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Autonomous Driving Multimodal Model Algorithm Engineer

Black Sesame Technologies
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Black Sesame Technologies is building high-performance AI algorithms and self-developed chips for intelligent driving and beyond. As an Autonomous Driving Multimodal Model Algorithm Engineer, you will work on next-generation multimodal AI models for autonomous driving, including Vision-Language Models, Vision-Language-Action Models, and World Models.

You will collaborate with perception, prediction, planning, data, simulation, and deployment teams to integrate multimodal models with existing BEV perception, two-stage E2E, and one-stage E2E autonomous driving systems.

We are looking for candidates with hands-on experience in one or more of the following areas: Vision-Language Models, Vision-Language-Action Models, World Models.

Responsibilities

Multimodal Model Development for Autonomous Driving

  • Work on one or more multimodal modeling directions for autonomous driving, including VLM-based scene understanding, VLA-style planning-oriented modeling, and World Model-based future prediction. 
  • Develop and optimize models that reason over multi-camera images, BEV features, map elements, object/lane instances, occupancy, trajectories, ego-motion, and driving context. 
  • Explore model architectures that connect perception, prediction, planning, and decision-making in two-stage and one-stage E2E autonomous driving systems.  
  • Collaborate with BEV perception and planning teams to improve representation quality, temporal consistency, long-tail robustness, and planning relevance. 

Vision-Language and Vision-Language-Action Modeling

  • Develop VLM-based methods for driving scene understanding, open-vocabulary perception, risk reasoning, corner-case analysis, and interpretable autonomy. 
  • Adapt and extend open-source multimodal architectures such as LLaVA, Qwen-VL, InternVL, MiniCPM-V, OpenVLA, or similar models for autonomous driving scenarios. 
  • Research VLA-style models that map multimodal driving context, navigation intent, and high-level instructions to trajectories, actions, or planning representations. 
  • Align visual, BEV, map, object, lane, occupancy, trajectory, and language representations for driving-specific tasks. 
  • Build supervised fine-tuning, instruction-tuning, and efficient adaptation pipelines for driving-relevant multimodal tasks. 

World Model and Future Prediction

  • Build world-model-based approaches for future BEV, occupancy, object motion, lane evolution, traffic interaction, and ego-conditioned scene rollout. 
  • Explore generative and predictive modeling methods such as diffusion models, autoregressive transformers, latent dynamics models, video prediction, and BEV prediction. 
  • Use learned world models for scenario generation, counterfactual reasoning, long-tail case mining, planning evaluation, and closed-loop analysis. 
  • Work with simulation and data teams to improve safety-critical scenario discovery and model-based evaluation. 

Efficient Adaptation and Deployment

  • Apply efficient fine-tuning and adaptation methods such as LoRA, QLoRA, Adapter, Prompt Tuning, Prefix Tuning, or other PEFT techniques. 
  • Develop multimodal feature alignment modules, including projection heads, query adapters, cross-attention modules, tokenization strategies, and representation converters. 
  • Optimize model architecture, latency, memory footprint, and compute cost for automotive deployment. 
  • Apply distillation, quantization, pruning, sparse computation, and efficient attention methods where appropriate. 
  • Collaborate with chip, compiler, runtime, and deployment teams to adapt multimodal models to in-house automotive AI hardware. 

Research, Evaluation, and Iteration

  • Track the latest research in VLM, VLA, World Models, BEV perception, E2E driving, robotics foundation models, generative simulation, and multimodal learning. 
  • Design evaluation metrics for reasoning quality, grounding accuracy, temporal consistency, prediction quality, planning relevance, and safety-critical scenarios. 
  • Perform systematic failure analysis and drive data/model iteration based on real-world autonomous driving cases. 
  • Contribute to patents, technical reports, internal research platforms, and conference or journal publications. 

Qualifications

  • MS or PhD in Computer Science, Electrical Engineering, Robotics, Artificial Intelligence, or a related field. 
  • Strong background in deep learning, computer vision, multimodal learning, robotics, or autonomous driving. 
  • Hands-on experience in one or more of the following areas: 
    • Vision-Language Models, multimodal large models, or open-source VLM adaptation 
    • Vision-Language-Action models, robotics foundation models, or action-conditioned modeling 
    • World models, generative prediction, latent dynamics modeling, or future scene simulation 
    • BEV perception, multi-view 3D perception, or end-to-end autonomous driving 
    • Motion prediction, planning, trajectory generation, or closed-loop evaluation 
  • Practical experience with open-source multimodal architectures such as LLaVA, Qwen-VL, InternVL, MiniCPM-V, OpenVLA, BLIP-style models, Flamingo-style models, or similar systems. 
  • Solid understanding of multimodal feature alignment, including vision-language alignment, cross-modal attention, visual tokenization, projection layers, query-based fusion, or embedding-space alignment. 
  • Experience with efficient fine-tuning or adaptation methods, such as LoRA, QLoRA, Adapter, Prompt Tuning, Prefix Tuning, supervised fine-tuning, or instruction tuning. 
  • Proficient in PyTorch and capable of modifying, training, debugging, and evaluating deep learning models. 
  • Familiar with transformer architectures, attention mechanisms, temporal modeling, and large-scale training. 
  • Experience with multimodal data, such as camera, radar, LiDAR, IMU, map, trajectory, language, or structured driving data. 
  • Strong engineering ability in Python; C++/CUDA/TensorRT experience is a plus. 
  • Comfortable with Git, Docker, Linux, distributed training, and collaborative development workflows. 
  • Strong communication skills and ability to work across perception, planning, data, simulation, and deployment teams. 

Preferred Qualifications

  • Experience adapting or fine-tuning VLM/VLA models such as LLaVA, Qwen-VL, InternVL, MiniCPM-V, OpenVLA, or similar architectures. 
  • Experience with Hugging Face Transformers, PEFT, DeepSpeed, FSDP, vLLM, SGLang, TensorRT-LLM, or similar training/inference frameworks. 
  • Experience building multimodal instruction datasets, driving-scene QA datasets, grounding datasets, scene-reasoning datasets, or planner-oriented supervision signals. 
  • Experience aligning multimodal model representations with BEV features, object queries, lane instances, occupancy grids, map vectors, trajectories, or planner inputs. 
  • Experience with autonomous driving architectures such as BEVFormer, DETR/DINO, MapTR/MapQR, occupancy networks, diffusion planners, trajectory transformers, or similar models. 
  • Experience with world models, generative models, video prediction, future BEV prediction, occupancy forecasting, learned simulation, or closed-loop evaluation. 
  • Experience with efficient adaptation of large models, including LoRA/QLoRA, distillation, quantization, pruning, sparse attention, or lightweight adapter design. 
  • Experience deploying deep learning models on automotive SoCs, ASICs, GPUs, or edge AI accelerators. 
  • Publications or strong project experience in CVPR, ICCV, ECCV, NeurIPS, ICLR, ICML, CoRL, ICRA, IROS, RSS, or related autonomous driving and robotics venues. 
  • Strong ability to convert research ideas into robust production systems.
  • Experience with AI agent tools and basic harness engineering, including building evaluation scripts, task runners, automated workflows, tool-use pipelines, and reproducible testing environments for model or agent development.