Senior Data Scientist

About Us

AI Workers

autonomous, self-learning AI

Role Overview

Senior Data Scientist (Research)

LLM-backed speech systems

Your work will help shape the cognitive and auditory intelligence of our AI Workers — making them expressive, contextually aware, and capable of continual self-improvement.

Key Responsibilities

• Research and develop

  direct speech-to-speech modeling pipelines

  leveraging

  LLM backbones

  (e.g., Qwen) and

  audio encoders/decoders

  (e.g., Whisper).
• Model and evaluate

  turn-taking, latency handling, and voice activity detection (VAD)

  mechanisms for real-time conversational AI.
• Explore

  Agentic Reinforcement Training (ART)

  and

  self-learning loops

  for continual improvement of speech models.
• Design and experiment with

  memory-augmented multimodal architectures

  that allow persistent recall and contextual understanding across interactions.
• Develop novel approaches for

  expressive speech generation

  ,

  emotion conditioning

  , and

  speaker identity preservation

  .
• Collaborate with the Research Director to define and drive

  long-term AI research roadmaps

  around autonomy, speech cognition, and agentic intelligence.
• Conduct internal benchmarks and contribute to

  SOTA research

  in multimodal learning, audio-language alignment, and agent reasoning.
• Work closely with MLEs for model training and evaluation, while focusing entirely on

  research design, datasets, and experimentation

  .

Requirements

• Experience:

  2+ years of applied or academic experience in

  speech, multimodal, or LLM research

  .

• Education:

  Bachelor’s or Master’s in Computer Science, AI, or Electrical Engineering.

• Programming:

  Strong in

  Python

  and scientific computing; experience in

  JupyterHub

  environments.

• Core Skills:

• Deep understanding of

  LLM architectures

  ,

  transformers

  , and

  multimodal embeddings

  .
• Experience with

  speech modeling pipelines

  — ASR, TTS, speech-to-speech, or audio-language models.
• Understanding of

  turn-taking systems

  ,

  VAD

  ,

  prosody modeling

  , and

  real-time voice synthesis

  .
• Familiarity with

  self-supervised learning

  ,

  contrastive representation learning

  , and

  agentic reinforcement (ART)

  .
• Strong background in

  dataset curation, experimental design

  , and

  model evaluation

  .

• Tools & Ecosystem:

• Comfortable using

  Agno

  ,

  Pipecat

  ,

  HuggingFace

  , and

  Pytorch

  .
• Familiarity with

  LangChain

  ,

  vector stores

  , and

  memory systems

  for agentic research.
• Excellent written communication and ability to clearly articulate research insights.

Mindset & Traits

• Deep

  research curiosity

  — constantly exploring new methods beyond published papers.
• Strong

  ownership mindset

  — design, experiment, analyze, iterate independently.
• Excited about

  building cognitive speech systems

  , not just fine-tuning existing ones.

• Commitment to mission:

  passionate about shaping the future of autonomous AI Workers.
  

What We Offer

• Opportunity to work on

  frontier research

  in speech, multimodal, and agentic AI.
• Access to

  dedicated compute infrastructure

  and collaboration with experienced ML researchers.
• Environment that encourages

  paper publication, experimentation, and rapid innovation

  .

• Industry-best compensation

  with no upper cap for exceptional contributors.
• High-growth trajectory in one of the fastest-evolving AI domains globally.