AI Engineer, Pindrop

• You are a creative problem solver, excited about building products that add real value and make technology safer and more human
• You’re intrinsically motivated, resourceful, and thrive in a fast-paced, ambiguous, innovation-driven culture
• You have strong communication and collaboration skills; you’re enthusiastic about sharing complex concepts in simple terms and supporting colleagues across disciplines
• You are resilient in the face of challenges, ambiguity, and change; you adapt swiftly and maintain an optimistic approach
• You are driven by integrity, accountability, and the continual pursuit of excellence in both research and product delivery
• 7+ years of professional software or ML engineering experience
• 7+ years designing and developing modern, distributed microservices and enterprise-scale APIs
• 5+ years delivering cloud-native AI/ML applications on AWS and/or Google Cloud, including architecture, optimization, and cost management
• 5+ years implementing DevOps and CI/CD automation with Docker, Kubernetes, and infrastructure-as-code tools (Terraform, CDK, GitOps)
• 3+ years of hands-on experience designing, integrating, and deploying LLM-based systems (e.g., GPT, Claude, Gemini, Llama, Mistral)
• Strong experience with ML frameworks such as TensorFlow, PyTorch, Scikit-learn, and XGBoost
• Experience with LLM frameworks and orchestration tools, including LangChain, LlamaIndex, Langfuse, Dust, Google ADK, AWS Bedrock, and Vertex AI
• Solid understanding of retrieval-augmented generation (RAG) patterns, embeddings, vector search, and caching strategies
• Proficiency in Python for production-grade backend development and data pipeline engineering
• Familiarity with AI observability and evaluation (A/B testing, groundedness, hallucination detection, drift monitoring)
• Knowledge of Responsible AI frameworks and practices, including data governance, safety filtering, and compliance enforcement
• Strong communication and collaboration skills, with the ability to work across ML, infra, and product teams to deliver scalable solutions
• We want a diverse, global team, with a broad range of experience and perspectives. If this job sounds great, but you’re not sure if you qualify, apply anyway! We carefully consider every application and will either move forward with you, find another team that might be a better fit, keep in touch for future opportunities, or thank you for your time

Desirable

• Architect, develop, and deploy scalable ML and LLM-based systems — from model experimentation through production and continuous improvement
• Build and maintain modern microservices architectures and APIs that power AI-driven applications at enterprise scale
• Develop and automate MLOps and LLMOps pipelines for model training, versioning, evaluation, deployment, and monitoring using MLflow, Kubeflow, Vertex AI, or SageMaker
• Design and implement RAG and GraphRAG architectures, integrating structured and unstructured data using vector databases (FAISS, Pinecone, Weaviate, Milvus) and hybrid retrieval/reranking pipelines
• Leverage cloud AI platforms, including:
• AWS Bedrock (Converse API, Agents, Knowledge Bases, Guardrails)
• Google Cloud Vertex AI (Gemini API, Agent Builder, Vector Search, Search & Grounding, Model Garden)
• Integrate and productionize ML frameworks (TensorFlow, PyTorch, Scikit-learn, XGBoost) into robust, monitored, and scalable services
• Collaborate cross-functionally with data, infrastructure, and product teams to translate requirements into secure, efficient, and compliant AI solutions
• Implement observability and evaluation pipelines for AI systems, including tracing, latency tracking, hallucination analysis, and drift detection
• Champion Responsible AI, including safety guardrails, red-teaming, fairness/bias testing, and human-in-the-loop workflows
• Optimize AI workloads for performance and cost efficiency using techniques like model distillation, quantization, caching, batching, and inference streaming
• Define reference architectures, golden paths, and technical guardrails for reliable multi-cloud AI delivery
• Continuously explore emerging AI and ML technologies, evaluating new frameworks, APIs, and deployment methods
• Within 30 days, you’ll
• Be introduced to your team and colleagues, participate in onboarding, and assimilate into our culture through 1:1s and training
• Engage with product demos and learning resources to understand Pindrop’s AI-driven solutions
• Within 60 days, you’ll
• Start contributing to one or more research or product AI projects
• Participate in team technical reviews and join regular meetings to shape the AI roadmap
• Within 90 days, you’ll
• Gain intermediate familiarity with our products and research initiatives
• Begin delivering on AI engineering milestones, presenting learnings, and helping chart future innovation

• This position will be posted for 60 days after 10/14/2025