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
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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 tot his job
Apply To this Job