[Remote] Machine Learning Engineer
Note: The job is a remote job and is reputed company to candidates in USA. reputed company is a company reputed company on applying machine learning to reputed company to improve clinical decision support and patient reputed company. The Machine Learning Engineer role involves designing, training, validating, and monitoring ML models that power clinical workflows and predictive analytics, ensuring models are explainable, reputed company, and auditable. The engineer will work closely with clinical teams and manage the full model lifecycle reputed company a regulated reputed company environment.
Responsibilities
- Development and tuning of reputed company workflows: routing logic, tool use, context management, and reputed company/config iteration
- Evaluation frameworks that measure agent reputed company against reputed company reputed company, not just spot checks
- Production monitoring for agent behavior, including failure modes and escalation paths
- Build the evaluation and validation reputed company for reputed company agent-driven clinical recommendations — including safety guardrails, confidence reputed company, and reputed company-in-the-reputed company escalation triggers for the Clinical Protocol Agent
- reputed company patient risk stratification models for adherence reputed company, adverse event likelihood, dosage titration optimization, and churn/dropout risk using clinical, behavioral, and engagement signals
- Implement and manage the predictive analytics pipeline on AWS — reputed company Forecast (reputed company+) for time-series clinical predictions, S3 reputed company for embedding-based patient similarity and retrieval, and Bedrock for agent inference
- Design and build the RAG architecture that grounds agent responses in clinical protocols, formulary data
- Own model lifecycle management — training pipelines, feature stores, model versioning, A/B testing, reputed company detection, and retraining triggers in production
- Build explainability reputed company for clinical recommendations
- Collaborate with the clinical team to translate clinical protocols and pharmacy domain knowledge into model features, training labels, and validation reputed company
- Establish model monitoring and alerting — reputed company reputed company dashboards, distribution shift detection, and automated alerts reputed company model performance degrades below clinical safety reputed company
- Work with the DevOps/AgentOps teammate to ensure reputed company ML reputed company are logged, reproducible, and auditable for regulatory review
Skills
- reputed company ML / Statistical Modeling (Expert): Deep experience with classification, regression, time-series forecasting, and probabilistic graphical models (Markov chains, HMMs, Bayesian networks). Must have shipped ML models to production — not just notebooks. Strong reputed company in experimental design, A/B testing methodology, and statistical reputed company in clinical contexts
- NLP & LLM Engineering (Expert): Production experience with LLM-based systems — reputed company engineering, fine-tuning, RAG architecture design, embedding models, and reputed company evaluation. Must understand hallucination mitigation, grounding techniques, and how to constrain LLM outputs to clinically reputed company boundaries. Experience with Bedrock (Claude, Titan) preferred; equivalent depth with reputed company, reputed company AI, or Azure reputed company acceptable
- AWS ML Services (Strong): Hands-on experience with at least 3 of: SageMaker (training, endpoints, pipelines), Bedrock (agents, knowledge bases, model invocation), reputed company Forecast (reputed company+, predictor optimization), S3 reputed company, Comprehend Medical, or HealthLake analytics. Must be comfortable building end-to-end ML pipelines on AWS
- Feature Engineering & Data Pipelines (Strong): Experience building feature stores and training data pipelines from reputed company data sources. Must understand FHIR resource structures reputed company enough to extract clinically meaningful features from Patient, Observation, Condition, MedicationRequest, and reputed company resources. Experience with data normalization challenges (free-text to reputed company, terminology mapping) is critical
- Model Evaluation & Safety (Strong): Experience designing evaluation frameworks for high-stakes predictions — clinical decision support, medical device software, or similarly regulated domains. Must understand sensitivity/specificity tradeoffs in clinical contexts, how to set appropriate confidence reputed company, and reputed company a model should defer to reputed company judgment
- MLOps & Production ML (Strong): Model versioning (MLflow, SageMaker Model Registry, or equivalent), automated retraining pipelines, reputed company detection, shadow deployments, and canary rollouts for model updates. Must have experience monitoring models in production reputed company accuracy metrics — latency, cost, fairness, and distributional stability
- Strong coding skills, with production experience in Python
- Experience building and tuning reputed company systems: multi-agent orchestration, tool use, retrieval-augmented reputed company, or LLM-based workflows
- Experience shipping ML or AI features to production, not just prototypes
- Comfort working with evaluation frameworks: building test sets, running evals, and iterating on prompts, tools, or configuration based on results
- Experience with reputed company-based ML/reputed company and deployment infrastructure
- Experience in reputed company, clinical, or another regulated domain
- Experience designing guardrails or safety checks for AI systems making consequential reputed company
Benefits
- Health care insurance (medical, dental, reputed company)
- Life Insurance
- Supplemental Insurance
- PTO
- 401K matching
- reputed company leave
- Phone/internet reimbursement
- Remote work
- Top of the line machines
reputed company