Senior ML/AI Engineer

Reacher•San Francisco, CA

13d

About The Position

About Reacher: We're the #1 TikTok Shop partner helping brands like Under Armour, Hanes, HeyDude, and Logitech scale their affiliate marketing. We've crossed 7 figures in ARR , and are rapidly scaling our team this year. Our vision is to become the Hubspot for creator marketing , powering brands and creators to connect and grow across all commerce platforms (Youtube Shopping, Instagram Shopping, Shopify, Amazon). We're building key infrastructure for the creator economy and implementing AI with the world's largest brands and creators . Your work will directly impact users on day 1 —our user base depends on our product daily. Your work will directly impact users on day 1. We have a highly responsive user base that depends on our product day in and day out.

Requirements

You have 4–8 years of ML engineering experience building and deploying models in production
You have strong Python and ML fundamentals and write clean, maintainable production code
You've built ML models end-to-end : data pipelines, training, serving, monitoring
You're experienced with modern ML frameworks (PyTorch, TensorFlow, scikit-learn)
You have production experience with LLMs and AI APIs (OpenAI, Anthropic, Hugging Face)
You can build ML systems across multiple domains —NLP, computer vision, and audio
You're product-minded and identify where ML can solve user problems and improve business metrics
You're comfortable with MLOps tools and cloud platforms (AWS or GCP)
You're resourceful and thrive in ambiguous environments

Nice To Haves

You've worked at an early-stage startup or been a first/early ML hire
You have experience building multimodal search systems or semantic retrieval at scale
You have experience with video understanding —extracting features, generating embeddings, or analyzing visual content
You've implemented feature stores or advanced MLOps infrastructure
You have experience with real-time inference and low-latency model serving
You have expertise in adversarial robustness and model safety testing
You've built hybrid RAG + fine-tuning systems
You have experience with multimodal models (vision + language, audio + text)
You're experienced with model optimization (quantization, distillation, pruning)
You have a track record of shipping ML-driven product features that moved key metrics
You've shipped side projects or contributed to open-source ML projects

Responsibilities

Own ML systems end-to-end : research, prototype, train, deploy, and iterate rapidly
Build multimodal ML systems for video, text, images, and audio at scale
Design and deploy LLM-powered applications using RAG and AI APIs
Develop content understanding and classification models for text and visual data
Build search and discovery systems using embeddings and semantic retrieval
Create audio analysis and processing pipelines
Build MLOps infrastructure —data pipelines, model serving, monitoring, and experiment tracking
Ideate ML/AI product features with product and customers
Leverage modern AI tools to accelerate development
Work directly with customers and translate vague requirements into shipped ML solutions
Ship fast and learn fast —high urgency environment