an engineer responsible for designing, implementing, and maintaining data-validation workflows inside Docker-based build pipelines. This role involves creating and managing Dockerfile labels, metadata standards, and validation scripts that ensure datasets, schemas, and model artifacts meet quality and compliance requirements before deployment. Main tasks listed: Develop and optimize Dockerfiles with built-in data-validation steps; Implement LABEL metadata for dataset versions, schemas, and lineage; Create validation scripts (Python/Bash) for schema checks, data integrity, and quality control; Integrate validation steps into CI/CD pipelines and enforce fail-on-bad-data checks.

We are looking for candidates with strong analytical and English comprehension skills. The ideal candidate should have the ability to read, summarize, and break down large content into smaller logical blocks, conduct research online, validate claims made in the content through online research, and work with the LLM (Large Language Models) to solve puzzles! Your role is critical in helping fine-tune and improve large language models (like gpt), and will make you an expert on how to leverage AI to be a better… Main tasks listed: You would spend time answering a variety of interesting analytical questions and creating scenarios that can train the LLM models to get better. Here are a couple of examples which models might get wrong and you would have to give the correct answer and explanation to enable the models to learn:; Based on a given distribution of sales by month across locations, could you analyze which location has grown the most? (Hint: what time period should we look at? should we account for sudden variability at the beginning?); In a small town, there are four distinct neighborhoods: Oak, Pine, Maple, and Elm. A postman is assigned to deliver mail and can only deliver to two neighborhoods in one day, with certain rules (e.g. Oak is always visited before Pine). If he delivers to Oak and Elm on the first day, which neighborhoods does the carrier deliver to on the second day?; Note: No other prior specialized domain experience is needed.

We are looking for candidates with strong analytical and English comprehension skills. The ideal candidate should have the ability to read, summarize, and break down large content into smaller logical blocks, conduct research online, validate claims made in the content through online research, and work with the LLM (Large Language Models) to solve puzzles! Your role is critical in helping fine-tune and improve large language models (like gpt), and will make you an expert on how to leverage AI to be a better… Main tasks listed: You would spend time answering a variety of interesting analytical questions and creating scenarios that can train the LLM models to get better. Here are a couple of examples which models might get wrong and you would have to give the correct answer and explanation to enable the models to learn:; Based on a given distribution of sales by month across locations, could you analyze which location has grown the most? (Hint: what time period should we look at? should we account for sudden variability at the beginning?); In a small town, there are four distinct neighborhoods: Oak, Pine, Maple, and Elm. A postman is assigned to deliver mail and can only deliver to two neighborhoods in one day, with certain rules (e.g. Oak is always visited before Pine). If he delivers to Oak and Elm on the first day, which neighborhoods does the carrier deliver to on the second day?; Note: No other prior specialized domain experience is needed.

We are looking for candidates with strong analytical and English comprehension skills. The ideal candidate should have the ability to read, summarize, and break down large content into smaller logical blocks, conduct research online, validate claims made in the content through online research, and work with the LLM (Large Language Models) to solve puzzles! Your role is critical in helping fine-tune and improve large language models (like gpt), and will make you an expert on how to leverage AI to be a better… Main tasks listed: You would spend time answering a variety of interesting analytical questions and creating scenarios that can train the LLM models to get better. Here are a couple of examples which models might get wrong and you would have to give the correct answer and explanation to enable the models to learn:; Based on a given distribution of sales by month across locations, could you analyze which location has grown the most? (Hint: what time period should we look at? should we account for sudden variability at the beginning?); In a small town, there are four distinct neighborhoods: Oak, Pine, Maple, and Elm. A postman is assigned to deliver mail and can only deliver to two neighborhoods in one day, with certain rules (e.g. Oak is always visited before Pine). If he delivers to Oak and Elm on the first day, which neighborhoods does the carrier deliver to on the second day?; Note: No other prior specialized domain experience is needed.

We are looking for candidates with strong analytical and English comprehension skills. The ideal candidate should have the ability to read, summarize, and break down large content into smaller logical blocks, conduct research online, validate claims made in the content through online research, and work with the LLM (Large Language Models) to solve puzzles! Your role is critical in helping fine-tune and improve large language models (like gpt), and will make you an expert on how to leverage AI to be a better… Main tasks listed: You would spend time answering a variety of interesting analytical questions and creating scenarios that can train the LLM models to get better. Here are a couple of examples which models might get wrong and you would have to give the correct answer and explanation to enable the models to learn:; Based on a given distribution of sales by month across locations, could you analyze which location has grown the most? (Hint: what time period should we look at? should we account for sudden variability at the beginning?); In a small town, there are four distinct neighborhoods: Oak, Pine, Maple, and Elm. A postman is assigned to deliver mail and can only deliver to two neighborhoods in one day, with certain rules (e.g. Oak is always visited before Pine). If he delivers to Oak and Elm on the first day, which neighborhoods does the carrier deliver to on the second day?; Note: No other prior specialized domain experience is needed.

We are looking for candidates with strong analytical and English comprehension skills. The ideal candidate should have the ability to read, summarize, and break down large content into smaller logical blocks, conduct research online, validate claims made in the content through online research, and work with the LLM (Large Language Models) to solve puzzles! Your role is critical in helping fine-tune and improve large language models (like gpt), and will make you an expert on how to leverage AI to be a better… Main tasks listed: You would spend time answering a variety of interesting analytical questions and creating scenarios that can train the LLM models to get better. Here are a couple of examples which models might get wrong and you would have to give the correct answer and explanation to enable the models to learn:; Based on a given distribution of sales by month across locations, could you analyze which location has grown the most? (Hint: what time period should we look at? should we account for sudden variability at the beginning?); In a small town, there are four distinct neighborhoods: Oak, Pine, Maple, and Elm. A postman is assigned to deliver mail and can only deliver to two neighborhoods in one day, with certain rules (e.g. Oak is always visited before Pine). If he delivers to Oak and Elm on the first day, which neighborhoods does the carrier deliver to on the second day?; Note: No other prior specialized domain experience is needed.

We are looking for candidates with strong analytical and English comprehension skills. The ideal candidate should have the ability to read, summarize, and break down large content into smaller logical blocks, conduct research online, validate claims made in the content through online research, and work with the LLM (Large Language Models) to solve puzzles! Your role is critical in helping fine-tune and improve large language models (like gpt), and will make you an expert on how to leverage AI to be a better… Main tasks listed: You would spend time answering a variety of interesting analytical questions and creating scenarios that can train the LLM models to get better. Here are a couple of examples which models might get wrong and you would have to give the correct answer and explanation to enable the models to learn:; Based on a given distribution of sales by month across locations, could you analyze which location has grown the most? (Hint: what time period should we look at? should we account for sudden variability at the beginning?); In a small town, there are four distinct neighborhoods: Oak, Pine, Maple, and Elm. A postman is assigned to deliver mail and can only deliver to two neighborhoods in one day, with certain rules (e.g. Oak is always visited before Pine). If he delivers to Oak and Elm on the first day, which neighborhoods does the carrier deliver to on the second day?; Note: No other prior specialized domain experience is needed.