Data Specialist​/Annotator; Image Labeller

Data Specialist / Annotator - Shape Future AI Products & Accelerate Scientific Discovery with Us!

Singer Instruments empower scientists in laboratories in over 60 countries to accelerate their research efforts on global challenges. We are looking for a Data Specialist / Annotator to create the high‑quality datasets that train our Computer Vision models, ensuring peak accuracy and safety before deployment.

Supported by a newly secured public funding grant, we are initiating an intensive engineering phase to transition a proprietary, laboratory‑validated technical imaging technology from a Technology Readiness Level (TRL) 4 Proof of Concept into an operationally ready, commercially viable TRL 7 multi‑tenant AI SaaS platform.

We are seeking a meticulous and analytical Data Specialist / Annotator to play a vital role in fueling our artificial intelligence capabilities. In this role, you will be responsible for creating the high‑quality datasets that train our Computer Vision and Generative AI systems. Working alongside our Machine Learning Engineers, you will curate, label, and audit massive amounts of visual and text data, ensuring our models achieve peak accuracy and safety before deployment.

Visual data in Life Sciences is notoriously complex, and you will act as the human‑in‑the‑loop, translating complex biological images into clean, structured training arrays.

Foundation models like SAM 3 and SAM 2 require precise, high‑quality visual prompts to adapt to niche datasets. You will provide the exact pixel‑level masks, polygons, and point prompts required to successfully fine‑tune SAM 3 for our specific use cases.

For our real‑time object detection pipelines (YOLO
26 / YOLO
11), accuracy depends entirely on bounding box precision. You will ensure thousands of training images are flawlessly labelled with minimal spatial error to prevent model confusion.

As the project scales and external third‑party data vendors are used for bulk annotation, you will lead the Quality Assurance (QA) function. You will build annotation guidelines, manage vendor pipelines, and run strict statistical audits on incoming data.

By cleansing raw data, managing versioning at the dataset level, and eliminating corrupt or mislabelled files early, you will prevent “garbage in, garbage out”—saving immense amounts of expensive AWS GPU compute time.

High‑precision annotation of visual datasets (images and videos) for tasks like object tracking, instance segmentation, and landmark detection using advanced labelling tools.

Review, filter, and structure raw data inputs, eliminating anomalies, duplicate records, or corrupt files to maintain high data integrity.

Perform rigorous quality checks on datasets labelled by internal teams or external third‑party vendors, identifying and correcting errors.

Collaborate closely with Machine Learning Engineers within an active Agile environment, participating in daily stand‑ups and tracking priorities using Jira and Confluence.

Draft, refine, and maintain detailed data labelling documentation and taxonomies to ensure consistency across the annotation pipeline.

2+ years of experience working as a Data Annotator, Data Specialist, or in a highly detailed data quality role.

Hands‑on experience with bounding boxes, polygons, semantic segmentation, and key‑point annotation for visual models (experience with tools like Labelbox, CVAT, or Robo Flow is highly advantageous).

Proven track record working within an Agile team structure, with daily proficiency using tools like Jira and Confluence.

Exceptional focus and accuracy when handling repetitive data tasks over long intervals.

Strong written and verbal communication to effectively translate model requirements into strict annotation rules.

Background or prior experience working with Life Sciences, Biotech, or Medical Imaging data (e.g., identifying cellular structures, tissue types, or laboratory imagery) is a major advantage.

Familiarity with Python or SQL to write basic scripts for automating data loading, file parsing,…