Senior Staff ML Engineer, ASAPP

• 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
• If you're driven by continuous learning, rapid pivots, and the challenges of building in a high-growth startup, we’d love to talk
• 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
• Knowledge of additional languages such as SQL, Go, JavaScript, or TypeScript
• Graduate degree (MS or PhD) in Computer Science, Machine Learning, or related field

Desirable

• 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
• 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