Staff Level Machine Learning Engineer NLP Encinitas CA

Our client is a mission-driven technology company focused on transforming how individuals access mental health and substance use care. Based in Encinitas, CA, their platform leverages modern software and intelligent systems to streamline the connection between people in need and verified care providers. Partnering with school districts, government agencies, and organizations across the country, we offer a secure, data-informed coordination system that simplifies what can often be a complex and overwhelming process. As a Staff Machine Learning Engineer at Care Solace, you’d play a critical role in building and optimizing scalable models that improve provider matching, personalize care recommendations, and drive proactive engagement. Our goal is to harness the power of data and AI to reduce barriers, close care gaps, and ensure timely access to life-changing support—while upholding the highest standards of privacy, ethics, and inclusivity.

Required Skills & Experience

• 5+ years in a Machine Learning or similar role
• Bachelor’s degree in Computer Science, Engineering, Logistics, or related field (preferred)
• Proficient in integrating into monolithic Python/Django applications
• Strong knowledge of NLP and classical Deep Learning models
• Experience in fine-tuning traditional LLMs such as BERT, GPT, or LlaMa
• Familiarity with AWS cloud platform & Snowflake data warehouse

What You Will Be Doing Tech Breakdown

• 50% Python/Django integration
• 50% Gen AI model development and deployment for NLP, documentation, and automated notetaking

Daily Responsibilities

• Design, train, and fine-tune large language models (LLMs) and transformer-based architectures for core NLP tasks such as sentiment analysis, summarization, and context-aware note generation
• Experiment with foundation models (BERT, GPT, LlaMa) and adapt them to domain-specific use cases
• Implement prompt engineering and retrieval-augmented generation (RAG) techniques to enhance model outputs
• Own end-to-end deployment lifecycle of ML models, from prototype to production, using containerized services (e.g., Docker, Kubernetes) and model serving platforms (e.g., SageMaker, Triton, TorchServe)

The Offer

• Bonus
• Equity

Benefits

• Medical, Dental, and Vision Insurance
• Generous PTO

Applicants must be authorized to work in the US on a full-time basis now and in the future.

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