Associate Machine Learning Scientist, AI
Elevance Health
full-time
Posted on:
Location: Illinois, Ohio, Virginia • 🇺🇸 United States
Visit company websiteSalary
💰 $110,440 - $165,660 per year
Job Level
JuniorMid-Level
Tech Stack
CloudKafkaRaySparkSQL
About the role
- Develops and maintains infrastructure systems that connect internal data sets
- Creates new data collection frameworks for structured and unstructured data
- Assists in leading enterprise-scale AI initiatives by designing horizontal capabilities such as RAG, evaluations-as-a-service, prompt/version control, guardrails, feature, and vector stores
- Develops, analyzes, and models complex operational, clinical, and economic data; delivers end-to-end ML (XGBoost/LightGBM) and LLM systems with clear SLOs
- Builds scalable solutions including cloud lakehouse (Databricks/Spark, SQL), streaming (Kafka/Kinesis), and API-first approaches
- Oversees serving technologies (vLLM/Triton/KServe/Ray/SageMaker)
- Establishes and manages LLMOps/MLOps processes using MLflow, CI/CD, and IaC focusing on observability, drift detection, hallucination rates, and SLOs/SLAs
- Implements data leadership strategies including data contracts, quality SLAs, and FHIR-aware design for PHI/PII
- Develops Responsible AI frameworks including fairness/robustness evaluations, red-teaming, and model risk management ensuring audit readiness (HIPAA, SOC 2, HITRUST)
- Assists other Machine Learning Scientists with algorithm development and implementation
- Assists in determining cause and effect relations
Requirements
- Requires a Bachelor’s degree in a highly quantitative field (Computer Science, Machine Learning, Operational Research, Statistics, Mathematics, etc.) or equivalent degree
- 1 or more years of experience
- Any combination of education and experience in configuration management may be accepted
- Preferred: Master’s or PhD in a quantitative field and 2+ years of experience in building production ML/LLM systems
- This position is not eligible for current or future visa sponsorship
- Associates required to be in-office 1-2 days per week; candidates not within a reasonable commuting distance will not be considered unless an accommodation is granted
- Junior level Artificial Intelligence (AI) scientific and statistical methods
- Experience developing and maintaining infrastructure systems connecting internal data sets
- Experience creating data collection frameworks for structured and unstructured data
- Familiarity with RAG, evaluations-as-a-service, prompt/version control, guardrails, feature and vector stores
- Experience delivering end-to-end ML systems (XGBoost / LightGBM) and LLM systems with SLOs
- Experience with cloud lakehouse (Databricks / Spark, SQL), streaming (Kafka/Kinesis), and API-first approaches
- Experience with serving technologies (vLLM / Triton / KServe / Ray / SageMaker)
- Experience with LLMOps/MLOps tools like MLflow, CI/CD, and IaC
- Knowledge of observability, drift detection, hallucination rates, and maintaining SLOs/SLAs
- Knowledge of data contracts, quality SLAs, FHIR-aware design for PHI/PII
- Experience with embedding/vectorization strategies
- Experience implementing Responsible AI frameworks: fairness/robustness evaluations, red-teaming, model risk management
- Audit readiness knowledge (HIPAA, SOC 2, HITRUST)
- Ability to assist other Machine Learning Scientists and determine cause-and-effect relations