Top Deep Learning Jobs in Boston, MA

Lead the integration of advanced imaging analytics and AI into drug development, focusing on medical image analysis using deep learning. Collaborate with clinical teams and develop models for disease detection and treatment response assessment.

Develop deep learning and 3D computer vision models for state estimation, visual navigation, localization, depth, correspondence, and pose estimation. Own data preparation, training, evaluation, benchmarking, and reproducible workflows. Integrate learned measurements into VIO and terrain-relative navigation systems, optimize models for onboard compute, and collaborate with deployment, software, systems, and flight-test teams to deliver production-ready Hivemind SDK components.

Design, build, and deploy scalable ML pipelines for multimodal neuroscience data. Research, train, and deploy advanced deep learning and reinforcement learning models to predict brain states, detect pathological patterns, and generate personalized neuromodulation parameters. Select AI frameworks and cloud infrastructure, collaborate with computational neuroscientists, and contribute to IP and publications.

Develop deep learning methods for protein language modeling, structural modeling, and multimodal representation learning of TCR-pMHC interactions. Apply machine learning, statistics, computational biology, and data science to complex biological datasets while collaborating with wet-lab scientists. The role includes training and fine-tuning protein and structure-prediction models, building AI pipelines, and using rational structure-based and physics-informed approaches to support therapeutic discovery.

Lead research and design of large-scale deep learning systems for protein structure prediction and molecular glue design. Develop novel architectures and training paradigms, collaborate with chemistry/biology experts, publish at top ML venues, and apply models to drive drug discovery value.

Design, optimize, and deploy deep learning models for customer behavior prediction. Perform feature engineering, implement research-informed architectures, scale training pipelines, collaborate with client teams, document findings, and uphold engineering best practices.

Lead design and training of large-scale transformer and hybrid foundation models across text and multimodal domains. Own architecture choices, diagnose large-scale training instabilities, optimize scaling tradeoffs (data, compute, model), design loss and alignment strategies, and execute distributed training (FSDP/ZeRO, tensor/pipeline parallelism) with mixed precision to build in-house foundation model expertise.

Lead and grow an engineering team building GPU‑accelerated deep learning inference frameworks. Drive roadmap, collaborate with compiler/libraries/research teams, optimize LLM and multimodal model performance, and oversee multi‑GPU communications and production deployment of inference software.

Design and implement novel neural network architectures for YOLO, run large-scale experiments and ablation studies, optimize models for accuracy and efficiency (quantization/pruning/distillation), build training and evaluation pipelines, and collaborate with R&D and engineering to productionize open-source models.

Conduct research and develop algorithms for real-time video processing and deepfake detection, translating machine learning models for production use.