Remote ML Engineer - End-to-End Vision & Document AI

True Talent Solutions Partners; San Francisco (CA); Remote; USD 200,000 - 325,000; Full time

Benefits offered by this job

Visa sponsorship

Job summary

True Talent Solutions Partners is seeking a machine learning engineer to own models end-to-end, from research and experimentation through production deployment for enterprise workloads.

You will build and deploy models for parsing unstructured data, experiment to improve accuracy, create data pipelines, and integrate models into the product while shaping engineering strategy with leadership and customers.

Qualifications

• 4+ years of full-time ML engineering experience after graduation.
• Experience training, fine-tuning, and evaluating ML models for production systems.
• Hands-on experience with OCR and document understanding, as well as computer vision, vision-language models, or LLMs.
• Strong Python skills and experience building with ML tools and frameworks; Streamlit experience is relevant.
• Startup or startup-like environment with ownership of models shipped to users.
• Strong computer science or engineering fundamentals.
• Research publications in CV, vision-language models, or document understanding are a plus.

Responsibilities

• Build and deploy models for parsing and interpreting unstructured data.
• Experiment with techniques to improve language model accuracy and develop tools to measure results.
• Create data pipelines, evaluate model performance, and integrate models into the product.
• Contribute to product direction and engineering strategy in collaboration with company leadership and customers.

Skills

Python

OCR

Computer vision

Vision-language models

Streamlit

Education

Bachelor's degree in Computer Science or related field

Tools

TensorFlow

PyTorch

Streamlit

Job description

True Talent Solutions Partners is seeking a machine learning engineer to own models end-to-end, from research and experimentation through production deployment for enterprise workloads.

You will build and deploy models for parsing unstructured data, experiment to improve accuracy, create data pipelines, and integrate models into the product while shaping engineering strategy with leadership and customers.