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2Nd Shift Data Annotation Jobs (NOW HIRING)

Teleskope

Jr. AI Engineer - Data Annotation

New York, NY · On-site

$75K - $90K/yr

... shift. You'll work shoulder-to-shoulder with data scientists and ML engineers, people who think ... What You'll Do * Do hands-on data annotation and quality control (labeling, reviewing, and ...

Truly Free

2nd Shift Supervisor

Williamsburg, MI · On-site

Supervisor, 2nd shift Department: Operations Supervisor: Production Supervisor Location ... Support data integrity within ERP, ensuring accurate reporting of production results, material ...

Truly Free Home

2nd Shift Supervisor

Williamsburg, MI · On-site

$65K - $77K/yr

Supervisor, 2nd shift Department: Operations Supervisor: Production Supervisor Location ... Support data integrity within ERP, ensuring accurate reporting of production results, material ...

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All Jobs2Nd Shift Data Annotation Jobs

2Nd Shift Data Annotation information

Is 2nd shift data annotation a part-time job?

2nd shift data annotation jobs can be either full-time or part-time, depending on the employer and specific role. Many positions offer flexible schedules, but part-time opportunities are common for those seeking fewer hours or supplemental income. Candidates should review job postings for specific shift and hour requirements.

What is a 2nd shift data annotation?

A 2nd Shift Data Annotation job involves labeling and categorizing data, such as images, audio, or text, to help train artificial intelligence and machine learning models. The '2nd shift' typically refers to working hours in the late afternoon to evening, often from around 3 PM to 11 PM. Data annotators play a crucial role in ensuring the accuracy and quality of data used for machine learning. This work can be detail-oriented and may be performed remotely or in an office setting.

What are the key skills and qualifications needed to thrive as a 2nd shift data annotation specialist?

To thrive as a 2nd Shift 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 equivalent. Proficiency in annotation platforms, basic computer operations, and sometimes specialized tools like image or audio tagging software is typically required. Reliability, time management, and the ability to work independently during non-standard hours are crucial soft skills for this role. These skills ensure accurate data labeling, efficient workflow, and consistent quality in supporting machine learning projects.

What is the difference between 2Nd Shift Data Annotation vs Data Labeler?

Aspect2Nd Shift Data AnnotationData Labeler
CredentialsHigh school diploma or equivalent, basic computer skillsHigh school diploma or equivalent, attention to detail
Work EnvironmentData centers, offices, or remote settings during evening/night hoursOffices or remote, often during daytime hours
Industry UsageTech, AI, machine learning companiesAI, autonomous vehicles, tech industries
Job FocusAnnotating data during second shift hoursLabeling data for machine learning models

Both roles involve data annotation and are common in AI and machine learning industries. The main difference lies in shift timing, with 2Nd Shift Data Annotation working during evening or night hours, while Data Labelers typically work during regular daytime hours. The skills and industry usage are similar, making them closely related roles in data preparation for AI applications.

What are some common challenges faced by 2nd shift data annotation professionals and how can they be addressed?

Working the 2nd shift in data annotation often involves adjusting to a non-traditional work schedule, which can impact work-life balance and communication with daytime teams. Additionally, maintaining focus and accuracy during extended periods of repetitive tasks is essential, as data quality directly affects machine learning outcomes. To address these challenges, it's helpful to establish a consistent routine, leverage communication tools for asynchronous updates with colleagues, and take regular breaks to maintain accuracy and prevent fatigue.

What cities are hiring for 2Nd Shift Data Annotation jobs?

Cities with the most 2Nd Shift Data Annotation job openings:

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

The most popular types of Data Annotation jobs are:

What states have the most 2Nd Shift Data Annotation jobs?

States with the most job openings for 2Nd Shift Data Annotation jobs include:

    2Nd Shift Data Annotation jobs near you

    Jr. AI Engineer - Data Annotation

    Teleskope

    New York, NY • On-site

    $75K - $90K/yr

    Full-time

    Medical, Dental, Vision, Retirement, PTO

    Posted 27 days ago

    Job description

    About Teleskope
    Teleskope is redefining data security for the AI era with the only dedicated platform that combines precise visibility with automated remediation. Teleskope continuously scans, catalogs, and classifies data in-motion and at-rest while automating policy-based actions, helping organizations proactively manage data sprawl while securely enabling AI adoption.
    Fresh off our $25 million Series A round, Teleskope is entering a high-growth phase backed by top-tier investors and exceptional product-market fit.
    About the Role
    We're looking for a hungry, hands-on AI Engineer to join our data science team. You'll do the work directly, labeling and reviewing classification data and running QC, but you won't just execute. You'll bring an engineer's mindset to it: when a task is repetitive, you script it; when quality is hard to measure, you build a way to measure it. You'll use Python, SQL, and agentic development tools to make annotation and QC faster, more consistent, and more scalable.
    This is a rapidly evolving role, and we expect you to context switch comfortably as priorities shift. You'll work shoulder-to-shoulder with data scientists and ML engineers, people who think about data the way you do, and the labels and quality signals you produce feed directly into the models that protect real customers' most sensitive data. The work is high-impact and the data is messy; a big part of the job is learning, through the work itself, what it takes to make it usable.
    This is a hybrid role requiring 3+ days in-office in New York City.
    Who Should Apply
    We're looking for someone with programming ability, dependability, and the drive to learn on the job. Recent grads are welcome, and CS and STEM backgrounds are a great fit. What matters most is that you can think critically, you're excited to work through messy data, you can context switch as priorities change, and you want to grow fast in a fast-moving environment.
    What You'll Do
    • Do hands-on data annotation and quality control (labeling, reviewing, and correcting classification outputs) as a core member of the data science pipeline.
    • Take ownership of improving and scaling the process: find the bottlenecks, repetitive steps, and sources of error, and fix them with Python, SQL, and agentic workflows.
    • Build and run quality control checks that catch labeling errors, measure inter-annotator agreement, and surface systematic issues before they reach production.
    • Work closely with data scientists and ML engineers to close the loop between real-world performance and model improvement.
    • Context switch across labeling, quality analysis, scripting, and process work as priorities evolve.
    • Document QC processes and annotation guidelines to support team scaling and onboarding.
    About You
    • Solid programming ability, with hands-on Python experience and a willingness to dig into scripts, SQL, and data wrangling.
    • Comfortable using agentic development tools, or eager to ramp up on them fast.
    • A quality-first mindset. You notice when something is off in the data and won't let it slide.
    • Dependable and adaptable. Teammates can count on you, and you stay effective as priorities shift.
    • Energized by messy, real-world data and by working alongside other data-minded people.
    • Hungry, self-directed, and ready to grow with Teleskope as we scale.
    Nice to Have
    • Familiarity with feedback loops in ML systems and how label quality connects to model performance.
    • Experience with annotation platforms (Label Studio, Prodigy, Scale, or custom-built systems).
    • Familiarity with active learning or online learning approaches.
    • Experience with SQL and building lightweight dashboards to track quality metrics.
    • Background in NLP or text classification workflows.
    What You'll Get
    • A seat alongside data scientists and ML engineers, data-minded people to learn from every day.
    • Work that visibly matters. Your labels feed the models that protect real customers' most sensitive data.
    • Ownership of the annotation and quality processes that determine classification accuracy across the platform.
    • Room to grow fast, with real ownership from day one as Teleskope scales.
    • A beautiful, well-stocked office in NYC's Financial District.
    • Flexible vacation and work-from-home days.
    • Competitive salary and meaningful equity.
    • Health, vision, dental, 401k, and more benefits, heavily subsidized by Teleskope.
    What We Value
    At Teleskope, we value builders who care about the details. This role is for someone who sees data quality not as a support function but as a force multiplier, and who takes pride in making the people around them more effective. We look for dependable teammates who ship iteratively, take ownership, and understand that great ML starts with great data.

    Apply