ML Engineer: Self-Supervised Representation Learning (NYC)

BehaviorAI; New York (NY); On-site; USD 160,000 - 200,000; Full time

Benefits offered by this job

Equity

Comprehensive benefits

Competitive salary

Job summary

BehaviorAI in New York City is seeking a Machine Learning Engineer to own the core modeling layer of a ground-up platform. You will turn vast unlabeled sensing data into general-purpose models and collaborate with the founding team across ML, sensing, data, software, and product.

You will work on self-supervised learning, representation learning, and large-scale training using PyTorch, building scalable systems in an early-stage startup environment.

Qualifications

• MS/PhD in CS/EE/ML or equivalent industry experience.
• 3+ years hands-on deep learning, self-supervised or representation learning.
• Strong PyTorch and sequential data architectures.
• Experience scaling models with distributed training and data pipelines.
• Comfort with real-world time-series data and sparse labels.

Responsibilities

• Design, train, and scale self-supervised pretraining approaches for multimodal time-series data.
• Build models that generalize across users, devices, and real-world conditions.
• Develop fine-tuning and adaptation strategies for downstream tasks.
• Define evaluation protocols and benchmarks (linear probing, few-shot transfer).
• Shape data collection, curation, and pretraining corpus strategy with the data team.
• Partner with hardware and sensing teams so modeling informs sensor design.
• Help build training infrastructure, experiment tracking, and engineering practices as the team grows.

Skills

Ownership mindset

Problem solving

Engineering rigor

Adaptability

Education

MS or PhD in Computer Science / Electrical Engineering / Machine Learning or related field

Tools

PyTorch

Transformers

Temporal CNNs

Distributed training

Job description

BehaviorAI in New York City is seeking a Machine Learning Engineer to own the core modeling layer of a ground-up platform. You will turn vast unlabeled sensing data into general-purpose models and collaborate with the founding team across ML, sensing, data, software, and product.

You will work on self-supervised learning, representation learning, and large-scale training using PyTorch, building scalable systems in an early-stage startup environment.