This role requires candidates who are currently authorized to work in the U.S. without sponsorship, and C2C arrangements are not accepted.

We’re seeking an experienced and innovative Senior NLP Engineer to join a newly established AI team. This role offers the opportunity to shape and build AI-driven solutions leveraging advanced Natural Language Processing (NLP) techniques, including LLM customization, prompt engineering, and real-time deployment into production environments.

As one of the first hires on the AI team, you'll collaborate closely with leadership and cross-functional stakeholders to address meaningful, real-world challenges—particularly in the realm of language understanding, automation, and applied AI.

If you're excited by the opportunity to work independently, make a tangible impact, and help architect foundational AI capabilities from the ground up, we’d love to connect.

Key Responsibilities

• LLM Customization & Prompt Engineering: Fine-tune and deploy large language models (e.g., Llama 3.2) for tasks like real-time transcription, summarization, and tailored AI functionality, with a focus on data privacy and secure infrastructure.

• AI-Powered Feature Development: Build core NLP features such as case timeline generation, co-pilot assistants, and sentiment analysis tools to improve operational efficiency and user experience.

• NLP Research & Development: Explore and implement modern NLP techniques (e.g., RAG, transformer models) to solve complex language tasks, including insight extraction and accurate summarization.

• System Prototyping & Deployment: Collaborate with engineering to prototype, test, and deploy scalable, production-grade AI systems with emphasis on usability and performance.

• Performance Monitoring & Feedback Loops: Create robust mechanisms for evaluating LLM output quality and incorporating user feedback, ensuring models adhere to accuracy and ethical standards.

• Cross-Functional Collaboration: Partner with stakeholders such as product managers, domain experts, and data teams to understand use cases, define requirements, and integrate NLP capabilities into existing workflows.

• Mentorship & Best Practices: Support the growth of the AI function by sharing knowledge, contributing to best practices, and mentoring junior team members as needed.

Key Initiatives

• Timeline and event summarization

• Sentiment and tone analysis from user interactions

• Retrieval-Augmented Generation (RAG) systems for assistant tools

• Real-time NLP for data enrichment and contextual awareness

Required Qualifications

• Education: Master’s or Ph.D. in Computer Science, Artificial Intelligence, Computational Linguistics, or a related discipline.

• Experience:

  • 5+ years of hands-on experience in NLP and machine learning, with a focus on deploying LLM-powered solutions.

  • Demonstrated success in developing, fine-tuning, and operationalizing NLP applications in production environments.

• Technical Skills:

  • Proficiency in fine-tuning LLMs (e.g., Llama, Qwen, Mistral) and designing RAG pipelines.

  • Strong programming skills in Python; experience with frameworks like Hugging Face Transformers, MLX-LM, SpaCy, TensorFlow, and PyTorch.

  • Practical knowledge in prompt engineering, transfer learning, and building real-time language understanding systems.

  • Awareness of data privacy and compliance (e.g., HIPAA, GDPR) in sensitive data applications.

• Mindset:

  • Comfortable working in fast-paced, ambiguous environments with minimal oversight.

  • A builder’s mindset—excited to create tools and infrastructure from scratch and solve open-ended problems.

Preferred Qualifications

• Experience with AI solutions in healthcare, mental health, or similar domains.

• Familiarity with NLP data pipelines (ETL, cleaning, preprocessing).

• Knowledge of AI infrastructure tools such as Docker, Kubernetes, and cloud services (AWS, GCP).

• Previous work in startup or early-stage teams with high autonomy.

What You’ll Get

• High Impact Work: Be a critical part of building AI capabilities that solve meaningful problems.

• Career Growth: Work alongside AI leadership and influence the direction of AI strategy and implementation.

• Collaborative Culture: Join a mission-driven team where ideas are welcomed and initiative is encouraged.

• Competitive Compensation: Including salary, potential equity, and benefits (details customized by employer).