Bachelor’s degree in Computer Science, Software Engineering, Computer Engineering, Systems Engineering, Aerospace Engineering, Electrical Engineering, or a related technical field
Develop and curate multimodal datasets for pretraining Hark's AI models, focusing on text, code, image, and video modalities, and design training mixtures and experiments to improve model performance.
Compensation
The US base salary range for this full-time position is between $180,000 - $450,000 annually.
Responsibilities
Develop methods to select, filter, deduplicate, and prepare text, code, image, and video datasets at scale.
Build paired and interleaved datasets, including image-text documents and video-text data.
Build quality classifiers and sampling methods, and measure their impact through controlled training experiments.
Develop synthetic captioning and other supervision methods for image and video data, and assess their accuracy, coverage, and biases.
Design mixtures and curricula across sources, domains, and modalities.
Design and develop state-of-the-art algorithms for large-scale 3D geospatial data, applying AI and machine learning techniques to the ArcGIS platform.
Responsibilities
Design and develop state-of-the-art algorithms for large-scale 3D geospatial data, including point clouds, meshes, Gaussian splats, and 3D scene representations.
Apply and adapt modern AI and machine learning techniques, including large language models (LLMs), to improve semantic understanding, feature extraction, automation, and data quality.
Build intelligent workflows that combine 3D analytics, geospatial reasoning, and AI-driven agents to accelerate data processing and insight generation.
Develop scalable processing pipelines capable of handling massive geospatial datasets in distributed and cloud-based environments.
Collaborate with researchers, engineers, and product teams to transform innovative ideas into robust, production-ready capabilities.
Requirements
Bachelor’s in computer vision, geometry processing, or AI on 3D data
Nice to have: Master’s or PhD in computer vision, geometry processing, or AI on 3D data
Designing, developing, and maintaining software that interfaces with correlator hardware and the computing systems around it for the Correlator team within the Monitor & Control (M&C) group.
Locations (4)
Socorro, New Mexico, United States
Albuquerque, New Mexico, United States
Charlottesville, Virginia, United States
Green Bank, West Virginia, United States
Compensation
The starting salary/hourly rate of this position is between $79,000-$121,000.
Responsibilities
Developing and maintaining software that configures, controls, and monitors correlator hardware
Design and build scalable batch and real‑time data platforms, own architecture of ingestion, orchestration, monitoring, and governance, and collaborate with clients and cross‑functional teams to deliver enterprise‑grade data solutions.
Responsibilities
Design and build scalable batch and real‑time data platforms
Own the architecture of ingestion patterns, orchestration, lineage, monitoring, security, governance, and resilience
Develop enterprise‑grade ETL and ELT pipelines across cloud and hybrid landscapes
Integrate enterprise systems such as ERPs, CRMs, identity platforms, APIs, streaming systems, and legacy applications
Build data products that support analytics, machine learning, GenAI, RAG, and agentic AI applications
Architect, build, and scale a global data ecosystem for AI-based ground truth generation for autonomous driving and robotics.
Responsibilities
Develop and productionize AI-powered auto-labeling workflows using computer vision models, vision-language models, and AI agents, integrating human review where needed.
Design and implement MLOps capabilities for reproducible training and inference, model and dataset versioning, evaluation, deployment, and lifecycle management.
Build observability and data quality frameworks covering pipeline health, job execution, labeling quality, data lineage, and GT release readiness.
Optimize distributed processing and GPU inference workloads to improve throughput, resource utilization, cost efficiency, and release turnaround time.
Drive technical decisions and cross-functional execution with perception, autolabeling modeling, data operations, and partner-facing teams to translate requirements into production solutions.
Requirements
BS, MS, or PhD in Computer Science, Software Engineering, Data Engineering, or a related field, or equivalent practical experience.
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