ZipRecruiter

Log In

1

Map Annotation Jobs (NOW HIRING)

Hired Recruiters

Full Stack Engineer

Austin, TX · On-site +1

As a full stack software engineer at the company, you'll work on various user-facing applications and tools for data management, annotation, and mapping. You will: * Work closely with design and ...

Figure

Data Quality Manager

San Jose, CA · On-site

$140K - $200K/yr

... map cleanly onto the multimodal, embodied, sensor-rich data that Helix and our other AI systems ... Partner with engineering to design and develop new internal tooling for annotation, QA, and data ...

OpenReq

Senior Full Stack Engineer

San Francisco, CA

This tool is crucial for ensuring the accuracy and reliability of our object and line detection features in 3D maps. Key Responsibilities * Lead the development and enhancement of our 3D annotation Q ...

Stack AV

Contract Labeling Associate

Pittsburgh, PA · On-site

Perform high-precision mapping and spatial data annotation activities crucial for autonomous vehicle navigation, utilizing proprietary tooling. * Update issue trackers based on defects found ...

next page

Showing results 1-20

All JobsMap Annotation Jobs

Map Annotation information

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

To thrive as a Map Annotation specialist, you should have excellent attention to detail, spatial reasoning abilities, and familiarity with geographic information systems (GIS) or digital mapping platforms. Proficiency with annotation tools like ArcGIS, QGIS, or proprietary annotation software is often required, and some roles may prefer prior experience or training in cartography or remote sensing. Strong communication, problem-solving skills, and the ability to follow complex guidelines are valuable soft skills in this position. These skills and qualities are vital because accuracy and consistency in annotating map data directly influence the quality of geographic datasets used in applications like navigation, autonomous vehicles, and urban planning.

What does a typical day look like for someone working in Map Annotation?

In a Map Annotation role, your day often involves reviewing and labeling satellite imagery or digital maps using specialized software to identify features like roads, buildings, or natural landmarks. You may work independently or as part of a team, following detailed guidelines to ensure consistency and high accuracy across the dataset. Collaboration with supervisors or quality assurance teams is common to resolve ambiguities and uphold annotation standards. The work is usually computer-based, and you might attend regular meetings to discuss updates to project requirements or annotation protocols. Over time, you can expand your responsibilities by training new teammates or moving into lead annotator or project coordinator roles.

What is a Map Annotation job?

A Map Annotation job involves labeling, tagging, or annotating geographic data to improve the accuracy of digital maps. This can include identifying roads, landmarks, businesses, addresses, and other points of interest. Annotators help train AI and mapping systems by reviewing and verifying location-based information. The role requires attention to detail, spatial awareness, and sometimes familiarity with local geography. These jobs are often remote and may be project-based.

More about Map Annotation jobs
What cities are hiring for Map Annotation jobs? Cities with the most Map Annotation job openings:
What are the most commonly searched types of Map Annotation jobs? The most popular types of Map Annotation jobs are:
What states have the most Map Annotation jobs? States with the most job openings for Map Annotation jobs include:
What job categories do people searching Map Annotation jobs look for? The top searched job categories for Map Annotation jobs are:
Map Annotation jobs near you

Senior Machine Learning Engineer

Troveo AI Inc

San Francisco, CA • On-site

$200K - $400K/yr

Full-time

Posted 12 days ago

Job description

About Troveo
Troveo is building the next-generation data platform to train AI video models. Troveo offers the world's largest library of AI video training data, featuring millions of hours of licensed video content. Our end-to-end data pipeline connects creators, rights holders, and AI research labs, enabling scalable, compliant, and innovative uses of video across for AI application and model development.
We are an early-stage, high-growth venture backed by forward-thinking investors, and we are seeking an innovative strategic engineer to help us scale.
Role Overview
The Senior Machine Learning Engineer will play a central role in designing, building, and optimizing large-scale machine learning pipelines for AI video model training. You'll work across the full ML lifecycle, from structuring massive datasets to deploying, evaluating, and training models in production.
This is a hands-on, high-impact role for an engineer who thrives on scale, autonomy, and cross-functional collaboration. You will combine deep technical expertise with strong communication and business acumen, translating models into measurable costs, performance targets, and real-world outcomes.
Key Responsibilities
Data Curation & Indexing Pipelines
  • Architect and implement large-scale pipelines for video ingestion, metadata extraction, and indexing using vector databases and embedding models to enable fast, semantic retrieval.
  • Design annotation workflows integrating active learning, weak supervision, and human-in-the-loop systems to curate high-quality labeled datasets for video models.
  • Contribute to optimizing data partitioning, sharding, and caching strategies to handle petabyte-scale video corpora, ensuring low-latency search and robust data lineage.

Model Training & Evaluation
  • Develop and fine-tune multimodal models (e.g., CLIP variants, transformer-based encoders) for video embeddings, scene segmentation, and relevance ranking using PyTorch and Hugging Face.
  • Build evaluation frameworks with metrics like NDCG, mAP, and annotation consistency scores to iteratively improve search accuracy and annotation efficiency.
  • Deploy models via containerized services with A/B testing and monitoring for drift detection in production search and annotation pipelines.
  • Collaborate with Product and Operations teams to translate ML performance into business insights and cost implications.

Infrastructure & Optimization
  • Scale ML infrastructure on AWS, leveraging multi-GPU clusters and distributed training to accelerate embedding computation and indexing jobs.
  • Implement testing and deployment processes across large distributed systems. Fine-tune OSS models. Working knowledge in training large models is a plus.
  • Implement automated CI/CD for model versioning, hyperparameter tuning, and resource orchestration to minimize compute costs and maximize GPU utilization.
  • Profile and tune systems for bottlenecks in vector similarity search, batch annotation, and real-time querying.

Cross-Functional Collaboration
  • Partner with product, research, and data teams to align ML outputs with business KPIs, such as search latency targets and annotation throughput.
  • Translate technical trade-offs (e.g., recall vs. precision in embeddings) into actionable insights for stakeholders, fostering adoption in video discovery features.
  • Work closely with data engineers, research scientists, and product teams to align model performance with strategic business goals.
  • Communicate technical concepts clearly to both technical and non-technical stakeholders.
  • Take ownership of project outcomes in a fast-paced, startup environment.

Qualifications & Experience
  • 6+ years in ML engineering, with a focus on information retrieval, embedding systems, or data annotation pipelines.
  • Proven track record building scalable indexing and search infrastructure, including vector stores and similarity search algorithms.
  • Expertise in Python and PyTorch for core model development; hands-on experience with Hugging Face Transformers for multimodal embeddings and fine-tuning.
  • Working experience with video, computer vision, and multi-modal LLMs.
  • Hands-on experience deploying models in production environments and measuring model accuracy.
    Proficiency in ML ops tools (e.g., MLflow, Weights & Biases) for experimentation, versioning, and deployment.
  • Hands-on experience with production ML deployment, evaluation metrics for retrieval/annotation tasks, and cost-optimized scaling on cloud platforms like AWS.
  • Strong analytical skills for dissecting performance in large distributed systems; familiarity with multi-GPU training and vector databases preferred.
  • Excellent communication to bridge technical depth with strategic priorities in collaborative settings.

Nice to Have
  • Prior experience training video models or working with video-based datasets.
  • Demonstrated expertise in GPU optimization and large-scale compute performance tuning.
  • A blend of startup agility and big tech rigor.
  • Contributions to open source development and projects
  • Experience working with search ranking algorithms.

Location & Compensation
  • Location: Strong preference for candidates based in the San Francisco Bay Area.
  • Compensation: $200,000 - $400,000 base salary + equity.

Why Join Troveo?
  • Work at the cutting edge of AI, video, and large-scale data infrastructure.
  • Build systems that directly power the next generation of AI video models.
  • Collaborate with a world-class team of engineers, researchers, and industry experts.
  • High autonomy, high impact, your work will shape the foundation of our platform.
  • Competitive compensation with meaningful equity upside.

Apply