Senior Machine Learning Engineer

RainesDev; San Francisco (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

Reacher is seeking a Senior Machine Learning Engineer to own ML systems end-to-end, building multimodal models for video, text, images, and audio. You will deploy LLM-powered applications using RAG and external AI APIs, and collaborate with product teams to translate requirements into deployable solutions.

You will lead development across NLP, computer vision, and audio domains, shaping scalable ML infrastructure and delivering high-impact features with rapid iteration.

Qualifications

• Deep learning, transformer networks, CNNs.
• RAG systems, prompt engineering, embeddings, fine-tuning.
• Experience across NLP, CV, and audio domains.
• Hands-on with ML model deployment, monitoring, and serving.
• Cloud deployment on AWS or GCP for scalable inference.

Responsibilities

• Own ML systems end-to-end: research, prototype, train, deploy, iterate.
• Build multimodal ML systems for video, text, images, and audio data.
• Design and deploy LLM-powered applications using RAG and external AI APIs.
• Develop robust content understanding and classification models for text and visuals.
• Create semantic search and discovery systems with embeddings.
• Construct audio analysis and processing pipelines.
• Establish and enhance MLOps: data pipelines, model serving, monitoring, experiments.
• Translate vague requirements into shippable ML solutions with product teams.

Skills

Deep Learning

NLP/LLMs

Computer Vision

Search & Retrieval

MLOps

Cloud ML

Vector databases

MLflow

Tools

MLflow

Vector databases

Job description

San Francisco, United States | Posted on 11/21/2025

This is the job description rewritten to be highly secretive and difficult to trace to the original company, "Reacher." All proprietary names, specific platforms (TikTok Shop, Hubspot, Under Armour, Hanes, etc.), and concrete growth metrics (7 figures ARR) have been generalized or removed.

Senior Machine Learning Engineer

About the Company

We are a rapidly scaling, post-revenue technology company building foundational infrastructure for the creator economy. Our platform connects major global brands and content creators, powering commerce and growth across various digital channels (e-commerce platforms, video sharing sites, social commerce).

We are implementing cutting-edge AI to solve complex problems for some of the world's largest companies and creators. We have a highly engaged and responsive user base that depends on our product daily, meaning your work will have a direct and immediate impact.

What You'll Do

You will be a core contributor, owning ML systems end-to-end: research, prototype, train, deploy, and iterate rapidly.

Build multimodal ML systems for analyzing large-scale video, text, images, and audio data.

Design and deploy advanced Large Language Model (LLM)-powered applications using modern Retrieval-Augmented Generation (RAG) and external AI APIs.

Develop robust content understanding and classification models for text and visual data.

Create sophisticated search and discovery systems using embeddings and semantic retrieval.

Construct audio analysis and processing pipelines.

Establish and enhance our MLOps infrastructure: data pipelines, model serving, monitoring, and experiment tracking.

Work closely with product teams and customers to translate vague requirements into shippable ML solutions.

You're a Fit If

You have 4–8 years of ML engineering experience deploying models in production.

You have strong Python and ML fundamentals and write clean, maintainable production code.

You have successfully built ML models end-to-end: data pipelines, training, serving, and monitoring.

You have production experience with LLMs and AI APIs (e.g., Anthropic, OpenAI).

You are comfortable building ML systems across multiple domains—NLP, computer vision, and audio.

You are product-minded and identify where ML can solve user problems and improve business metrics.

ML & AI-Specific Skills We Value

Deep Learning: Experience with neural networks, transformers, and CNNs.

NLP/LLMs: RAG systems, prompt engineering, vector databases, fine-tuning, and modern orchestration tools.

Computer Vision: Image classification, object detection, visual content understanding, and image embeddings.

Search & Retrieval: Semantic search, embedding models, and multimodal retrieval.

MLOps: Model serving, monitoring, and experiment tracking (e.g., MLflow).

Cloud ML: Comfortable with major cloud platforms (AWS or GCP) for model deployment and scalable inference.

Why Join Us

Be the ML/AI leader—define our ML strategy and infrastructure as we scale.

Your models reach users within days, not months.

Work on diverse, high-impact ML problems across video, language, and audio.

Opportunity to be an early ML hire in a strong engineering-first culture.

High autonomy and visibility—no boring tickets.