Sr Research Engineer, Computer Vision

Job Requisition ID #26WD96331 Senior Software Engineer, Computer Vision & Multimodal AI (Applied Research) Location Flexible / Hybrid / Remote (team-dependent)

About The Role We are hiring a Senior Software Engineer focused on Computer Vision and Multimodal AI to build robust perception and understanding systems used across multiple teams and product areas. You will develop end-to-end pipelines that transform images and video into structured, reliable observations by combining modern vision models with multimodal reasoning and contextual signals (for example: domain metadata, documents, and sensor inputs).

This role blends applied research with strong software engineering: rapid iteration, rigorous evaluation, and production‑minded implementation for cloud‑scale batch processing and interactive workflows.

Key Responsibilities

Design, build, and improve multi‑stage computer vision pipelines that may include segmentation, detection, tracking, and VLM‑based analysis, producing structured outputs (entities, attributes, actions/events, confidence, provenance).

Build systems that handle real‑world variability in visual inputs (for example: low resolution, poor lighting, motion blur, cluttered scenes, inconsistent capture devices).

Work with diverse media types such as photos, video, timelapse, 360 video, and RGB‑D when available.

Fuse visual evidence with contextual inputs such as metadata, documents, and sensor streams to improve recognition quality and reduce ambiguity.

Evaluate and integrate state‑of‑the‑art vision and vision‑language foundation models, including open‑vocabulary recognition, grounded perception, segmentation, and multimodal reasoning.

Apply fine‑tuning or adaptation approaches when needed; partner with ML teams on training, data strategy, and infrastructure best practices.

Define measurable acceptance criteria and benchmarking for accuracy, robustness, latency/cost, and reliability across datasets and domains.

Build scalable cloud workflows for batch processing and integrate outputs with APIs and downstream consumers.

Improve operational performance and cost via batching, caching, model selection, and pipeline observability.

Write maintainable code, contribute to design docs, code reviews, shared libraries, and cross‑team technical decisions.

Minimum Qualifications

Bachelor’s degree in Computer Science, Electrical Engineering, Robotics, or related field (or equivalent practical experience).

4+ years of experience building computer vision systems using Python.

Strong experience with deep learning for computer vision (detection, segmentation, and/or video understanding) using modern frameworks such as PyTorch.

Experience taking ML prototypes into reliable pipelines, including evaluation, monitoring, and failure analysis.

Experience building or integrating ML systems into cloud or backend workflows (batch processing and/or services).

Strong collaboration and communication skills; ability to work across teams and stakeholders.

Preferred Qualifications

Experience with vision‑language models (VLMs) and multimodal systems (for example: grounded vision, open‑vocabulary recognition, retrieval‑augmented multimodal reasoning).

Experience with multimodal fusion (combining imagery/video with metadata, documents, and sensor signals).

Experience with video pipelines (tracking, temporal aggregation, long‑video processing).

Experience with real‑world datasets, including data curation, labeling strategy, augmentation, and quality control under limited data constraints.

Experience developing reusable platform components adopted across multiple teams.

What Success Looks Like

Delivered an end‑to‑end system that ingests real‑world image/video inputs and outputs a structured, queryable set of observations (objects plus activities/events), with clear accuracy and reliability metrics.

Demonstrated robustness to common visual failure modes (lighting, occlusion, clutter, camera variation) and measurable improvements when contextual signals are available.

Built a modular pipeline architecture (segmentation/detection/VLM reasoning components) that can be reused and extended across domains and teams.

Maintained strong engineering quality: reproducible experiments, documented decisions, maintainable code, and dependable integrations.

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