- Outram Central Region (Singapore) Singapore
Kerja Sepenuh Masa Physical AI (Edge Intelligence) Research Scientist, Gaji tinggi SGD 11,000 di STARRY RECRUITMENT PTE. LTD. Central Region (Singapore) - Maukerja
Physical AI (Edge Intelligence) Research Scientist
STARRY RECRUITMENT PTE. LTD.
Kongsi
Simpan
Lokasi Kerja
Penerangan Kerja
Tanggungjawab
About the Role
We are seeking a
Physical AI (Edge Intelligence) Research Scientist
to advance research at the intersection of
Physical AI, Embodied AI, Multimodal AI, Foundation Models, Robotics, and Edge Intelligence
.
The role focuses on developing AI models that can perceive, reason, and generate actions in real-world physical environments, with an emphasis on
efficient, robust, and low-latency AI inference on edge systems
.
You will bridge the gap between
AI research and real-world deployment
, working closely with AI researchers and hardware acceleration engineers to develop novel algorithms, optimise AI models, and validate them on physical edge platforms and testbeds.
Key Responsibilities
Conduct research in
Physical AI, Embodied AI, Multimodal AI, and Foundation Models
for real-world applications.
Develop and fine-tune
Vision-Language Models (VLMs), Vision-Language-Action (VLA) models, multimodal models, and action-generation policies
.
Develop AI algorithms for perception, reasoning, decision-making, and action generation using multimodal sensory inputs.
Optimise AI models for
efficient and low-latency edge inference
, including model compression, quantisation, pruning, knowledge distillation, and computational graph optimisation.
Deploy and benchmark AI models on
edge devices, embedded platforms, robots, and physical testbeds
.
Integrate vision feeds and multimodal sensor inputs into real-time AI inference pipelines.
Utilise simulation environments for
synthetic data generation, pre-training, domain adaptation, and sim-to-real evaluation
.
Collaborate with hardware and systems engineers to optimise AI workloads for target edge platforms.
Develop and optimise AI inference pipelines using
PyTorch, ONNX, TensorRT
, and related technologies.
Conduct experiments, benchmark model performance, analyse results, and translate research concepts into working prototypes.
Publish research findings in leading AI/ML, computer vision, robotics, and related conferences or journals.
Identify opportunities for
technical patents and intellectual property
.
Qualifications
Ph.D. in
Computer Science, Artificial Intelligence, Computer Vision, Robotics, Electrical Engineering
, or a related quantitative discipline. Candidates with a research-oriented Master's degree and strong publications may also be considered.
Strong research background in one or more of the following areas:
Physical AI / Embodied AI
Robotics / Robot Learning
Multimodal AI
Vision-Language Models (VLMs)
Vision-Language-Action (VLA) Models
Foundation Models
Computer Vision
Edge AI
Strong understanding of
Deep Learning, Transformers, multimodal models, or generative AI
.
Strong publication record in top-tier AI/ML, computer vision, robotics, or related conferences and journals.
Strong proficiency in
Python and PyTorch
.
Familiarity with model deployment and optimisation frameworks such as
ONNX, TensorRT, CUDA
, or similar technologies.
Experience with
model compression, quantisation, pruning, knowledge distillation, or inference optimisation
is highly desirable.
Experience deploying AI models on
edge devices, embedded systems, robots, or physical testbeds
is highly desirable.
Working knowledge of
C/C++
is preferred.
Strong research, experimentation, analytical, and scientific communication skills.
Preferred Technical Experience
Experience in any of the following areas would be highly valued:
Physical AI | Embodied AI | VLA | VLM | Multimodal AI | Robotics | Robot Learning | Computer Vision | Edge AI | AI Inference | Model Optimisation | Quantisation | TensorRT | ONNX | Sim-to-Real | Sensor Fusion | Real-Time AI
Research Focus
The role covers the following research pipeline:
Foundation Models → Multimodal Perception → Reasoning & Decision Making → Action Generation → Model Optimisation → Edge Deployment → Physical Testbed Validation
The position is primarily focused on
AI research, algorithm development, model optimisation, and edge deployment
, rather than traditional telecommunications or semiconductor IC design.
With a strong focus on supporting startups in Singapore, we’re committed to driving your global success through exceptional leadership hires.Industries that I am specialised in:
*************
Licence No. : 25S2734Registration ID: R22107458
We are seeking a
Physical AI (Edge Intelligence) Research Scientist
to advance research at the intersection of
Physical AI, Embodied AI, Multimodal AI, Foundation Models, Robotics, and Edge Intelligence
.
The role focuses on developing AI models that can perceive, reason, and generate actions in real-world physical environments, with an emphasis on
efficient, robust, and low-latency AI inference on edge systems
.
You will bridge the gap between
AI research and real-world deployment
, working closely with AI researchers and hardware acceleration engineers to develop novel algorithms, optimise AI models, and validate them on physical edge platforms and testbeds.
Key Responsibilities
Conduct research in
Physical AI, Embodied AI, Multimodal AI, and Foundation Models
for real-world applications.
Develop and fine-tune
Vision-Language Models (VLMs), Vision-Language-Action (VLA) models, multimodal models, and action-generation policies
.
Develop AI algorithms for perception, reasoning, decision-making, and action generation using multimodal sensory inputs.
Optimise AI models for
efficient and low-latency edge inference
, including model compression, quantisation, pruning, knowledge distillation, and computational graph optimisation.
Deploy and benchmark AI models on
edge devices, embedded platforms, robots, and physical testbeds
.
Integrate vision feeds and multimodal sensor inputs into real-time AI inference pipelines.
Utilise simulation environments for
synthetic data generation, pre-training, domain adaptation, and sim-to-real evaluation
.
Collaborate with hardware and systems engineers to optimise AI workloads for target edge platforms.
Develop and optimise AI inference pipelines using
PyTorch, ONNX, TensorRT
, and related technologies.
Conduct experiments, benchmark model performance, analyse results, and translate research concepts into working prototypes.
Publish research findings in leading AI/ML, computer vision, robotics, and related conferences or journals.
Identify opportunities for
technical patents and intellectual property
.
Qualifications
Ph.D. in
Computer Science, Artificial Intelligence, Computer Vision, Robotics, Electrical Engineering
, or a related quantitative discipline. Candidates with a research-oriented Master's degree and strong publications may also be considered.
Strong research background in one or more of the following areas:
Physical AI / Embodied AI
Robotics / Robot Learning
Multimodal AI
Vision-Language Models (VLMs)
Vision-Language-Action (VLA) Models
Foundation Models
Computer Vision
Edge AI
Strong understanding of
Deep Learning, Transformers, multimodal models, or generative AI
.
Strong publication record in top-tier AI/ML, computer vision, robotics, or related conferences and journals.
Strong proficiency in
Python and PyTorch
.
Familiarity with model deployment and optimisation frameworks such as
ONNX, TensorRT, CUDA
, or similar technologies.
Experience with
model compression, quantisation, pruning, knowledge distillation, or inference optimisation
is highly desirable.
Experience deploying AI models on
edge devices, embedded systems, robots, or physical testbeds
is highly desirable.
Working knowledge of
C/C++
is preferred.
Strong research, experimentation, analytical, and scientific communication skills.
Preferred Technical Experience
Experience in any of the following areas would be highly valued:
Physical AI | Embodied AI | VLA | VLM | Multimodal AI | Robotics | Robot Learning | Computer Vision | Edge AI | AI Inference | Model Optimisation | Quantisation | TensorRT | ONNX | Sim-to-Real | Sensor Fusion | Real-Time AI
Research Focus
The role covers the following research pipeline:
Foundation Models → Multimodal Perception → Reasoning & Decision Making → Action Generation → Model Optimisation → Edge Deployment → Physical Testbed Validation
The position is primarily focused on
AI research, algorithm development, model optimisation, and edge deployment
, rather than traditional telecommunications or semiconductor IC design.
With a strong focus on supporting startups in Singapore, we’re committed to driving your global success through exceptional leadership hires.Industries that I am specialised in:
- Artificial Intelligent
- Semicondutor
- B2B: Cloud, SaaS
- CTO, VP | Director of Engineering
- Legal | Financial Director
- HR Director
- Sales Director
- Software Engineer (Backend, Frontend, Fullstack, QA, Mobile)
- DevOps | Site Reliability Engineer
- Machine Learning | Algorithm Engineer
- AI Researcher | Scientist
- Product Manager
- Solution Architect
*************
Licence No. : 25S2734Registration ID: R22107458
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