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Map Annotation Jobs in California (NOW HIRING)

Planet

Visiting Staff Scientist

San Francisco, CA · On-site +1

Use embeddings to design active learning workflows that prioritize labeling and reduce the annotation burden for time-sensitive mapping tasks. * Academic & Technical Leadership: Publish findings in ...

Planet

Visiting Staff Scientist

San Francisco, CA · On-site

Use embeddings to design active learning workflows that prioritize labeling and reduce the annotation burden for time-sensitive mapping tasks. * Academic & Technical Leadership: Publish findings in ...

Meter, Inc

Software Engineer, Models

San Francisco, CA · On-site

Map the existing landscape: telemetry in ClickHouse, configs in Postgres, support history in Salesforce. 60 days in Ship a working v1 of the annotation interface. Network engineers should be able to ...

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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.

What are the most commonly searched types of Map Annotation jobs in California? The most popular types of Map Annotation jobs in California are:
What are popular job titles related to Map Annotation jobs in California? For Map Annotation jobs in California, the most frequently searched job titles are:
What job categories do people searching Map Annotation jobs in California look for? The top searched job categories for Map Annotation jobs in California are:
What cities in California are hiring for Map Annotation jobs? Cities in California with the most Map Annotation job openings:
Map Annotation jobs near you
Senior Simulation Engineer

Senior Simulation Engineer

EPAM Systems

Mountain View, CA

Other

Posted 3 days ago

Job description

We are seeking a highly skilled Senior Simulation Engineer to own and accelerate our synthetic data generation capabilities. This role is crucial for bridging the gap between our high-fidelity simulation environment and our production ML models. You will be responsible for architecting, implementing, and maintaining the entire simulation-to-data pipeline, ensuring a consistent and massive flow of quality training data.

This will be an on-site position, working alongside our client in Mountain View, CA. Req#: 906141765 Responsibilities Simulation Acceleration & Data Collection: Directly utilize expertise in NVIDIA Isaac Sim to design, script, and optimize simulation scenarios specifically tailored to generate high-quality, diverse data for VLA (Vision-Language Alignment) and NOVA model training objectives Pipeline Architecture: Design, build, and maintain robust, scalable pipelines for CAD ingestion, scene creation, sensor emulation, and data processing within the Isaac Sim framework Synthetic Data Management: Implement and manage advanced auto-annotation tools within the simulation environment to rapidly label complex sensory data (e.g., 3D bounding boxes, semantic segmentation, depth maps) for supervised learning Environment Maintenance: Take ownership of setting up, configuring, and maintaining the simulation environment (including hardware/software dependencies, GPU utilization, and headless operation) to ensure reliable, large-scale parallel execution Cross-Functional Support: Collaborate closely with the Machine Learning Engineering team to analyze data gaps, iterate on simulation parameters, and ensure the synthetic data distribution matches the necessary complexity and variability of real-world deployment Performance Tuning: Profile and optimize simulation execution speed to maximize data throughput, essential for rapid iteration cycles in model tuning Requirements Expertise in Simulation: 3+ years of hands-on experience with NVIDIA Isaac Sim (or a comparable high-fidelity physics simulator like Unity/Unreal used for robotics) Programming Proficiency: Expert-level proficiency in Python for scripting, automation, data pipeline construction, and tool development Robotics Fundamentals: Strong understanding of robotics kinematics, sensor physics (LIDAR, RGB-D cameras, IMU), and their accurate representation in simulation ML Data Pipeline Experience: Proven experience setting up automated data collection pipelines for Deep Learning projects, including experience with data versioning and metadata management CAD/3D Workflow: Familiarity with 3D assets, CAD formats, and procedural generation techniques to create complex virtual scenes Familiarity with VLA/NOVA model architectures or similar foundation models in robotics Experience with cloud computing environments (AWS, Azure, Google Cloud Platform) for scaling simulation jobs Familiarity with other robotics simulation/middleware like ROS/ROS 2 Experience with hardware-in-the-loop (HIL) or software-in-the-loop (SITL) testing methodologies

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