ML Engineer - Austin, TX

Baasi Inc.; Austin (TX); Hybrid; USD 100,000 - 130,000; Full time

Benefits offered by this job

Competitive compensation

Equity potential

Flexible remote work

Job summary

A stealth-mode startup in Austin, Texas is seeking an AI ML Engineer (Python) to design and implement AI pipelines for their next-generation infrastructure. This role focuses on productizing existing fine-tuning techniques, automating data preprocessing, and collaborating with a technical team. The ideal candidate will have strong Python skills, experience with PyTorch, and familiarity with LoRA/QLoRA methods. Join us for competitive compensation and flexible remote work opportunities.

Qualifications

• Strong programming skills in Python.
• Hands-on experience with PyTorch and Hugging Face ecosystem.
• Familiarity with LoRA/QLoRA or parameter-efficient fine-tuning methods.
• Understanding of mixed precision training (FP16/BF16) and memory optimization techniques.
• Experience building training scripts that are production-ready.
• Comfortable working in Linux GPU environments.

Responsibilities

• Implement and maintain LoRA/QLoRA fine-tuning pipelines using PyTorch + Hugging Face Transformers + PEFT.
• Develop logic for incremental training and adapter stacking.
• Automate data preprocessing for user-supplied datasets.
• Build training scripts/workflows that integrate with orchestration backends.
• Implement monitoring hooks for dashboards.
• Collaborate with DevOps to ensure reproducible training environments.

Skills

Python

PyTorch

Hugging Face ecosystem

Linux GPU environments

LoRA/QLoRA

MLOps

Job description

We are a stealth-mode startup building next-generation infrastructure for the AI industry. Our mission is to make advanced language models portable, efficient, and customizable for real-world deployments. We’re building tools that allow vendors to fine-tune models easily and deploy them securely on diverse hardware.

Role

We are seeking a AI ML Engineer (Python) to help design and implement our AI Pipelines. This is not an academic research role — you will be productizing and automating existing fine-tuning techniques (LoRA/QLoRA) so vendors can train and manage their own adapters with minimal effort.

You’ll work closely with backend engineers (Node.js) who orchestrate jobs and dashboards, while you focus on the training pipelines and adapter export logic.

Responsibilities

Implement and maintain LoRA/QLoRA fine-tuning pipelines using PyTorch + Hugging Face Transformers + PEFT.

Develop logic for incremental training and adapter stacking, producing clean, versioned “delta packs.”

Automate data preprocessing (tokenization, formatting, filtering) for user-supplied datasets.

Build training scripts/workflows that integrate with orchestration backends (Node.js, REST/gRPC, or job queues).

Implement monitoring hooks (loss curves, checkpoints, eval metrics) to feed into dashboards.

Collaborate with DevOps to ensure reproducible, portable training environments.

Write tests to guarantee reproducibility and correctness of adapter outputs.

Willingness to occasionally be present in the office for discussions and team collaboration.

Requirements

Strong programming skills in Python.

Hands‑on experience with PyTorch and the Hugging Face ecosystem (Transformers, Datasets, PEFT).

Familiarity with LoRA/QLoRA or parameter‑efficient fine‑tuning methods.

Understanding of mixed precision training (FP16/BF16) and memory optimization techniques.

Experience building training scripts that are production‑ready (reproducibility, logging, error handling).

Comfortable working in Linux GPU environments (CUDA, ROCm).

Ability to collaborate with backend/frontend engineers who are not ML specialists.

Nice to Have

Experience with bitsandbytes, xformers, or flash-attention.

Familiarity with distributed training (multi‑GPU, NCCL, DeepSpeed, or Accelerate).

Prior work in MLOps or packaging ML pipelines for deployment.

Contributions to open‑source ML libraries.

Why Join

Build the core training product that lets vendors adapt models safely and efficiently.

Focus on product engineering, not open‑ended research.

Collaborate with a lean, highly technical team at the intersection of AI and systems.

Competitive compensation, equity potential, and flexible remote work.

To Apply

Please send your CV along with your motivation to apply to work at Baasi. Make sure to specify the position you are currently applying for.

Apply now

We encourage individuals of all backgrounds to apply: We’re actively taking steps to make sure our culture is inclusive and that our processes and practices promote equity for all. We’d love to have the opportunity to consider you!