Key Responsibilities:

• Build and deploy AI agents for high-volume, real-world task automation.
• Integrate human-in-the-loop feedback mechanisms for model reliability.
• Optimize infrastructure for model serving, real-time monitoring, and scaling.
• Design systems to interpret and reason across complex, document-rich data.
• Advance retrieval-augmented generation (RAG) capabilities.
• Run experiments to test model robustness, safety, and performance.
• Stay on the cutting edge of LLM research, reinforcement learning, and agentic systems.

Required Qualifications:

• MS or PhD in Computer Science, Machine Learning, or related discipline.
• 5+ years of hands-on experience in ML system development and deployment.
• Fluency in Python and ML libraries (PyTorch, TensorFlow, scikit-learn).
• Experience with cloud platforms (AWS, GCP) and scalable model deployment.
• Deep familiarity with LLM agent frameworks, RL techniques, and external data integrations.
• Strong grasp of search/retrieval tech (e.g., Elasticsearch, vector DBs, graph databases).

Preferred Experience:

• NLP, speech, computer vision, or other applied AI specialties.
• Experience fine-tuning proprietary models or working with custom data pipelines.
• Background in high-growth tech companies (required).
• Active LinkedIn profile (required).

Perks & Benefits:

• Competitive pay and equity package.
• Medical, dental, and vision coverage (options 100% covered).
• Learning and development stipend.
• Unlimited PTO and hybrid flexibility.
• Company retreats and team lunches at oƯice hubs.

Ideal For:

Engineers excited by building scalable ML infrastructure in tech-driven environments. You’ll thrive here if you enjoy turning leading-edge research into practical, enterprise-grade solutions with measurable impact.