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Senior Machine Learning Engineer
Senior Machine Learning EngineerHenpen Corporation • San Francisco, CA, United States
Senior Machine Learning Engineer

Senior Machine Learning Engineer

Henpen Corporation • San Francisco, CA, United States
1 day ago
Job type
  • Full-time
Job description

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

  • 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
  • #J-18808-Ljbffr

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