Inactive
T

Tacit

AI-driven digital twin for building operations

Data Scientist Intern

Applied Machine LearningPosted on Oct 9
Internship · Spring 2027, Summer 2027San Francisco, CA, USA$40 - $50 /hr

About the Job

Requirements

  • Currently enrolled in, or recently graduated from, a PhD program in neuroscience, biomedical engineering, computer science, applied mathematics, or a related field.
  • Fluency in Python; experience with dimensionality reduction techniques and time-series data analysis.
  • Effective communication and writing skills, and the ability to collaborate across teams and adapt to new fields.
  • Independent work ethic, flexibility, and resourcefulness; comfort working independently in a fast-moving startup environment.

Responsibilities

  • Design, implement, and test algorithms for feature extraction from biosignals for use in machine-learning models.
  • Rapidly iterate on experiments evaluating hardware prototypes and data-collection improvements, and build understanding of key aspects of the system.
  • Develop analytical tools for visualization and establish data-quality metrics.
  • Work with hardware and electrical engineers to improve the sensing stack.
  • Collaborate with the machine-learning team on data augmentation, multimodal training, and synthetic-data experiments to improve generalization.

Desired Qualifications

  • PyTorch experience.
  • Familiarity with real-time human-machine interaction, including automatic speech recognition and closed-loop neural interfaces.
  • Expertise in speech motor control, communication prostheses, or speech neuroscience.
  • Prior experience with consumer wearable devices.
  • Basic machine-learning fundamentals and machine-learning infrastructure, including experiment-management tracking tools such as Hydra and WandB, and AWS and Slurm.

About the Company

Tacit

AI-driven digital twin for building operations

Tacit develops an AI-powered digital twin platform for real estate buildings that automates and optimizes operations by connecting to HVAC, BMS, meters and IoT sensors to form a unified view. Its Agentic AI uses context-aware agents to diagnose, troubleshoot, and autonomously adjust systems to reduce manual intervention. It provides root-cause analysis, predictive maintenance, and energy optimization based on real-time data like occupancy and weather, aiming to lower OpEx and energy waste. Audit trails log automated actions and reasoning to ensure compliance, with the goal of freeing facilities teams for more strategic work.

Company size
1-10
Company stage
Seed
Total funding
$330K
Headquarters
London, United Kingdom
Founded
1997

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