Sr. Machine Learning Engineer

Twenty80 LLC; San Francisco (CA); On-site; USD 140,000 - 210,000; Full time

Job summary

Twenty80 LLC seeks a Senior ML Engineer in San Francisco to build and scale AI systems used daily by many users. You will own ML projects end-to-end, from research through deployment and iteration, and develop multimodal models across video, text, images, and audio.

You’ll work with LLMs and external AI APIs to create content understanding models and semantic search capabilities, while maintaining robust MLOps and collaborating with product teams for impactful outcomes.

Qualifications

• 4–8 years of ML engineering experience, including production model deployment.
• Proficient in Python and core ML concepts with clean, maintainable code.
• Experience building end-to-end ML pipelines: data handling, training, deployment, monitoring.
• Comfortable working with LLMs and AI APIs in production.
• Expertise across NLP, computer vision, and audio processing.
• Product-focused, identifying ML opportunities to solve user problems.

Responsibilities

• Own ML projects end-to-end: research, prototyping, training, deployment, and iteration.
• Build multimodal ML systems for video, text, images, and audio data.
• Develop applications using LLMs and AI techniques like Retrieval-Augmented Generation (RAG).
• Create models for content understanding across text and visual media.
• Build search and discovery systems using semantic embeddings and retrieval methods.
• Develop audio analysis pipelines.
• Maintain and optimize MLOps infrastructure: data pipelines, model serving, monitoring, experiment tracking.
• Collaborate with product teams to translate requirements into deployable ML solutions.

Skills

Python

ML fundamentals

End-to-end ML pipelines

LLMs / AI APIs

NLP

Computer Vision

Audio processing

Product thinking

Tools

MLflow

AWS

GCP

Vector databases

Job description

San Francisco, CA

About the role

A San Francisco startup is seeking a Senior Machine Learning Engineer to build and scale AI systems. You’ll work on impactful projects with models used daily by many users, with your work directly influencing the product.

Key Responsibilities

• Own ML projects end-to-end: research, prototyping, training, deployment, and iteration.
• Build multimodal ML systems for video, text, images, and audio data.
• Develop applications using Large Language Models (LLMs) and modern AI techniques such as Retrieval-Augmented Generation (RAG) with external AI APIs.
• Create models for content understanding and classification across text and visual media.
• Build search and discovery systems using semantic embeddings and retrieval methods.
• Develop audio analysis pipelines.
• Maintain and optimize MLOps infrastructure, including data pipelines, model serving, monitoring, and experiment tracking.
• Work with product teams and stakeholders to turn high-level requirements into deployable ML solutions.

Requirements

• Have 4–8 years of ML engineering experience, including production model deployment.
• Be proficient in Python and core ML concepts, writing clean and maintainable code.
• Have experience building end-to-end ML pipelines: data handling, training, deployment, and monitoring.
• Be comfortable working with LLMs and AI APIs in production.
• Have expertise across NLP, computer vision, and audio processing.
• Be product-focused, identifying where ML can solve user problems and improve outcomes.

Tech Skills

• Deep Learning: Neural networks, transformers, CNNs
• NLP / LLMs: RAG systems, prompt engineering, vector databases, fine-tuning, orchestration tools
• Computer Vision: Image classification, object detection, visual content understanding, embeddings
• Search & Retrieval: Semantic search, multimodal retrieval, embedding models
• MLOps: Model deployment, monitoring, experiment tracking (e.g., MLflow)
• Cloud ML: Deploying models on cloud platforms (AWS, GCP) with scalable inference

Why This Role is Exciting

• Lead ML strategy and infrastructure in a growing startup environment.
• See your models reach users quickly for fast feedback and iteration.
• Work on challenging ML problems across video, language, and audio.
• Join an early ML team in a product-focused, engineering-first culture.
• Enjoy high autonomy and ownership over impactful projects.