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AI Research Engineer: Vision AI / VLM / Physical AI-1
Summary by AIEngineer.careers
AI Research Engineer: Vision AI / VLM / Physical AI-1 is a AI Research Engineer role (full-time). with Centific. in REMOTE, WORLDWIDE. Compensation shown: $180K–$240K. Imported listing (source: remotesource.com). Apply on the employer's site (remotesource.com).
Imported listing (source: remotesource.com) · Apply on remotesource.com
REMOTE · WORLDWIDE
$180K–$240K
Full-time
SENIOR
The sections below reproduce the third-party job description for reference. AIEngineer.careers does not write or control this text.
Imported job description
Sourced from remotesource.com
Centific is hiring an AI Research Engineer to advance Vision AI across 2D/3D perception, vision-language models, and embodied/physical AI. You will translate cutting-edge research into production systems, owning experiments from paper to deployable module. The role spans computer vision, multimodal reasoning, robotics simulation, and scalable evaluation.
Responsibilities
- Build and fine-tune models for detection, tracking, segmentation, pose recognition, and scene understanding
- Train and evaluate vision-language models for grounding, captioning, temporal QA, and agentic loops
- Prototype perception-in-the-loop policies for physical AI and robotics
- Curate datasets and design high-signal evaluation protocols and KPIs
- Package research into reliable services using Kubernetes, Docker, Ray, and FastAPI
- Orchestrate multi-agent pipelines combining perception, reasoning, simulation, and code generation
Requirements
- Masters or PhD in Computer Science, Electrical Engineering, Robotics, or related field
- Active publishing record in CV/ML/Robotics (e.g., CVPR, ICCV, ECCV, NeurIPS, ICML, ICLR, CoRL, RSS)
- Strong PyTorch or JAX and Python skills, with CUDA profiling and mixed-precision training experience
C
Centific
Redmond · US · 3500+ employees
Centific is an AI-native localization and software-development company that helps enterprises build and scale AI. It combines multilingual and multimodal data, data operations, localization, pre-trained datasets, industry-specific models, and responsible-AI practices for enterprise deployment.
