Machine Learning Engineer - Representation Learning

About the jobWe’re looking for a Machine Learning Engineer in NYC with deep expertise in self-supervised learning and large-scale representation learning. This role is for someone who wants to own the core modeling layer of a technology platform built from the ground up, turning large amounts of raw, unlabeled sensing data into general-purpose models. You’ll work directly with the founding team across machine learning, sensing, data, software, and product. This is a full-time, on-site role in New York City.The FocusBuild AI models that learn general-purpose representations from large-scale data.What You Will DoDesign, train, and scale self-supervised pretraining approaches (masked modeling, contrastive, predictive/JEPA-style, and similar) for multimodal time-series data.Build models that generalize across users, devices, and real-world conditions.Develop fine-tuning and adaptation strategies for various downstream tasks.Define evaluation protocols and benchmarks, including linear probing, few-shot transfer, and held-out-user generalization.Work with the data team to shape data collection, curation, and pretraining corpus strategy.Partner with hardware and sensing teams so that modeling insights feed back into sensor design.Help build training infrastructure, experiment tracking, and engineering practices as the team grows.What We Are Looking ForMS or PhD in Computer Science, Electrical Engineering, Machine Learning, or a related field, or equivalent industry experience.3+ years of hands-on deep learning experience, with direct work on self-supervised learning, representation learning, or foundation model pretraining.Strong proficiency in PyTorch and in modern architectures for sequential data (Transformers, state-space models, temporal CNNs).Experience training models at scale: distributed training, mixed precision, data pipelines for large datasets, and debugging training instability.Comfort working with real-world time-series data, noisy signals, and imperfect or sparse labels.Ability to move quickly from research ideas to working systems while keeping engineering rigor.An ownership mindset, practical problem-solving skills, and comfort with the pace and uncertainty of an early-stage startup.Nice to HaveExperience working with wearable sensing modalities.Experience with multimodal learning or cross-modal alignment.Publications in venues such as NeurIPS, ICML, ICLR, CVPR, IMWUT/UbiComp, or related venues.The PerksDirect collaboration with the founding team, a competitive salary and equity, comprehensive benefits, top-tier VC backing, and a rare chance to help build a “Zero to One” technology company at the intersection of AI and real-world human behavior understanding.Compensation: 160K - 200K base salary + equityLocation: In-person, New York CityApply: Send your resume to career@behaviorai.ai or apply directly on LinkedIn.