Senior AI/ML Engineer - US Remote

I’m helping Truelogic find a top candidate to join their team full-time for the role of Senior AI/ML Engineer - US Remote.You'll build trustworthy AI/LLM solutions to deliver safe, personalized consumer health experiences.Compensation:USD 160K - 220K/yearLocation:Remote: United StatesMission of Truelogic:"To partner with organizations worldwide by providing elite LATAM talent and strategic technology solutions that accelerate digital growth, innovation, and long-term success."What makes you a strong candidate:You are an expert in Machine learning.You have the potential to develop in Large language model (LLM).English - Fully fluentResponsibilities and more: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:- 5+ 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:- 160,000 - 220,000 USD per year (Remote (United States)).