ML Engineer: Real-Time Transformer Pipelines (Remote)

Adapter; United States; Remote; USD 180,000 - 225,000; Full time

Benefits offered by this job

Early stage equity

Health insurance

Generous PTO

Remote and in-person collaboration

Job summary

Adapter is seeking a Machine Learning Engineer to fine-tune transformer models and implement real-time fine-tuning pipelines in production. You will collaborate with designers, engineers, and innovators on consumer-use cases for intelligent technologies.

The team embraces both remote work and in-person collaboration across Austin, NYC, and the Bay Area. Responsibilities include deploying models at scale, monitoring performance in real time, and staying ahead of ML advancements.

Qualifications

• 3+ years of experience developing and deploying ML models in production environments.
• Experience with large-scale data processing and distributed systems.
• Strong programming skills in Python and proficiency with ML libraries (PyTorch, TensorFlow).

Responsibilities

• Use cutting-edge technologies (LLMs, multimodal models) to solve complex problems.
• Build and maintain fine-tuning pipelines and production-ready frameworks.
• Deploy models at scale and integrate with existing software systems.

Skills

Python

PyTorch

TensorFlow

Fine-tuning pipelines

Production ML

Distributed systems

Tools

Docker

Job description

Adapter is seeking a Machine Learning Engineer to fine-tune transformer models and implement real-time fine-tuning pipelines in production. You will collaborate with designers, engineers, and innovators on consumer-use cases for intelligent technologies.

The team embraces both remote work and in-person collaboration across Austin, NYC, and the Bay Area. Responsibilities include deploying models at scale, monitoring performance in real time, and staying ahead of ML advancements.