Senior Machine Learning Engineer
San Francisco, California, USA
170000-225000
Job Description
We're a leading partner in social commerce, collaborating with major athletic wear, footwear, and electronics brands to expand their influencer-driven sales channels. Having achieved significant revenue milestones, we're rapidly expanding our technical team. Our mission is to create the ultimate platform connecting content creators with brands across major e-commerce ecosystems including video shopping platforms, social media marketplaces, and online retail channels.
We're developing critical infrastructure for the digital creator landscape and integrating artificial intelligence with enterprise-level brands and influencers. Your contributions will have immediate user impact—our customer base relies on our platform for their daily operations.
Responsibilities
Architect and maintain machine learning systems across the full lifecycle: research, prototyping, training, deployment, and continuous improvement
Develop multimodal ML solutions processing video content, textual data, imagery, and audio at enterprise scale
Engineer and implement large language model applications utilizing retrieval-augmented generation and AI service integrations
Create content analysis and categorization models for written and visual media
Build discovery and search capabilities using vector embeddings and semantic matching
Develop audio processing and analysis workflows
Establish ML operations infrastructure including data engineering pipelines, model deployment services, performance monitoring, and experimentation frameworks
Collaborate on ML/AI product innovation with product teams and clients
Utilize cutting-edge AI tooling to enhance development velocity
Partner directly with clients to transform ambiguous needs into production ML solutions
Deliver rapidly in a fast-paced, high-priority environment
Qualifications
Ideal Candidate Profile
4–8 years building and deploying production machine learning systems
Strong Python expertise with solid ML fundamentals and production-quality code practices
End-to-end ML model development experience: data engineering, training, deployment, monitoring
Proficiency with contemporary ML frameworks (PyTorch, TensorFlow, scikit-learn)
Production experience with large language models and AI service APIs (OpenAI, Anthropic, Hugging Face)
Cross-domain ML capabilities spanning natural language processing, computer vision, and audio
Product-oriented mindset identifying ML opportunities that enhance user experience and business outcomes
Familiarity with MLOps tooling and cloud infrastructure (AWS or GCP)
Self-directed and effective in ambiguous situations
Technical Expertise We're Seeking
Core Capabilities:
Supervised/unsupervised learning, feature engineering, model evaluation, A/B testing
Neural architectures, transformers, convolutional networks, training optimization
RAG implementations, prompt engineering, vector databases, model fine-tuning, LangChain
Image classification, object detection, OCR, visual content analysis, image embeddings
Audio classification, automatic speech recognition, audio transcription
Semantic search, embedding models, vector similarity, multimodal retrieval
Model deployment, monitoring, experiment tracking (MLflow, Weights & Biases), data pipelines
Cloud ML services: AWS (SageMaker, Bedrock) or GCP (Vertex AI), scalable inference

 
Why is This a Great Opportunity
Revenue-generating company addressing genuine market needs
Define ML strategy and infrastructure during growth phase
High autonomy with meaningful work, no trivial tasks
Rapid deployment cycle, models reach production in days
Engineering-first culture
Influence both product and company direction
Tackle varied ML challenges across video, language, and audio domains
Direct input on product strategy, your ML concepts become shipped features