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Weekend Ai Data Annotation Jobs in Missouri (NOW HIRING)

Member of Technical Staff - DataAbout Us Veeda AI is building the next generation of multimodal ... Annotation & Auto-Labeling: Produce the labels the models need, such as VLM captions, camera pose ...

$6 - $65/hr

The role combines professional voice performance with linguistic evaluation, annotation, and AI ... Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process ...

$65/hr

You will contribute authentic, high-quality Romanian voice data used to improve the clarity ... Collaborate with project teams to refine prompts, evaluation methodologies, annotation practices ...

... computing and wearable AI. We provide real-time, store-level insights that were previously ... Data Curation & Annotation Systems: Build robust internal QC, mapping, and annotation platforms ...

Showing results 21-40

Weekend Ai Data Annotation information

What is a Weekend AI Data Annotation specialist?

Weekend AI Data Annotators are professionals who label, categorize, and tag data—such as images, audio, or text—for use in training artificial intelligence models, specifically working on weekends. Their work ensures that machine learning algorithms receive high-quality, accurately labeled datasets for tasks like computer vision, natural language processing, or speech recognition. This role often involves using specialized annotation tools and following precise guidelines to maintain consistency and accuracy. Weekend annotators may work remotely or on-site, and their contributions are vital for improving AI system performance.

What skills and qualifications are needed to thrive as a Weekend AI Data Annotation specialist?

To thrive as a Weekend AI Data Annotation Specialist, you need attention to detail, strong analytical skills, and familiarity with data labeling processes, often supported by a high school diploma or post-secondary coursework in a technical field. Proficiency with annotation platforms like Labelbox, Supervisely, or internal company tools is typically required, along with basic knowledge of data privacy protocols. Reliability, time management, and effective communication are crucial soft skills for meeting project deadlines and collaborating with remote teams. These skills and qualities ensure the accuracy and efficiency of annotated datasets, which are essential for high-performing AI systems.

What are common challenges faced by Weekend AI Data Annotation specialists, and how can they be managed?

Weekend AI Data Annotation specialists often encounter challenges such as maintaining high attention to detail during repetitive tasks and managing productivity over long annotation sessions. Since the work is typically remote or semi-remote, self-motivation and effective time management are crucial to meet project deadlines. It's helpful to take regular breaks, communicate proactively with team leads when questions arise, and make use of any annotation guidelines or quality assurance feedback provided. Collaborating with teammates through chat platforms or project management tools can also enhance consistency and resolve uncertainties quickly.

What are the most commonly searched types of Ai Data Annotation jobs in Missouri?

The most popular types of Ai Data Annotation jobs in Missouri are:

What are popular job titles related to Weekend Ai Data Annotation jobs in Missouri?

For Weekend Ai Data Annotation jobs in Missouri, the most frequently searched job titles are:

What cities in Missouri are hiring for Weekend Ai Data Annotation jobs?

Cities in Missouri with the most Weekend Ai Data Annotation job openings:

Member of Technical Staff - Data

Veeda

California, MO • On-site

$130 - $180/hr

Other

Posted 4 days ago


Key responsibilities

  • Build ingest pipelines for video, lidar, and robot trajectories with GPU decoding and data resharding.

  • Decide which data to include by applying filters based on blur, exposure, camera-trajectory scoring, and embedding deduplication.

  • Produce and validate labels such as VLM captions, camera pose, depth, and segmentation pseudo-labels, ensuring accuracy against human review.


Job description

Member of Technical Staff - DataAbout Us

Veeda AI is building the next generation of multimodal foundation world models for Physical AI. We're a small, fast-moving team of engineers and researchers from leading AI labs, tackling some of the most challenging problems at the intersection of AI, robotics, and embodied intelligence. If you're excited about pushing the boundaries of what's possible with Physical AI, you'll have the opportunity to make an outsized impact from day one.

Responsibilities
  • Multimodal Ingest: Build ingest for video, lidar, and robot trajectories on Ray Data and Daft, with GPU decode (NVDEC, DALI) and resharding into WebDataset and Lance layouts that stream sequentially rather than seeking per sample.

  • Curation & Filtering: Decide what earns a slot using blur, exposure, and camera-trajectory scoring plus embedding deduplication over cuVS indexes, and prove each filter with a downstream ablation, not a dataset-size delta.

  • Annotation & Auto-Labeling: Produce the labels the models need, such as VLM captions, camera pose from feed-forward reconstruction (VGGT, MASt3R), and depth and segmentation pseudo-labels, and hold each to a measured error rate against human review.

  • Real & Synthetic Interop: Normalize episodic data across formats such as LeRobotDataset v3, Open X-Embodiment, and RLDS, reconciling action spaces, control rates, and frame timing, and account for the simulated share of every training mixture.

  • Provenance, Licensing & Governance: Track license terms, restricted-source flags, and C2PA content credentials at source granularity, and version datasets as immutable manifests so any checkpoint traces back to the exact bytes that trained it.

Requirements
  • Bachelor's degree in Computer Science, Computer Engineering, or equivalent hands-on experience in large-scale data engineering.

  • Built and operated distributed data pipelines (e.g., Ray Data, Daft, Spark) over hundreds of terabytes, with rigor in idempotency, backfills, and schema evolution.

  • Strong Python skills and comfortable in the video stack (codecs, containers, ffmpeg, GPU decode) and in columnar and object storage formats.

  • Able to design and defend a data mixture empirically, running curation ablations that measure downstream model quality.

  • Experience working inside real licensing constraints on what may and may not be trained on, with provenance treated as a hard requirement.

Nice to Have
  • Experience with robot trajectory formats and tooling such as LeRobot, RLDS, ROS 2 bags, or MCAP.

  • Experience with sensor calibration, hardware time synchronization, and non-pinhole camera models such as fisheye or ftheta.

  • Experience running GPU-accelerated curation with RAPIDS or NeMo Curator.

  • Experience building PII, face, and plate redaction into a video pipeline at scale.

  • Managed annotation vendors and built the QA statistics that keep them honest.

  • Built lakehouse storage on Iceberg or Delta and reduced object-storage cost without losing read throughput.

  • Published on data curation or contributed to open-source data tooling such as DataTrove or video2dataset.

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