Machine Learning Engineer, New York City

We aim to close the gap between humans & AI, to better navigate the world around & inside us. Our north star is an extension of self, enabled by new software and hardware.

As a Machine Learning Engineer, you will design & build a new kind of entity—a natural extension of intent and thought. This role is perfect for someone who has developed user-facing products and who cares deeply about human-computer interaction. You should especially consider this role if you are sensitive to the qualitative differences between models.

Responsibilities
• Design and improve user-facing model behaviors including personality, memory, and voice expressivity.
• Develop infrastructure to rapidly generate synthetic data, employ fine-tuning methods, and evaluate models across qualitative and quantitative metrics.
• Optimize inference pipelines for latency and scalability

Qualifications
• Strong experience in applied machine learning, including data curation, fine-tuning, and scalable model evaluation
• Hands-on experience with LLMs, conversational applications is a major plus
• Strong opinions on qualitative differences between models (e.g. Claude Sonnet 3.5 vs. GPT 4.5)
• Proficiency in Python, PyTorch, and/or TensorFlow
• High independence and comfort with ambiguity
• Strong interest in human-computer interaction
• Portfolio of prior work is a major plus
• Prior hardware experience is not needed or required

Details
• $160-200k base
• High early-stage equity
• Apply here

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Machine Learning Engineer, New York City

We aim to close the gap between humans & AI, to better navigate the world around & inside us. Our north star is an extension of self, enabled by new software and hardware.

As a Machine Learning Engineer, you will design & build a new kind of entity—a natural extension of intent and thought. This role is perfect for someone who has developed user-facing products and who cares deeply about human-computer interaction. You should especially consider this role if you are sensitive to the qualitative differences between models.

Responsibilities
• Design and improve user-facing model behaviors including personality, memory, and voice expressivity.
• Develop infrastructure to rapidly generate synthetic data, employ fine-tuning methods, and evaluate models across qualitative and quantitative metrics.
• Optimize inference pipelines for latency and scalability

Qualifications
• Strong experience in applied machine learning, including data curation, fine-tuning, and scalable model evaluation
• Hands-on experience with LLMs, conversational applications is a major plus
• Strong opinions on qualitative differences between models (e.g. Claude Sonnet 3.5 vs. GPT 4.5)
• Proficiency in Python, PyTorch, and/or TensorFlow
• High independence and comfort with ambiguity
• Strong interest in human-computer interaction
• Portfolio of prior work is a major plus
• Prior hardware experience is not needed or required

Details
• $160-200k base
• High early-stage equity
• Apply here

Back