Seattle, WA · Remote
$130K - $160K/yr
Experience with dataset annotation/labeling tools and workflows (Roboflow, Labelbox, CVAT, or similar). * Strong communication skills. Preferred Qualifications: * Experience with continuous learning ...
New
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Seattle, WA · Remote
$130K - $160K/yr
Experience with dataset annotation/labeling tools and workflows (Roboflow, Labelbox, CVAT, or similar). * Strong communication skills. Preferred Qualifications: * Experience with continuous learning ...
New
$56.2K is the 25th percentile. Wages below this are outliers.
$51.2K - $56.6K
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The median wage is $60.7K / yr.
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To thrive as a CVAT Annotation specialist, you need keen attention to detail, familiarity with data labeling concepts, and a solid understanding of image and video annotation techniques. Experience with CVAT (Computer Vision Annotation Tool) or similar annotation platforms is essential, and knowledge of basic computer vision or machine learning principles can be beneficial. Strong organizational skills, patience, and the ability to work both independently and collaboratively help set top performers apart. These skills ensure high-quality, accurate annotations that are crucial for training effective AI and machine learning models.
As a CVAT Annotation professional, your daily tasks generally include reviewing and labeling large volumes of images or videos using the CVAT platform, ensuring precise and consistent annotations according to project guidelines. You may also be responsible for double-checking the accuracy of your labels, collaborating with data scientists or project managers to clarify requirements, and providing feedback to improve annotation processes. Depending on the project's complexity, you might handle multiple data types or contribute to developing new labeling standards. This work is usually part of a team environment, where clear communication and quality control are highly valued. Successfully handling these responsibilities helps create reliable datasets for computer vision projects and AI systems.
A CVAT annotation job involves labeling images or videos using the Computer Vision Annotation Tool (CVAT) to create datasets for machine learning models. Annotators draw bounding boxes, polygons, or other shapes around objects of interest to help train AI systems in tasks like object detection, segmentation, and classification. This job requires attention to detail, familiarity with annotation guidelines, and sometimes domain-specific knowledge depending on the project.
$130K - $160K/yr
Full-time
Medical, Dental, Vision, Retirement, PTO
Posted 2 days ago
New
About Us:
Outpost is building the backbone of freight. We’re reinventing how supply chain infrastructure works in America with carrier agnostic truck terminals. As a vertically integrated real estate, operations, and technology company, we acquire and operate mission-critical real estate across the country to serve the largest logistics providers in the world. Backed by $1B from Greenpoint Partners, we’re scaling and building the most valuable logistics network in the country.
We thrive on accountability, integrity, and a shared drive to raise the bar. If you’re excited to reshape the industry alongside a high-performance team with a championship mindset that executes relentlessly, welcome aboard.
Role Summary:
Our platform combines AI-powered gate automation, computer vision, and operational software to help logistics operators run smarter, faster facilities. We're a small, high-conviction team shipping real software that ends up in real yards, at real gates, moving real freight; and we're growing fast, with revenue set to grow 10X over the next 18 months.
As we onboard more customers, our computer vision system sees more camera layouts, identifier types, and edge cases than ever. We need someone to own accuracy end-to-end: measuring it, understanding why we get it wrong, and turning that into the labeled data that makes our models better. Today that's mostly measurement and curation. Once the pipeline matures and moves into maintenance mode, we expect this role to also contribute fixes to the product itself, not just flag issues for others to resolve.
Key Responsibilities:
Own tracking and reporting of CV accuracy metrics, per customer and per identifier type.
Investigate misclassifications and false negatives, categorize root causes, and identify patterns across customers and yards.
Curate, label, and prioritize datasets for model retraining, partnering closely with our ML and CV engineers.
Build and improve the continuous learning pipeline so new models ship weekly with minimal manual engineering effort.
Define functional acceptance criteria for CV accuracy per customer and track progress against them.
Translate accuracy findings into decisions the engineering team and customer-facing stakeholders can act on.
As the pipeline matures, expect to move from flagging issues to fixing them directly; building the labeling/preprocessing tooling, running retraining jobs, and owning fixes for the error patterns you find, not just reporting them.
What You Can Expect:
Direct ownership over the metric that decides whether our product works in the real world.
A small team that moves fast, argues in good faith, and trusts engineers to make decisions.
Real influence on what the ML team builds next; your findings drive the roadmap, not the other way around.
Problems grounded in the physical world: gates, cameras, trucks, and yards.
Qualifications:
3+ years in a data quality, ML data engineering or applied ML role.
Experience working with computer vision or object detection systems in production.
Comfortable writing Python for data analysis, pipeline automation, and dataset tooling.
Strong analytical rigor, comfortable digging into large volumes of imagery/data to find patterns, not just running a script and reporting a number.
Experience with dataset annotation/labeling tools and workflows (Roboflow, Labelbox, CVAT, or similar).
Strong communication skills.
Preferred Qualifications:
Experience with continuous learning or active learning pipelines for production ML systems.
Familiarity with OCR systems and identifier recognition (plates, container numbers, etc.).
Experience partnering with customer success or support teams on quality metrics.
Background in QA/test engineering for ML systems.
Experience with Roboflow specifically.
Our Stack:
Python · Roboflow · VLM/OCR pipelines · GCP (GCS) · PostgreSQL · Snowflake · Node.js/TypeScript
Benefits:
Title Commensurate with Experience
Comprehensive Benefits Package including Health, Dental, and Vision Insurance
401(k) Retirement Plan Matching
18 Paid Holidays
Unlimited PTO
Friday Team Lunches
Base salary range: $130,000 – $160,000 annually, depending on experience and qualifications. Total compensation includes a discretionary bonus; a complete compensation and benefits summary will be provided during the interview process.
Outpost is an Equal Opportunity Employer and Prohibits Discrimination of Any Kind.
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Food services and drinking places
201 - 500 Employees
Milwaukee, WI, US
1970