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Manager, PgM Tech - Frontier Labs

5 - 10 years

7 - 12 Lacs

hyderabad

Posted:17 hours ago| Platform: Naukri logo

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Skills Required

process design automation project management analytical data collection research stakeholder management operations robotics auditing

Work Mode

Work from Office

Job Type

Full Time

Job Description

We are looking for a driven and detail-oriented

L5B Program Manager

to join our

Frontier Labs AI team

, focused on building high-quality, multi-modal data pipelines to support advanced model development and foundational research.
In this role, you will lead the

end-to-end execution

of AI data labeling workflows across

text, image, audio, video, and instruction-tuned datasets

, partnering closely with researchers, data scientists, product managers, and annotation vendors. You will play a critical role in

scaling and operationalising labeling operations

, ensuring that the data used to train and evaluate cutting-edge models is accurate, diverse, and aligned with evolving research needs.
This is a hands-on role for someone who thrives in

high-ambiguity, high-velocity environments

and can bring structure and discipline to rapidly evolving labeling workflows
What You Will Do

Program Execution Delivery

  1. Manage AI data labeling programs from

    scoping to delivery

    , ensuring high-quality annotations at scale.
  2. Translate

    Frontier Labs research needs

    into concrete annotation specs, rubrics, and task designs.
  3. Own timelines, throughput plans, and quality controls for critical datasets used in LLM training and evaluation.

Stakeholder Management

  1. Partner with researchers, data scientists, product, and ops to ensure labeling goals are aligned with model objectives.
  2. Work cross-functionally to drive task clarity, resolve ambiguity, and incorporate feedback into successive batches.
  3. Act as the

    single-threaded owner

    for specific labeling programs, managing internal and external partners.

Operational Infrastructure

  1. Develop and refine

    batching strategies

    ,

    smart sampling plans

    , and

    audit workflows

    .
  2. Drive

    QA processes

    , including golden set calibration, rubric refinement, and disagreement adjudication.
  3. Ensure traceability from

    raw inputs to final labeled outputs

    , and track quality regressions over time.

Process Design Automation

  1. Identify opportunities to apply

    model-in-the-loop labeling

    ,

    active learning

    , or

    self-checking pipelines

    .
  2. Collaborate with tool owners and engineers to integrate annotation workflows with internal tooling systems.
  3. Own feedback loops that enable raters to improve over time and reduce error variance
What You Will Need

Bachelor s degree

in Engineering, Data Science, Linguistics, or related technical/analytical field.

5+ years

of program or project management experience in AI/ML, data ops, or labeling infrastructure.
Demonstrated ability to manage

end-to-end data pipelines

in AI/ML or research environments.
Strong working knowledge of

Robotics, Physical AI Data labeling tasks

, such as:
  1. Object detection and recognition
  2. Semantic Instance Segmentation
  3. Depth Pose Estimation
  4. Grasp Detection
  5. Action Segmentation
  6. Trajectory Labeling
  7. Prompt-response evaluation
  8. Instruction tuning
  9. Dialogue evaluation
  10. Vision-language QA
  11. Video slot tagging
  12. Image Tagging
  13. Documentation Extraction
  14. Data collection annotation
  15. HRI
Experience collaborating with research or model teams to scope data collection requirements.
Excellent written and verbal communication skills
Preferred Qualifications ----
  1. Experience in

    frontier AI research environments

    , such as foundation model labs or GenAI startups.
  2. Familiarity with tools like

    Label Studio, Scale AI, SuperAnnotate, Snorkel Flow, or in-house annotation platforms

    .
  3. Understanding of LLM training and evaluation lifecycles.
  4. Experience working with

    human-in-the-loop systems

    or model-assisted labeling pipelines.
  5. Familiarity with

    multilingual or multi-cultural annotation programs

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