Software Engineer - (AI/ML)

Location:

Compensation:

Machani Robotics

think, understand, and respond

If you’ve ever wanted to bring AI models to life — to make them move, speak, and feel present in the world — this is your stage.

AI Engineer (NLP)

LoRA adapters

Systems

• Fine-tune and adapt foundation models using

  LoRA

  ,

  PEFT

  , or

  QLoRA

  methodologies.
• Design retraining workflows using Unsloth.ai,

  PyTorch Lightning

  , or

  Hugging Face

  pipelines.
• Implement

  RAG (Retrieval-Augmented Generation)

  layers with hybrid memory stores such as

  Vector Store

  ,

  Redis

  , and

  GraphDB

  .
• Optimize model inference through

  GGUF

  ,

  GGML

  , or

  ONNXRuntime

  pipelines for real-time humanoid interaction.
• Contribute to the

  AI Daemon

  layer — orchestrating multimodal models for speech, vision, and cognition.
• Build automation around

  data curation

  ,

  evaluation

  , and

  model versioning

  using CI/CD workflows.
• Collaborate with cross-functional teams to deploy models across

  cloud

  ,

  edge

  , and

  on-device

  environments.

• Bachelor’s or Master’s degree in

  Computer Science

  ,

  Artificial Intelligence

  ,

  Machine Learning

  , or related fields.
• Deep understanding of

  transformer architectures

  ,

  attention mechanisms

  , and

  tokenization

  .
• Hands-on experience in

  LoRA/PEFT fine-tuning

  ,

  embedding generation

  , and

  inference optimization

  .
• Proficiency in

  Python

  and

  PyTorch

  (experience with

  TensorFlow

  ,

  vLLM

  , or

  Hugging Face Transformers

  is a plus).
• Strong grasp of

  vector databases

  ,

  RAG frameworks

  , and

  model evaluation metrics

  .
• Comfortable working with

  Linux-based systems

  ,

  Docker

  , and

  Kubernetes

  .
• Excellent collaboration skills and curiosity to explore

  AI-driven robotics applications

  .

• Familiarity with

  Go

  or

  Rust

  for backend model integration.
• Experience with

  quantization

  ,

  distillation

  , or

  ONNX graph optimization

  .
• Exposure to

  speech

  or

  vision

  models and multi-modal alignment.
• Contributions to open-source model optimization or training frameworks.

• Build AI that lives inside humanoid robots.
• Work with a passionate, cross-disciplinary team combining

  software

  ,

  hardware

  , and

  AI research

  .
• Access to cutting-edge compute infrastructure and model experimentation pipelines.
• Opportunity to publish, innovate, and lead next-gen AI integrations.
• Competitive compensation and long-term career growth within a frontier robotics company.