JobHabor

Senior Applied ML Engineer

Upstart

Location
United States · Canada
Workplace
Remote
Employment
Full Time
Salary
USD 167,700–231,800/yr
Apply on the employer’s site

Posted 1mo ago

The employer’s full description could not be read from their board. This is a summary of the posting — follow the apply link for the original.

Responsibilities

  • Design and build user-facing ML features that harness LLMs and generative AI
  • Partner with product, design, and ML research to prototype and deliver ML-powered experiences
  • Own the technical architecture and implementation strategy for applied ML systems
  • Build scalable services and APIs that bring model outputs to users
  • Collaborate across platform, infra, and legal/compliance teams to ensure ML deployments meet standards
  • Establish and evangelize best practices for prompt design, model evaluation, and experimentation

Requirements

  • 4+ years of software engineering experience
  • 2+ years working directly on ML-driven products or intelligent systems
  • Proven ability to lead complex initiatives across engineering, product, and research stakeholders
  • Strong backend development skills (e.g., Python with FastAPI or Flask)
  • Experience with cloud-native tooling (e.g., Kubernetes, Docker, Terraform)
  • Experience integrating LLMs or ML models into production systems, including APIs and user-facing applications
  • Excellent communication skills and a collaborative, product-minded approach
  • Ability to think rigorously about system design, latency tradeoffs, and user impact

Preferred

  • Experience shipping GenAI or LLM-powered features using frameworks like LangChain, LlamaIndex, or OpenAI APIs
  • Familiarity with retrieval-augmented generation (RAG), vector search (e.g., FAISS, Pinecone), and real-time inference patterns
  • Proficiency in full-stack development, including front-end work with React or similar frameworks
  • Strong intuition for prompt engineering, model testing, and evaluation methodologies
  • Experience navigating complex requirements around explainability, user trust, or compliance in ML applications
  • Track record of influencing architecture or product direction at a team or org level

Skills

  • Python
  • FastAPI
  • Flask
  • Kubernetes
  • Docker
  • Terraform
  • LangChain
  • LlamaIndex
  • OpenAI APIs
  • FAISS
  • Pinecone
  • React

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