Senior Applied ML Engineer building and shipping user-facing LLM and generative AI features. Own architecture for production ML systems, partner cross-functionally with product/research teams, and establish best practices for prompt engineering and model evaluation. Requires 4+ years software engineering with 2+ years on ML products, strong backend skills.
159k – 232k/yr
Remote4+ YOEML Engineering
About the role
How you’ll make an impact
Design and build user-facing ML features that harness LLMs and generative AI to unlock new product capabilities
Partner with product, design, and ML research to prototype and deliver high-impact, ML-powered experiences
Own the technical architecture and implementation strategy for applied ML systems - balancing latency, observability, and iteration speed
Build scalable services and APIs that bring model outputs to users in trustworthy and intuitive ways
Collaborate across platform, infra, and legal/compliance teams to ensure ML deployments meet standards for safety, fairness, and performance
Establish and evangelize best practices for prompt design, model evaluation, and experimentation across the org
Minimum qualifications
4+ years of software engineering experience, with 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), plus 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 when working with ML features
Preferred qualifications
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
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