Staff AI/ML Engineer – US Remote (TX, CA, NY, FL Only)

• Remote, Hiring from TX, CA, NY, FL only What you get to do every day: - Build end-to-end ML/LLM features from problem definition to data, modeling, evaluation, deployment, and monitoring. - Develop LLM applications with retrieval and tool use (e.g., RAG, orchestration/workflows, structured extraction) to deliver trustworthy consumer health experiences. - Convert unstructured text (posts, comments, messages, search queries) into structured signals (topics, entities, intent, sentiment, safety flags) using a mix of classical NLP and modern LLMs. - Create and maintain data pipelines for training, inference, evaluation, and analytics (batch and/or streaming as needed). - Design evaluation systems that measure quality and safety: offline metrics, golden datasets, human review workflows, and online A/B testing alignment. - Implement production guardrails to reduce harm and misinformation risk (policy constraints, refusal behavior, citations/attribution when appropriate, red-teaming, monitoring, and incident response). - Set up monitoring for model and system health (latency, cost, drift, regressions, quality metrics). - Partner closely with the Product, Engineering, and Data teams and clinical/subject-matter experts to validate outputs and define what “correct” means for sensitive, health-adjacent use cases. - (Staff scope) Lead architecture and technical direction for applied AI across the organization; mentor engineers; establish best practices and reusable platforms. Examples of problems you might work on: - Personalized recommendations for communities, posts, resources, or next-best actions. - Safer content understanding: detection of misleading/high-risk health claims, escalation workflows. - Search and discovery improvements using embeddings, hybrid retrieval, and ranking. - Summarization and structuring of long threads into navigable insights (with safety constraints). - Member intent understanding from behavioral and text signals. Must-have qualifications: - 8+ years building and shipping production ML systems (or equivalent experience with demonstrable impact). - Strong Python skills and experience with ML/LLM libraries and tooling (e.g., Hugging Face ecosystem, LangChain/LangGraph, or equivalent). - Proven ability to design production-grade pipelines (training/inference/eval) and operate models in real systems (monitoring, rollbacks, incident handling). - Solid grounding in ML fundamentals (NLP, deep learning, statistical reasoning, evaluation). - Experience with MLOps best practices: versioning, reproducibility, CI/CD, model registry patterns, feature/data management, and infrastructure collaboration. - Experience working with large-scale data using Databricks/Spark or equivalent distributed processing. - Strong product and stakeholder instincts: you can translate ambiguous business needs into measurable ML outcomes. Nice-to-have qualifications: - Experience building RAG and retrieval systems: vector databases, hybrid search, ranking, recommendation, query understanding. - Experience in healthcare or regulated environments, including privacy-by-design, auditability, and safety reviews (HIPAA/PHI familiarity a plus). - Experience with streaming/clickstream data, experimentation platforms, and causal/measurement thinking. - Ability to prototype end-to-end experiences (e.g., Streamlit, Gradio, lightweight frontends). - Experience designing LLM safety systems: red-teaming, adversarial testing, prompt injection mitigation, output filtering, human-in-the-loop review. Some tools we use: - Databricks/Spark for distributed processing. - Redshift and BI tools (Looker/Tableau) for analytics. - Terraform for infrastructure-as-code; Airflow for orchestration; GitHub Actions for CI/CD. - AWS (including Bedrock) and a mix of private and open-weight models (including fine-tunes where appropriate). - Experimentation tooling (A/B testing) and internal UX analytics tools. - AI-assisted coding tools (e.g., Cursor, Copilot, Claude/OpenAI tooling). Working model: - The Engineering team operates in a remote-first environment. - This role is fully remote, with optional in-person collaboration at our San Francisco office. If you're a driven professional seeking to make a real difference in healthcare marketing at a fast-growing, innovative company, join Swoop and help us revolutionize how brands connect with patients and HCPs. The pay range for this role is: - 240,000 - 260,000 USD per year (Remote (United States)). Location: Remote - United States Skills required for this job: • A/B testing • AWS • Bedrock • Data pipelines • Deep learning • LangChain • Large language model (LLM) • Machine learning • Natural language processing (NLP) • Python • Software architecture • Streamlit • TensorFlow • Terraform • Vector databases Apply To this Job Company : Truelogic Salary : 240K–260K a year