Top Deep Learning Jobs in Boston, MA

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.

Develop and evaluate deep learning models for advanced materials discovery and electrophoretic display design. Build data-processing, training, and evaluation pipelines; lead inverse design and model-based discovery using Bayesian optimization, diffusion models, and related methods. Collaborate with scientists to integrate domain knowledge, improve model interpretability, and ensure data quality, reproducibility, and experimental lineage.

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.

Develops multimodal deep learning and reasoning models for Edge AI, including time-series, audio, video, and sensor data. Responsibilities span data collection, annotation, preprocessing, model training, evaluation, supervised fine-tuning, reinforcement learning, knowledge distillation, transformer modification, and multi-agent orchestration. The role also prototypes emerging AI methods, documents research, and publishes results. Requires extensive AI/ML product experience, a related PhD, and a strong publication record.

Conduct research engineering for a probabilistic foundation model spanning temporal, tabular, and mixed-modality data. Design model architectures, encoders, attention mechanisms, distributional output heads, and decoding strategies; implement and train scalable PyTorch models; run controlled experiments, ablations, and performance profiling; diagnose training behavior; and produce reproducible, production-quality code. Candidates with stochastic modeling expertise may also develop synthetic data generators and simulate richer stochastic dynamics.

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.

Lead and scale an engineering team building GPU-accelerated deep learning inference frameworks. Drive strategy and roadmap, partner with compiler/libraries/research teams, oversee performance tuning and multi-GPU optimizations, mentor engineers, and deliver production-ready inference pipelines for LLM, multimodal, and generative AI across datacenter and edge platforms.

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.