ML Engineer: LLMs & Production Systems (SF, In-Office)

Reducto, Inc.; San Francisco (CA); On-site; USD 150,000 - 210,000; Full time

Benefits offered by this job

Unlimited PTO

Lunch provided

Transportation reimbursement

Health insurance

Wellness budget

Parental leave

Job summary

Reducto in San Francisco is seeking an in-person ML engineer to train, fine-tune, and evaluate models used in production. You will build data pipelines, experiment with new techniques, and collaborate directly with the founders and customer teams to shape the product direction.

You should be proficient in Python, experienced with traditional computer vision and vision-language models (VLMs), and able to ship end-to-end features from ideation to user delivery.

Qualifications

• 2+ years of experience with training, fine tuning, and evaluating ML models used in production systems.
• Strong Python skills; proficient with traditional computer vision and vision-language models (VLMs).
• Ability to build tools and simple apps (e.g., Streamlit) to test hypotheses and datasets.
• Quantitative, hands-on approach across the full development lifecycle from ideation to shipping.

Responsibilities

• Train and deploy state-of-the-art models for parsing and interpreting unstructured data.
• Experiment with novel techniques to improve model accuracy.
• Build data pipelines, evaluate model performance, and integrate models into the product.
• Collaborate with founders and customers to shape product direction and engineering strategy.

Skills

Python

Computer vision

VLMs

Production ML

Tools

Streamlit

Job description

Reducto in San Francisco is seeking an in-person ML engineer to train, fine-tune, and evaluate models used in production. You will build data pipelines, experiment with new techniques, and collaborate directly with the founders and customer teams to shape the product direction.

You should be proficient in Python, experienced with traditional computer vision and vision-language models (VLMs), and able to ship end-to-end features from ideation to user delivery.