United States onlyAt ASAPP, our mission is simple: deliver the best AI-powered customer experience—faster than anyone else. To achieve that, we’re guided by principles that shape how we think, build, and execute. We value customer obsession, purposeful speed, ownership, and a relentless focus on outcomes. We work in tight, skilled teams, prioritize clarity over complexity, and continuously evolve through curiosity, data, and craftsmanship.
We’re seeking technologists and problem solvers who thrive in fast-paced environments, love collaborating with great talent, and approach every day like it’s Day 1. We're a globally diverse team with hubs in New York City, Mountain View, Latin America, and India—embracing both hybrid and remote work to bring the best minds together, wherever they are. If you're driven by continuous learning, rapid pivots, and the challenges of building in a high-growth startup, we’d love to talk. This is more than a job—it’s a journey.
We're actively looking for a highly experienced Senior Staff AI Engineer to join our team. You will play a pivotal role in designing, building, and deploying cutting-edge AI systems that power mission-critical enterprise applications. This role is ideal for an individual who thrives in ambiguity, is deeply technical, and has a strong product sense paired with deep expertise in foundational models and enterprise AI systems.
We're looking for someone with a passion for solving hard, real-world problems using modern machine learning techniques, especially large language models, RAG systems, and prompt engineering. You will lead end-to-end AI initiatives, from quick prototyping through to robust production deployments, while collaborating across engineering, research, and product teams.
What you'll do
- Drive the design and implementation of scalable ML/AI systems with a focus on large language models, vector databases, and retrieval-based architectures
- Leverage foundation models from major providers (OpenAI, AWS Bedrock, Anthropic, etc.) for both prototyping and production use
- Fine-tune, adapt, and evaluate LLMs for domain-specific enterprise applications
- Develop robust infrastructure for experimentation, training, deployment, and monitoring of AI models in production environments
- Optimize model performance and inference workflows for latency, cost, and accuracy
- Provide hands-on leadership and mentorship to other engineers, helping them grow technically and operationally
- Translate research ideas and early-stage prototypes into enterprise-grade ML systems
- Actively stay abreast of advancements in AI, open-source models, and MLOps best practices
- Establish and improve internal processes for experimentation, evaluation, and deployment of ML models
- Collaborate with cross-functional stakeholders to define AI roadmap and influence product direction
Required Qualifications
- 8+ years of professional experience in Machine Learning, with 3+ years focused on LLMs or NLP at scale
- Proficiency on Python and ML frameworks like PyTorch or TensorFlow
- Proven experience working with foundational models (OpenAI, Bedrock, Anthropic, HuggingFace)
- Hands-on experience with fine-tuning LLMs
- Strong understanding of RAG pipelines, prompt engineering, and vector search
- Experience building AI systems using vector databases
- Deep experience with Python and cloud services. AWS is required; experience with GCP, Azure, or Snowflake is a plus
- Experience deploying and scaling AI systems using Docker, Kubernetes, and CI/CD practices
- Demonstrated ability to move fast: prototype, test, and iterate quickly
- Strong written and verbal communication skills; ability to communicate complex technical ideas to both technical and non-technical audiences
- Comfortable with ambiguity and proactive in identifying and solving high-impact problems
- Highly organized, with the ability to manage multiple priorities and projects in parallel
Nice to Have
- Prior experience in MLOps, experimentation platforms, or model evaluation pipelines
- Contributions to open-source AI/ML tools
- Knowledge of additional languages such as SQL, Go, JavaScript, or TypeScript
- Graduate degree (MS or PhD) in Computer Science, Machine Learning, or related field
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