新加坡商星智科技股份有限公司

Job Brief: We are looking for a talented Embedded Software Engineer with expertise in embedded CPU, RTOS and IP verification to join our innovative team. In this role, you will be responsible for designing, developing, and maintaining software for embedded systems, focusing on RISC-V architecture and RTOS, as well as verifying IP on simulators and FPGA platforms. Responsibilities: Design and implement software for embedded devices and systems using embedded CPU architecture and RTOS. Develop, code, test, and debug system software. Review code and design to ensure adherence to best practices and performance standards. Analyze and enhance the efficiency, stability, and scalability of system resources. Integrate and validate the next-generation AI accelerator designs. Perform IP verification using simulators and FPGA platforms. Support software applications and optimize I/O performance. Interface with hardware design and application development teams. Provide post-production support, including debugging and upgrading software. Document design specifications, installation instructions, and other system-related information. Requirements: - Proven working experience in embedded software engineering. - BS degree in Computer Science, Electrical Engineering, or a related field. - Experience in hands-on development and troubleshooting on embedded targets. - Solid programming experience in C or C++. - Non smoking

a. Job Description: We are looking for a highly motivated RTL Designer to join our team in developing high-performance digital IPs. The ideal candidate will have experience in Register Transfer Level (RTL) design and verification, with a strong understanding of digital logic, microarchitecture, and ASIC/FPGA development processes. The role involves designing and verifying custom hardware IPs for cutting-edge applications. b. Verification: Develop and execute test plans to verify functionality, performance, and power requirements. Create testbenches using SystemVerilog/UVM for functional verification. Perform simulation, debugging, and root cause analysis for design issues. Conduct code coverage and functional coverage analysis to ensure comprehensive testing. Collaborate with verification and firmware teams to validate IP functionality. c. Qualifications: Bachelor’s/Master’s degree in Electrical Engineering, Computer Engineering, or a related field. 2+ years of experience in RTL design and verification. Proficiency in Verilog, SystemVerilog Strong understanding of digital design concepts, including pipelining, clock domains, and low-power design techniques. Experience with simulation tools (e.g., ModelSim, VCS, Questa) and formal verification techniques. Familiarity with UVM methodology and testbench development. Knowledge of scripting (Python, TCL, Perl, Shell) for automation. Experience with FPGA or ASIC development flows, including synthesis and timing analysis. Strong debugging and problem-solving skills. Excellent communication and teamwork abilities. - Non smoking

a. Job Description: We are seeking a Software Engineer to develop and optimize GPU drivers for high-performance computing applications. The ideal candidate will have expertise in C++ and Python, with experience in low-level programming, system software development, and GPU architectures. This role involves designing, implementing, and debugging PC drivers for GPUs, ensuring optimal performance, stability, and compatibility across platforms. b. GPU Driver Development: Design and develop low-level GPU drivers for Windows and Linux environments. Implement driver components for memory management, scheduling, synchronization, and command submission. Optimize driver performance for latency, throughput, and power efficiency. Ensure compatibility with DirectX, Vulkan, OpenGL, CUDA, and other graphics/compute APIs. Work on device initialization, firmware interaction, and kernel mode development. c. Qualifications: Bachelor’s/Master’s degree in Computer Science, Electrical Engineering, or related field. 3+ years of experience in system software, GPU drivers, or low-level programming. Strong C++ development skills with knowledge of multi-threading, concurrency, and memory management. Experience with Python for automation and testing. Familiarity with operating system internals (Windows/Linux Kernel, I/O subsystems, and memory management). Understanding of GPU architectures, scheduling, and compute pipelines. Experience with profiling and debugging tools for GPU/CPU performance tuning. - Non smoking

a. Job Description: We are looking for an AI Software Engineer with expertise in performance profiling, model fine-tuning, and architecture identification for large-scale AI models such as Llama, DeepSeek, VLM, Evo-2, LLMs, Robot VLM, and DNA-based models. The ideal candidate has experience with deep learning frameworks, hardware acceleration, and AI optimization techniques to enhance the efficiency and scalability of AI models. b. Performance Profiling & Optimization Analyze and optimize AI model performance across various hardware platforms (GPUs, TPUs, NPUs). Profile training and inference pipelines for memory usage, compute efficiency, and latency. Work with CUDA, TensorRT, PyTorch, and JAX to optimize models for production deployment. Implement quantization, pruning, distillation, and mixed-precision training techniques. Debug performance bottlenecks using NVProf, Nsight, TensorBoard, and PyTorch Profiler. 2. Architecture Identification & Model Analysis Reverse-engineer and analyze LLM/VLM architectures to extract key architectural details. Identify activation functions, attention mechanisms, and parameter distributions in pretrained models. Compare performance trade-offs between transformer-based, mixture-of-experts (MoE), and diffusion models. Work on model compression techniques for real-time AI applications. 3. Fine-Tuning & Customization Fine-tune LLMs, VLMs, and DNA-based AI models on domain-specific datasets. Use LoRA, QLoRA, PEFT, and Adapter methods for parameter-efficient fine-tuning. Implement prompt engineering, retrieval-augmented generation (RAG), and reinforcement learning (RLHF) for LLMs. Train and deploy robotic VLMs for real-world AI applications. c. Qualifications: Bachelor’s/Master’s/PhD in Computer Science, Machine Learning, AI, or related fields. 3+ years of experience in deep learning, model optimization, and AI performance engineering. Strong proficiency in Python, PyTorch, TensorFlow, and JAX. Experience with CUDA, Triton, TensorRT, and ONNX for AI acceleration. Understanding of transformer architectures, vision-language models (VLMs), and multi-modal AI. Familiarity with LLM fine-tuning techniques, large-scale distributed training, and RLHF. Experience working with scientific AI applications (e.g., DNA sequencing, robotics, computational biology). - Non smoking

* Job Description: We are seeking a highly skilled AI Application Engineer to lead the porting and optimization of large AI models on GPU platforms and enable integration into next-generation applications. This role will focus on deploying and adapting LLMs, VLMs, and domain-specific AI models (e.g., robotics, genomics) into real-world solutions such as multi-modal copilots (MCPs), AI copilots, and Digital Avatar. --- * Responsibilities: 1. AI Model Porting & Optimization: - Port and optimize LLMs and VLMs (e.g., Llama, DeepSeek, Evo-2, Robot VLM, DNA-based models) to run efficiently on GPU-based inference/training platforms. - Leverage CUDA, TensorRT, PyTorch, and ONNX to fine-tune models for performance, memory, and power efficiency. - Implement quantization, pruning, and mixed-precision techniques to deploy large models in production environments. 2. Application Enablement: - Enable and integrate AI models into advanced applications such as: - MCP (Multi-modal Copilot Platforms) - AI Copilot Assistants for productivity, decision support, and workflow automation - Digital Avatar for interactive AI-driven virtual agents, avatars, and assistants - Collaborate with product and UI/UX teams to ensure models serve real-time, low-latency, and responsive experiences. 3. Performance Profiling & Deployment: - Conduct profiling and benchmarking of AI models across various hardware (NVIDIA GPUs, cloud, edge). - Optimize compute and memory usage based on deployment targets (e.g., data center, embedded systems). - Automate deployment pipelines using Docker, Kubernetes, or ML deployment frameworks. --- * Qualifications: - Bachelor’s or Master’s in **Computer Science, AI, Electrical Engineering**, or related fields. - Solid experience with **deep learning frameworks** (e.g., PyTorch, TensorFlow) and GPU programming. - Hands-on experience porting or deploying **large-scale AI models** on **NVIDIA GPU** infrastructure. - Familiarity with **model architectures** such as transformers, vision-language fusion, and sequence models. - Experience with **AI application domains**, including conversational AI, virtual assistants, or computational biology, is a strong plus. - Non smoking --- * Preferred Skills: - Experience with **multi-modal models** (text, image, speech, DNA/protein sequences). - Knowledge of **AI agent platforms**, avatar systems, or **digital human interaction engines**. - Understanding of **data pipelines**, inference optimization, and **AI-human interface design**.

Role Summary *** this position only open for experienced engineer with DNA AI Analysis*** We are seeking an AI researcher with a deep technical background in model architecture design and a passion for open-source genomics. This role focuses on building and maintaining state-of-the-art machine learning models specifically designed for DNA sequence understanding, including language models, encoders, and predictive sequence transformers. Your work will shape the future of programmable biology, genome interpretation, and foundational models for genomics. Key Responsibilities * Design, fineture, and benchmark deep learning architectures (e.g., CNNs, Transformers, LSTMs, diffusion models) for DNA sequence tasks such as promoter prediction, enhancer classification, mutation impact scoring, and epigenomic signal inference. * Lead the development of open-source genomic AI models (e.g., BERT-style for DNA, Enformer-like architectures, foundation models for genomics). * Optimize models for long sequences (10k–100k bp) using memory-efficient attention, sparse encoding, and segment-based learning. * Contribute to and maintain public GitHub repositories of models, datasets, and benchmarks for the genomics AI community. * Integrate experimental annotations (e.g., ATAC-seq, ChIP-seq) as conditioning signals in multi-modal architectures. * Collaborate with academic and industry partners on pretraining strategies over large-scale human and non-human genomes. * Drive internal model interpretability efforts using saliency, attention attribution, and motif visualization tools. Required Qualifications * experience in Machine Learning, Bioinformatics, Computational Biology, or related field. * Demonstrated expertise in deep learning architectures, especially sequence modeling (e.g., BERT, GPT, Performer, Perceiver IO, Hyena, RWKV). * Strong proficiency in PyTorch or JAX/Flax and hands-on experience building custom model layers. * Experience with DNA/RNA sequence data, k-mer tokenization, reverse complement invariance, and genome data structures (e.g., FASTA, BED). * Strong open-source track record (e.g., GitHub projects, community tools, published preprints/code). Preferred Skills * Contributions to tools like DNABERT, Enformer, AlphaFold, Genome Transformer, or related models. * Experience training large models on genome-scale data (e.g., full reference genomes, species pangenomes). * Familiarity with distributed training, mixed precision, and hardware optimization * Knowledge of bioethical concerns and data governance in genomic datasets.

AI Researcher – Quantum Computing Algorithms Key Responsibilities *** this position only open for experienced engineer with Quantum Computing Algorithms*** * Research and prototype quantum-native machine learning models, such as Quantum Neural Networks (QNNs), variational quantum circuits, and quantum-enhanced transformers. * Explore hybrid classical-quantum systems: e.g., using quantum layers in PyTorch or JAX pipelines. * Collaborate on quantum kernel methods, quantum Boltzmann machines, and generative models for data with quantum structure. * Contribute to open-source libraries (e.g., Qiskit, PennyLane, Cirq, TensorFlow Quantum) with new model blocks or benchmarks. * Simulate algorithms on quantum emulators and test performance on real quantum hardware (IBM Q, Rigetti, IonQ, etc.). * Stay ahead of quantum hardware roadmaps and model scalability trends. Required Qualifications * PhD in Physics, Computer Science, Quantum Information, or related field. * Hands-on experience with quantum programming (e.g., Qiskit, PennyLane, Cirq, Braket). * Strong background in machine learning or deep learning, including experience with PyTorch/JAX/TF. * Understanding of quantum circuit design, variational algorithms (VQE, QAOA), and entanglement-based computing. * Experience simulating quantum systems and implementing noise-aware training. Preferred Skills * Experience building hybrid quantum-classical deep learning architectures. * Familiarity with quantum-inspired classical models (e.g., tensor networks, low-rank factorization). * Contributions to open-source quantum ML frameworks or community benchmarks. * Understanding of hardware-specific constraints (connectivity, coherence time, gate depth).