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About O-HIVE O-HIVE develops Visual Language Model (VLM) technology that enables machines and robots to see, understand, reason, and execute in real-world environments. Our technology combines multimodal AI, computer vision, spatial intelligence, robotics, and edge computing to build intelligent systems for manufacturing, robotics, visual inspection, construction, and other industrial applications. We are looking for an AI Language Model Engineer to develop, optimize, and deploy language and multimodal models that power O-HIVEs VLM platform and robotics products. Role Overview As an AI Language Model Engineer, you will work on the development and optimization of Large Language Models (LLMs), Vision-Language Models (VLMs), and multimodal reasoning systems. You will work closely with our computer vision, robotics, embedded, and semiconductor teams to develop models capable of understanding visual information, reasoning about physical environments, generating structured outputs, and controlling downstream robotic or software systems. The ideal candidate has strong experience with Transformer-based models, PyTorch, model fine-tuning, inference optimization, and modern multimodal AI architectures. Key Responsibilities - Develop and improve O-HIVE's LLM and VLM architectures for industrial and robotic applications. - Fine-tune and adapt open-source language and multimodal models for O-HIVE-specific use cases. - Develop multimodal pipelines combining: - Images and video - Natural language - Spatial and 3D information - Sensor data - Robot state and control information - Design prompting, instruction-tuning, and structured-output methods for reliable machine reasoning. - Develop training and fine-tuning pipelines using techniques such as: - Supervised Fine-Tuning (SFT) - LoRA / QLoRA - Parameter-Efficient Fine-Tuning (PEFT) - Distillation - Quantization - Preference optimization - Improve model reasoning, grounding, object understanding, spatial reasoning, and task planning. - Optimize models for low-latency edge inference on GPUs, embedded AI devices, and future O-HIVE custom AI hardware. - Evaluate model accuracy, latency, memory consumption, hallucination rate, and task reliability. - Develop datasets and evaluation benchmarks for visual reasoning and industrial AI applications. - Integrate language models with computer vision, detection, segmentation, depth estimation, 3D mapping, and robotic-control pipelines. - Build APIs and inference services for O-HIVE's VLM Cloud and Edge AI products. - Research emerging LLM/VLM architectures and translate relevant research into production systems. - Collaborate with hardware and ASIC engineers to define model requirements for future O-HIVE VLM accelerator chips. Required Qualifications - Bachelor's, Master's, or Ph.D. degree in Computer Science, Artificial Intelligence, Machine Learning, Electrical Engineering, or a related field. - Strong programming skills in Python. - Solid experience with PyTorch and modern deep-learning frameworks. - Hands-on experience with Transformer-based models. - Experience working with LLMs such as: - Qwen - Llama - Gemma - Mistral - or similar open-source architectures - Experience with Hugging Face Transformers and related ML tooling. - Experience with model fine-tuning and inference. - Understanding of: - Attention mechanisms - Tokenization - Transformer architectures - Embeddings - KV cache - Quantization - Model compression - Ability to read and implement recent machine-learning research papers. - Strong analytical and problem-solving skills. Preferred Qualifications Experience in one or more of the following areas is highly desirable: - Vision-Language Models such as Qwen-VL, LLaVA, InternVL, Florence, or similar multimodal architectures. - Multimodal model training and fine-tuning. - Visual grounding and spatial reasoning. - Robotics or embodied AI. - 3D computer vision. - Image/video understanding. - Reinforcement learning or preference optimization. - CUDA, TensorRT, ONNX, vLLM, or other inference-optimization frameworks. - FP8 / INT8 / INT4 model optimization. - NVIDIA Jetson or other edge-AI platforms. - Distributed training using DeepSpeed, FSDP, or similar frameworks. - Synthetic-data generation and automated dataset creation. - RAG, vector databases, and agentic AI architectures. - Deployment of production AI APIs and inference infrastructure. - Model-hardware co-design or AI accelerator architecture. Example Projects You may work on projects including: - Developing O-HIVE's proprietary Visual Language Model for robotics. - Enabling robots to interpret camera input and generate actionable decisions. - Creating visual inspection models for manufacturing and industrial environments. - Building multimodal reasoning systems combining RGB, depth, 3D, and natural-language inputs. - Optimizing multimodal models for real-time inference on edge hardware. - Developing task-planning and reasoning systems that translate .
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.