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Data Annotation Engineer Jobs in Milton, FL (NOW HIRING)

Tocaro Blue

Data Analyst

Pensacola, FL · On-site

$60K - $80K/yr

Data Analyst Looking for an opportunity to make an impact at a fast growing, investor-backed AI/ML ... Working closely with our ML engineering team, you'll help define and refine annotation standards as ...

Tocaro Blue

Data Analyst

Pensacola, FL · On-site

$60K - $80K/yr

Data Analyst Looking for an opportunity to make an impact at a fast growing, investor-backed AI/ML ... Working closely with our ML engineering team, you'll help define and refine annotation standards as ...

All JobsData Annotation Engineer JobsData Annotation Engineer Jobs in Milton, FL

Data Annotation Engineer information

See Milton, FL salary details

$46.1K

$132.1K

$176.5K

How much do data annotation engineer jobs pay per year?

As of Jul 15, 2026, the average yearly pay for data annotation engineer in Milton, FL is $132,108.00, according to ZipRecruiter salary data. Most workers in this role earn between $75,300.00 and $175,600.00 per year, depending on experience, location, and employer.

What are the main challenges faced by Data Annotation Engineers in their daily work?

One of the main challenges Data Annotation Engineers face is ensuring consistent accuracy and quality in labeling large and often complex datasets. Attention to detail is critical, as even small errors can significantly affect machine learning model performance. Additionally, engineers must frequently adapt to evolving annotation guidelines and emerging data types, which requires ongoing learning and flexibility. Collaboration with data scientists and project managers is common to clarify requirements and resolve ambiguities, making strong communication skills essential for success.

What are the key skills and qualifications needed to thrive in the Data Annotation Engineer position, and why are they important?

To thrive as a Data Annotation Engineer, you need a strong background in data analysis, attention to detail, and familiarity with annotation processes, often supported by a degree in computer science or a related field. Proficiency with annotation tools like Labelbox, CVAT, or VIA, and understanding of data formats used in machine learning, is commonly required. Excellent communication, collaboration, and organizational skills help you effectively manage projects and cooperate with cross-functional teams. These abilities are crucial for delivering high-quality labeled data, which directly impacts the performance of AI and machine learning models.

Does data annotation really pay?

Data annotation engineers can earn competitive wages, often paid hourly or per task, with pay rates varying based on experience, complexity of annotations, and the platform or employer. Entry-level roles may start at minimum wage, while experienced annotators or those with specialized skills can earn higher salaries or freelance rates. Overall, data annotation can provide a reliable income, especially for remote or flexible work arrangements.

What is the highest salary for data annotator?

The highest salary for a data annotation engineer can reach up to $80,000 to $100,000 annually, depending on experience, location, and the complexity of annotation tasks. Senior roles or those with specialized skills in tools like Labelbox or CVAT may earn higher compensation. Salaries vary widely across companies and regions but generally reflect the technical skills required for high-quality data labeling.

What is a data annotation engineer?

A data annotation engineer is a professional responsible for labeling and annotating data, such as images, text, or videos, to prepare it for machine learning models. They often use specialized tools and follow guidelines to ensure data quality, supporting the development of AI systems.

How hard is it to get hired by data annotation?

Getting hired as a data annotation engineer typically requires basic computer skills, attention to detail, and familiarity with annotation tools. Many positions are entry-level and may not require advanced degrees, but strong accuracy and consistency are important for success in the role.

What is a Data Annotation Engineer job?

A Data Annotation Engineer is responsible for labeling and annotating data—such as text, images, audio, or video—to train machine learning models. They ensure that data is accurately categorized and structured to improve model performance. This role often involves using specialized annotation tools, following detailed guidelines, and working closely with data scientists and AI teams. Data Annotation Engineers play a crucial role in the development of AI applications by providing high-quality labeled datasets for supervised learning.

What are popular job titles related to Data Annotation Engineer jobs in Milton, FL? For Data Annotation Engineer jobs in Milton, FL, the most frequently searched job titles are:
What cities near Milton, FL are hiring for Data Annotation Engineer jobs? Cities near Milton, FL with the most Data Annotation Engineer job openings:
Data Annotation Engineer jobs near you

Robotics Data Pipeline Engineer - Multimodal Data

Persona AI

Pensacola, FL • On-site

$108K - $130K/yr

Full-time

Medical, PTO

Re-posted 6 hours ago

Job description

Job Title: Robotics Data Pipeline Engineer - Multimodal Data
Department: Software
Reports To: Teleoperations Lead
Employment Type: Full-Time
Location: Houston, TX or Pensacola Fl
Who We Are
Persona AI is building humanoid robots for the most demanding environments in heavy industry - shipyards, steel mills, fabrication facilities, and offshore platforms - performing welding, grinding, maintenance, inspection, and material-handling work that is dangerous, physically demanding, and increasingly difficult to staff.
We are backed by leading strategic and financial investors and engaged with global industrial leaders across Korea, Japan, the United States, and Singapore. Korea is the center of gravity for our early commercial strategy, anchored by relationships with the world's leading shipbuilders and steelmakers. Our work spans both the robot platform itself and the systems, partners, and playbooks required to deploy it at scale.
Why Join Persona AI?
  • We offer competitive compensation, a performance-based bonus, 99% employer covered medical benefits, early-stage equity, competitive PTO, and a company-wide paid winter break between December 24th and January 2nd.
  • You'll shape technology that's redefining the possibilities of robotics and human interaction.
  • Work alongside passionate teammates who value creativity, and continuous learning.
  • Enjoy full access to advanced tools,

About the Role
As a Data Pipeline Engineer, you will architect and scale the data infrastructure that feeds our foundation models. Your primary mission is to extract, augment, and align human dexterous manipulation data from massive complex, multi-sensor and egocentric video datasets. Crucially, you will build advanced post-processing algorithms to perform deep force analysis and infer hidden states from raw data-such as processing direct force-torque outputs to quantify grasp dynamics, estimating contact forces from visual cues, extrapolating heavily occluded hand positions, or deriving 3D geometry from 2D frames. You will use spatial, temporal, and cross-modal data augmentation to multiply the value of every minute of data our teleoperation team collects.
What You Will Be Doing
  • Multimodal Data Pipelines: Architect end-to-end ingestion pipelines that take raw, unstructured recordings-egocentric video, teleoperation sessions, third-party open datasets-and produce indexed, queryable, training-ready datasets. This includes temporal segmentation of long recordings into action clips, metadata and scene-graph extraction, embedding-based retrieval, and language annotation workflows.
  • Force Analysis & Hidden State Inference: Design cross-modal validation systems that verify video, proprioception, force/haptic signals, and language annotations agree with each other-e.g., reprojecting robot state into the image plane to confirm video-state consistency, and VLM-assisted checks that instructions match observed behavior.
  • Kinematic Retargeting & Alignment: orchestrating hand-tracking, segmentation, depth estimation, 3D reconstruction, and pose-tracking modules; retargeting human demonstrations into robot trajectories; and running simulation-in-the-loop validation (kinematic feasibility, physics replay, motion-consistency filtering) so synthesized data is physically grounded, not just visually plausible.
  • Advanced Data Augmentation: Implement robust data augmentation strategies (spatial transformations, temporal scaling, synthetic viewpoints, and sensor noise injection) to expand expert trajectories and improve the robustness of our learning models.
  • Teleoperation Synchronization: unified state-action representations across differing embodiments, coordinate frames, rotation conventions, gripper/hand parameterizations, and sampling rates-with per-dimension validity masking and per-source normalization so that adding a new robot or sensor is a configuration change, not a rewrite.
  • Close the loop with data consumers: build the tooling that lets researchers query, visualize, and audit datasets (clip browsers, trajectory viewers, annotation review UIs), and turn model-failure analyses into new curation rules and targeted re-collection requests.

What We Are Looking For
  • Education: M.S., or Ph.D. in Computer Science, Data Engineering, Machine Learning, Robotics, Mechanical Engineering, or a related field.
  • Programming & ML Frameworks: Deep expertise in Python and extensive experience with PyTorch, specifically in handling custom dataloaders for multimodal datasets.
  • Force & Time-Series Data Processing: Experience analyzing and processing complex time-series data from force-torque (F/T) sensors, load cells, or tactile arrays, ensuring pristine alignment with visual frames.
  • Video Processing Expertise: Mastery of video processing pipelines and libraries (OpenCV, FFmpeg, Decord) and managing the I/O bottlenecks of terabyte-scale video datasets.
  • Solid working knowledge of 3D geometry and robotics data: coordinate frames and transforms, rotation representations, camera intrinsics/extrinsics, forward/inverse kinematics, URDF-enough to build automated checks that catch geometric inconsistencies in the data.
  • Data Augmentation: Proven ability to implement programmatic and generative data augmentation techniques for computer vision and time-series data.

Bonus Skills
  • Experience with NVIDIA's robotic software stack (Open X-Embodiment, DROID, AgiBot World, EgoDex, or similar).
  • Familiarity with the modern perception toolbox as a user: segmentation (SAM-family), monocular depth, hand/body pose estimation (MANO/SMPL), 6-DoF object pose tracking, point tracking-you don't need to train these models, but you should be comfortable composing and evaluating them in a pipeline
  • Familiarity with distributed data processing systems (Ray, Apache Spark) for cluster computing.
  • Background in generating or utilizing synthetic robotic data via simulation (Omniverse, MuJoCo).
  • Experience integrating spatial awareness or tactile data representations (e.g., Fourier encoding) into visual pipelines.

Persona AI is an Equal Opportunity Employer.
All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, age, disability, veteran status, or any other characteristic protected by applicable federal, state, or local law.

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