Data Ops Lead
$160K - $220K/yr
Standing up human transcription, annotation and other operations, largely overseas, that make it ... Expect roughly 60 hours per week, with occasional weekend work around launches and deadlines. We're ...
Data Ops Lead
$160K - $220K/yr
Standing up human transcription, annotation and other operations, largely overseas, that make it ... Expect roughly 60 hours per week, with occasional weekend work around launches and deadlines. We're ...
Weekend Data Annotation information
What is a weekend data annotation job?
Weekend Data Annotation jobs involve labeling, tagging, or categorizing data such as text, images, audio, or video, typically for use in machine learning or artificial intelligence projects. These roles are specifically scheduled for weekends, making them ideal for individuals seeking flexible, part-time work outside of standard weekday hours. Data annotators often work remotely and use specialized tools or platforms to ensure data is accurately and consistently labeled according to project guidelines. This work is crucial for training algorithms to recognize patterns and make predictions in various applications, such as self-driving cars, language translation, or content moderation.
What skills and qualifications are needed for weekend data annotation?
To excel as a Weekend Data Annotation Specialist, you need strong attention to detail, consistency, and familiarity with data labeling processes, often supported by a high school diploma or equivalent. Competence with annotation tools like Labelbox, Supervisely, or proprietary platforms, and sometimes knowledge of basic coding or data formats (e.g., CSV, JSON), is typically required. Reliability, time management, and the ability to follow precise instructions are essential soft skills for this role. These skills ensure high-quality, accurate datasets that are critical for training effective machine learning models.
What challenges do weekend data annotation specialists face, and how can they be addressed?
Weekend data annotation specialists often encounter challenges such as maintaining focus during repetitive tasks and managing tight turnaround times for projects that require quick weekend processing. To address these issues, it's helpful to take regular short breaks to reduce fatigue and to establish a clear workflow at the start of each shift. Additionally, open communication with team leads about workload and expectations can ensure priorities are clear, making it easier to manage deadlines and collaborate effectively, even outside traditional weekday hours.
What is the difference between Weekend Data Annotation vs Data Labeler?
| Aspect | Weekend Data Annotation | Data Labeler |
|---|---|---|
| Credentials | Basic computer skills, attention to detail | Basic computer skills, attention to detail |
| Work Environment | Remote or flexible hours, part-time | Remote or on-site, part-time or full-time |
| Industry Usage | AI training, machine learning projects | AI, autonomous vehicles, healthcare, retail |
| Search & Comparison Intent | Yes | Yes |
Weekend Data Annotation and Data Labeler roles both involve labeling data for AI and machine learning. The main difference lies in scheduling; Weekend Data Annotation typically offers flexible, weekend-only hours, making it suitable for part-time workers or students. Data Labelers may work during regular hours and can be employed full-time or part-time. Both roles require similar skills and are used across industries like autonomous vehicles, healthcare, and retail. The choice depends on your availability and preferred work schedule.
Can I do weekend data annotation part-time?
Weekend data annotation jobs are often available as part-time positions, allowing flexibility for workers to choose weekend hours. These roles typically require basic skills in data labeling and may involve using annotation tools; scheduling depends on the employer's needs. Candidates should verify specific job postings for weekend availability and part-time options.
Does weekend data annotation have projects on weekends?
Weekend data annotation jobs often offer flexible schedules, and some projects may require work on weekends. However, availability depends on the employer and project deadlines, so not all data annotation roles are weekend-specific. It is common for part-time or freelance roles to include weekend work if needed.
What are the most commonly searched types of Data Annotation jobs in Princeton, NJ?
The most popular types of Data Annotation jobs in Princeton, NJ are:
What are popular job titles related to Weekend Data Annotation jobs in Princeton, NJ?
For Weekend Data Annotation jobs in Princeton, NJ, the most frequently searched job titles are:
What job categories do people searching Weekend Data Annotation jobs in Princeton, NJ look for?
The top searched job categories for Weekend Data Annotation jobs in Princeton, NJ are:
$160K - $220K/yr
Full-time
Re-posted 2 days ago
Job description
About Neon
Many large companies make billions each year by monetizing Americans' personal data. At Neon, we're finally cutting consumers in on the deal. Neon allows our users to make hundreds (or even thousands) of dollars per year by securely selling their anonymized data. We're backed by Lightspeed, Upper90, Upfront Ventures, and other cool investors.
About the role
Your mission is to turn Neon's raw consumer audio streams into the cleanest, most reliable training data on the market, and to build the commercial and operational engine that gets it into the hands of the world's leading AI labs.
As a Data Ops Lead, you'll own the end-to-end journey that takes raw recordings from our growing community of 500,000+ mobile users and delivers production-ready datasets to frontier labs. In practice, that means three things above all:
You'll work directly with our CEO on commercial priorities and help shape each deal, interface with buyer-side engineering and research teams at frontier labs to translate their exact specifications into deliverable dataset plans, and partner with internal engineering and external vendors to make sure the pipeline supports what we've sold. This is a foundational role: the datasets and processes you build are the product we sell.
We're a small, early-stage team moving fast. Expect roughly 60 hours per week, with occasional weekend work around launches and deadlines. We're upfront about this because we'd rather find people who are energized by that pace than surprise anyone after they join.
You have...
Bonus points
Many large companies make billions each year by monetizing Americans' personal data. At Neon, we're finally cutting consumers in on the deal. Neon allows our users to make hundreds (or even thousands) of dollars per year by securely selling their anonymized data. We're backed by Lightspeed, Upper90, Upfront Ventures, and other cool investors.
About the role
Your mission is to turn Neon's raw consumer audio streams into the cleanest, most reliable training data on the market, and to build the commercial and operational engine that gets it into the hands of the world's leading AI labs.
As a Data Ops Lead, you'll own the end-to-end journey that takes raw recordings from our growing community of 500,000+ mobile users and delivers production-ready datasets to frontier labs. In practice, that means three things above all:
- Structuring and managing the data deals that turn our recordings into revenue
- Holding every dataset to a quality bar that keeps buyers coming back
- Standing up human transcription, annotation and other operations, largely overseas, that make it all possible
You'll work directly with our CEO on commercial priorities and help shape each deal, interface with buyer-side engineering and research teams at frontier labs to translate their exact specifications into deliverable dataset plans, and partner with internal engineering and external vendors to make sure the pipeline supports what we've sold. This is a foundational role: the datasets and processes you build are the product we sell.
We're a small, early-stage team moving fast. Expect roughly 60 hours per week, with occasional weekend work around launches and deadlines. We're upfront about this because we'd rather find people who are energized by that pace than surprise anyone after they join.
You have...
- Authorization to work in the US.
- 5+ years of experience building and scaling data pipelines for AI/ML applications, with significant time spent on audio, speech, or multimodal data.
- A track record of structuring and delivering against data or dataset agreements with external partners: taking their requirements, turning them into clear specifications, and owning delivery end to end.
- Experience building and managing overseas or outsourced teams for data tagging, annotation, and QA, with a track record of maintaining quality and throughput across time zones.
- Deep ownership of data quality: designing QA processes, defining acceptance criteria, and catching problems before a customer ever sees them.
- Enough technical fluency to be credible on both sides of a deal. You understand digital audio fundamentals (sample rates, VAD, multichannel formats), can reason about how pipelines are built, and know what "good" looks like, even if you're not writing every line of code yourself.
- A "Founder's Mentality." You're comfortable building from zero and making high-stakes calls with incomplete information.
Bonus points
- A background working with audio data in some capacity.
- Direct experience with training data for TTS, ASR, speaker ID, or full-duplex conversational models.
- Familiarity with the modern audio stack (Librosa, FFmpeg, SoX, torchaudio) and cloud data infrastructure (S3, Redshift, BigQuery, or equivalent).
- An understanding of how high-quality, speaker-separated audio gets captured (for example, via WebRTC-based recording tools).
- Experience with active learning loops, human-in-the-loop QA systems, or corpus stratification for balanced dataset design.
- Prior experience leading a data or infrastructure team, including hiring and mentoring engineers.