Home › Companies › Hatchit › Image & Computer Vision AI Engineer
Image & Computer Vision AI Engineer
Hatchit · Reston, VA/Washington, DC OR Somerville, MA · Hybrid · Active · $140,000–$170,000 / year · Lever
Job facts
| Field | Value |
|---|---|
| Company | Hatchit |
| Title | Image & Computer Vision AI Engineer |
| Normalized title | - |
| Department / team | Featured Hatch Accounts / Babel Street |
| Location | Reston, VA, United States |
| Work model | Hybrid / Hybrid |
| Employment type | Full Time |
| Salary | $140,000–$170,000 / year |
| Status | active |
| ATS provider | Lever |
| Posted / first seen | 2026-04-24 / 2026-05-29 |
| Changed / last seen | 2026-05-29 / 2026-06-06 |
Related slices
| Page | What it contains | Open |
|---|---|---|
| Company jobs | Active postings from Hatchit. | Open |
| Company breakdowns | Role, location, ATS, and work model facets for this company. | Open |
| ATS provider jobs | Active postings observed through Lever. | Open |
| Provider filtered search | The same provider as a filtered job collection. | Open |
| City jobs | Active postings in Reston. | Open |
| Department jobs | Active postings in Featured Hatch Accounts. | Open |
| Work model jobs | Active Hybrid postings. | Open |
| Lifecycle events | Open, update, close, and reopen events for this posting. | Open |
| Original posting | Canonical source or apply URL captured from the ATS. | Open |
Linked records
| Company | Hatchit |
| Source | e2ea7802-e924-462c-bda6-32def5821788 |
| ATS provider | Lever |
Description
hatch I.T. is partnering with Babel Street to find an Image & Computer Vision AI Engineer . Please see details below:
About the Role
As an Engineer on the Image & Computer Vision AI team , you will play a hands-on role in developing and deploying computer vision capabilities that support Babel Street’s intelligence applications. You will build systems that extract, analyze, and reason over visual data—enabling facial matching, object and scene understanding, geolocation and location inference from imagery, and multimodal intelligence workflows.
This role is execution-focused and suited for engineers with strong foundations in computer vision, image processing, and machine learning who want to apply their skills to real-world, mission-driven problems. You will work closely with AI, Product, and Engineering teams to deliver reliable, scalable, and cost-efficient vision capabilities, including integration with multimodal LLM systems that allow users to search and reason over images using natural language.
This is a hybrid role to be based out of either their Reston, VA/Washington DC office or their Somerville MA office.
About the Company
Babel Street is the trusted technology partner for the world’s most advanced identity intelligence and risk operations. They deliver advanced AI and data analytics solutions providing unmatched, analysis-ready data regardless of language, proactive risk identification, 360-degree insights, high-speed automation, and seamless integration into existing systems. Babel Street empowers government and commercial organizations to transform high-stakes identity and risk operations into a strategic advantage. The actionable insights we deliver safeguard lives and protect critical assets around the world . Babel Street is headquartered in Reston, Virginia, with regional offices in Boston, MA and Cleveland, OH, and international offices in Australia, Canada, Israel, Japan, and the U.K.
Role Focus:
This role spans three practical execution areas:
Computer Vision & Image Analytics
You will implement and operate image analytics pipelines that support facial matching, object detection, scene understanding, and image similarity. This includes image preprocessing, feature extraction, model inference, evaluation, and performance optimization to meet mission-grade accuracy and latency requirements.
Geospatial & Location Inference from Imagery
You will contribute to capabilities that infer location, context, or environmental attributes from imagery—leveraging visual cues, metadata, and learned representations. This includes supporting image-based geolocation, landmark recognition, and contextual scene analysis used in intelligence workflows.
Multi-Modal AI & Image Search
You will support multimodal AI systems that combine vision models with LLMs, embeddings, and retrieval pipelines to enable natural-language search and reasoning over images and image collections. You will help integrate visual understanding into broader intelligence applications and workflows.
What you will do:
Build and maintain computer vision pipelines for image ingestion, preprocessing, inference, and evaluation.
Implement facial matching, and identity-related vision workflows in accordance with accuracy, safety, and compliance requirements.
Develop and support object detection, image similarity, and scene understanding models.
Contribute to image-based geolocation and location inference capabilities using visual features and contextual signals.
Support multimodal AI workflows that combine image embeddings with LLM-based search and reasoning.
Write clean, maintainable Python code and contribute to production services and APIs.
Assist with model evaluation, bias testing, and accuracy monitoring for vision systems.
Optimize inference pipelines for performance, scalability, and cost efficiency (GPU usage, batching, model selection).
Collaborate with Product and Engineering teams to integrate vision capabilities into user-facing intelligence applications.
What you will bring:
Required
3+ years of experience in computer vision, image processing, or applied machine learning.
Hands-on experience with computer vision models and techniques (e.g., CNNs, transformers for vision, feature embeddings).
Experience building or integrating image analytics such as facial recognition, object detection, or image similarity.
Strong programming skills in Python ; experience with common CV/ML libraries (PyTorch, TensorFlow, OpenCV, etc.).
Solid understanding of machine learning fundamentals, model evaluation, and performance tradeoffs.
Experience working with large image datasets and production ML pipelines.
Ability to work collaboratively in a fast-moving, mission-driven engineering environment.
Preferred
Experience with facial matching or biometric systems in regulated or high-stakes environments.
Experience with image-based geolocation or scene/location inference.
Familiarity with multimodal AI systems , including combining vision models with LLMs or natural-language search.
Education:
Bachelor’s degree in Computer Science, Engineering, Data Science, or a related technical field required.
Advanced degree is a plus but not required.
Full job record
| Job ID | 829ceb1dd1305de2ce590fd95b757c91352706eb |
| Org ID | 3883f6fa-759f-4977-ab65-05926fcbad14 |
| Source ID | e2ea7802-e924-462c-bda6-32def5821788 |
| Board ID | e2ea7802-e924-462c-bda6-32def5821788 |
| Provider | lever |
| Provider Job Key | ba014f27-8b15-4652-bd7f-ab40336cd7db |
| Title | Image & Computer Vision AI Engineer |
| Normalized Title | — |
| Status | active |
| Active | yes |
| Location Text | Reston, VA/Washington, DC OR Somerville, MA |
| Department | Featured Hatch Accounts |
| Team | Babel Street |
| Employment Type | Full Time |
| Workplace Type | hybrid |
| Remote Policy | hybrid |
| Country | United States |
| Region | VA |
| City | Reston |
| Salary Raw | USD 140000-170000 per-year-salary |
| Salary Min | 140,000 |
| Salary Max | 170,000 |
| Salary Currency | USD |
| Salary Period | year |
| Source URL | https://jobs.lever.co/hatchit/ba014f27-8b15-4652-bd7f-ab40336cd7db |
| Apply URL | https://jobs.lever.co/hatchit/ba014f27-8b15-4652-bd7f-ab40336cd7db/apply |
| First Seen At | 2026-05-29 07:02:02Z |
| Last Seen At | 2026-06-06 07:57:50Z |
| Last Checked At | 2026-06-06 07:57:50Z |
| Last Changed At | 2026-05-29 07:02:02Z |
| Inactive At | — |
| Source Posted At | 2026-04-24 16:31:11Z |
| Source Updated At | — |
| Raw Payload Uri | s3://job-postings-prod-raw-590183727216/raw/provider=lever/board=hatchit/date=2026-06-06/2026-06-06T07-57-50-001Z-c79a78cff4aa80e280e0b0bda57519c1c16bd3312d1cd5708cb7513eeb399043.json |
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