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Full Time Machine Learning Data Annotation Jobs in Detroit, MI

FastTek

Machine Learning Engineer

Dearborn, MI

Machine Learning Engineer #1058742 Position Description: We are seeking an experienced AI Engineer ... This role combines expertise in Data Science, Software Engineering, and MLOps to deliver scalable ...

Mariana Minerals

Machine Learning Engineer

Ann Arbor, MI · On-site

$120K - $180K/yr

... data-driven decision-making. The Role Mariana Minerals is building the critical minerals supply ... As a Machine Learning Engineer at Mariana, you'll help build and improve the machine learning ...

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All JobsMachine Learning Data Annotation JobsFull Time Machine Learning Data Annotation JobsFull Time Machine Learning Data Annotation Jobs in Detroit, MI

Full Time Machine Learning Data Annotation information

See Detroit, MI salary details

$37.1K

$121.5K

$194.5K

How much do full time machine learning data annotation jobs pay per year?

As of Sep 3, 2026, the average yearly pay for full time machine learning data annotation in Detroit, MI is $121,507.00, according to ZipRecruiter salary data. Most workers in this role earn between $97,500.00 and $134,600.00 per year, depending on experience, location, and employer.

What is a full time machine learning data annotation job?

Full time machine learning data annotation jobs involve labeling, tagging, or categorizing data such as images, text, audio, or video to help train machine learning models. Data annotators play a crucial role in ensuring that AI systems learn from high-quality, accurately labeled datasets. These positions often require attention to detail, consistency, and sometimes familiarity with the subject matter or specialized tools. Full-time roles may be remote or onsite and can span industries like autonomous vehicles, healthcare, retail, and more.

What are some common challenges faced by machine learning data annotators, and how are these typically addressed within a team?

Machine learning data annotators often encounter challenges such as maintaining consistency in labeling, handling ambiguous data, and meeting tight deadlines for large datasets. Teams usually address these by establishing clear annotation guidelines, conducting regular training sessions, and implementing quality assurance processes like peer reviews and spot checks. Collaboration with data scientists and project managers is also common, ensuring that annotators can ask questions and clarify uncertainties, leading to higher-quality labeled data and a supportive work environment.

What are the key skills and qualifications needed to thrive as a full time machine learning data annotation specialist, and why are they important?

To thrive as a Full Time Machine Learning Data Annotation Specialist, you need strong attention to detail, basic data literacy, and familiarity with data labeling concepts, often supported by a high school diploma or equivalent. Proficiency in specialized annotation platforms, spreadsheet tools, and sometimes knowledge of Python or labeling frameworks is typically required. Reliability, patience, and effective communication are valuable soft skills for ensuring accuracy and collaborating with team members. These skills and qualities are crucial because they directly impact the quality of training data, which is essential for developing effective machine learning models.

What is the difference between Full Time Machine Learning Data Annotation vs Data Labeling Specialist?

AspectFull Time Machine Learning Data AnnotationData Labeling Specialist
CredentialsHigh school diploma or equivalent; some roles prefer technical certificationsHigh school diploma or equivalent; training often provided on the job
Work EnvironmentOffice or remote; collaborative with data science teamsRemote or office; focused on labeling tasks
Industry UsageUsed across AI/ML companies, tech firms, and startupsCommon in AI/ML, data services, and outsourcing companies
Job FocusCreating labeled datasets for machine learning modelsAnnotating data such as images, videos, or text for AI training

Full Time Machine Learning Data Annotation involves creating high-quality labeled datasets for AI models, often requiring technical understanding. Data Labeling Specialists focus on annotating data accurately, typically with less emphasis on technical skills. Both roles are essential in AI development but differ mainly in scope and technical complexity.

What are popular job titles related to Full Time Machine Learning Data Annotation jobs in Detroit, MI?

For Full Time Machine Learning Data Annotation jobs in Detroit, MI, the most frequently searched job titles are:

What job categories do people searching Full Time Machine Learning Data Annotation jobs in Detroit, MI look for?

The top searched job categories for Full Time Machine Learning Data Annotation jobs in Detroit, MI are:

Full Time Machine Learning Data Annotation jobs near you
Infographic showing various Full Time Machine Learning Data Annotation job openings in Detroit, MI as of July 2026, with employment types broken down into 19% Full Time, 7% Part Time, 68% Contract, and 6% Nights. Highlights an 4% Physical, and 96% Remote job distribution, with an average salary of $121,507 per year, or $58.4 per hour.

Software Engineer II - Fleet Enablement & Insights (Annotation Platform)

Torc Robotics

Ann Arbor, MI • On-site

$95K - $130K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 19 days ago

Job description

About the Company
At Torc, we have always believed that autonomous vehicle technology will transform how we travel, move freight, and do business.
A leader in autonomous driving since 2007, Torc has spent over a decade commercializing our solutions with experienced partners. Now a part of the Daimler family, we are focused solely on developing software for automated trucks to transform how the world moves freight.
Join us and catapult your career with the company that helped pioneer autonomous technology, and the first AV software company with the vision to partner directly with a truck manufacturer.
Meet the Team
The Software Engineer II will be a core member of the Fleet Enablement & Insights team, building Torc's in-house annotation platform: the web-based tooling that turns multi-sensor autonomy data into the labeled datasets that train and validate the autonomous truck platform. This role develops the interactive 2D/3D annotation editor and the services behind it which fuses HD map data and multi-camera, lidar, and other sensor context into the labeling workflow. The platform also serves adjacent use cases across the data organization, including data QC and scene-selection review. The ideal candidate is a collaborative engineer who thrives in a fast-paced environment and is passionate about high-performance visualization of large sensor datasets, thoughtful annotator-facing UX, and the pipelines that connect human review to machine learning at scale. This position offers the opportunity to work at the intersection of software engineering, computer graphics, and machine learning with a direct line of sight to the data that trains and validates the AV stack.
What You'll Do
  • Design, develop, and maintain the TypeScript/React web application for 2D and 3D annotation, including cuboid and polygon editing, cross-frame interpolation and track propagation, attribute editing, and review-first (accept/reject) workflows.
  • Build high-performance point cloud and image rendering with three.js/WebGL: octree/LOD-based streaming formats, predictive prefetching for smooth frame scrubbing, camera-LiDAR projection, and multi-sensor overlays across a high camera count.
  • Design and build the services behind the editor: label storage and versioning, task assignment and QA workflow, authentication, and multi-user isolation guardrails.
  • Integrate pre-labeling and pseudo-labeling pipeline outputs into the annotation workflow, and instrument acceptance-rate and throughput metrics that drive the auto-labeling feedback loop.
  • Fuse HD map data into annotation and QC workflows as priors and reference layers for labeling and validation.
  • Build data converters and ingestion paths from Torc's multi-sensor scene data (multiple LiDARs, many cameras, calibration data) into the platform's formats.
  • Deliver dataset exports compatible with downstream ML training and validation consumers, with the lineage and auditability the safety case requires.
  • Leverage AWS cloud services and Databricks adjacency to host scene data and deploy scalable, reliable services.
  • Collaborate closely with the Data Annotation team (the platform's primary users), Autonomy/ML, Scene Selection, Mapping, and Data Engineering to align the tool with real annotator workflows and downstream requirements.
  • Participate in agile ceremonies, sprint planning, and weekly demos with annotators and stakeholders to keep the tool grounded in real use.
  • Contribute to a culture of engineering excellence through code reviews, documentation, and knowledge sharing, including hardening prototype code into production systems.
  • Identify and address technical debt, performance bottlenecks, and reliability gaps across the annotation platform.

What You'll Need to Succeed
  • Strong proficiency in TypeScript and React, with experience building and shipping production-quality web applications.
  • Experience with browser-based 3D graphics (three.js, WebGL, or similar), or strong graphics fundamentals and a demonstrated ability to ramp quickly.
  • Proficiency in Python for building production backend services and APIs.
  • Experience working with large binary or sensor datasets (point clouds, imagery, video) and optimizing data-heavy user interfaces for performance.
  • Strong proficiency with SQL and hands-on experience with PostgreSQL for label, task, and metadata storage.
  • Experience with AWS services for hosting data and deploying services.
  • Proficiency with Git and GitHub for version control, branching strategies, and collaborative code workflows.
  • Familiarity with JIRA or similar project management tools for tracking tasks, bugs, and sprint deliverables.
  • Solid understanding of software engineering fundamentals including data structures, algorithms, and system design.
  • Strong written and verbal communication skills with the ability to effectively collaborate across technical and non-technical stakeholders, including non-engineer users of the tools you build.
  • Bachelor's Degree in Computer Science, Robotics, Electrical Engineering or related technical field plus demonstrates competences and technical proficiencies typically acquired through 4+ years of experience or;
  • Master's Degree in Computer Science, Robotics, Electrical Engineering or related technical field plus demonstrates competences and technical proficiencies typically acquired through 0-3+ years of experience.

Bonus Points
  • Experience with point cloud rendering and streaming formats (octree/LOD structures such as Potree or COPC, 3D Tiles, LAS/LAZ) or large-scale spatial data structures.
  • Experience building or extending annotation/labeling tools.
  • Familiarity with robotics data and visualization ecosystems (e.g., Rerun, Foxglove, MCAP, ROS bags).
  • Background in machine learning workflows for perception: object detection, tracking, segmentation, model-assisted or auto-labeling, active learning.
  • Understanding of multi-view geometry and sensor calibration (camera intrinsics/extrinsics, LiDAR-camera projection, time synchronization).
  • Prior experience working with High-Definition (HD) maps, map formats (e.g., OpenDRIVE, NDS, Lanelet2), or fusing map data into perception or labeling workflows.
  • Familiarity with PostGIS or spatial queries for geometry operations and geospatial data management.
  • Experience with Databricks or similar platforms for large-scale data processing.
  • Contributions to open-source graphics, geospatial, annotation, or data engineering projects.

Perks of Being a Full-time Torc'r
Torc cares about our team members and we strive to provide benefits and resources to support their health, work/life balance, and future. Our culture is collaborative, energetic, and team focused. Torc offers:
  • A competitive compensation package that includes a bonus component and stock options
  • 100% paid medical, dental, and vision premiums for full-time employees
  • 401K plan with a 6% employer match
  • Flexibility in schedule and generous paid vacation (available immediately after start date)
  • AD+D and Life Insurance

At Torc, we're committed to building a diverse and inclusive workplace. We celebrate the uniqueness of our Torc'rs and do not discriminate based on race, religion, color, national origin, gender (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity, gender expression, age, veteran status, or disabilities.
Even if you don't meet 100% of the qualifications listed for this opportunity, we encourage you to apply.
Our compensation reflects the cost of labor across several geographic markets. Pay is based on a number of factors and may vary depending on job-related knowledge, skills, and experience. Torc's total compensation package will also include our corporate bonus and stock option plan. Dependent on the position offered, sign-on payments, relocation, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits.
Job ID: 102797
Hiring Range for Job Opening
US Pay Range
$139,000-$166,800 USD

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