Senior Machine Learning Engineer

Clera; United States; On-site; USD 96,000 - 103,000; Full time

Job summary

Clera in United States seeks a Senior Machine Learning Engineer for a W2 contract with a healthcare technology organization. You will own end-to-end ML initiatives, from data prep to deployment, in a HIPAA-compliant enterprise environment.

Based in Palo Alto, CA, this on-site/hybrid role expects 8+ years of experience, strong Python/SQL, and hands-on MLOps across Azure/AWS/GCP with Databricks/Spark. You will lead pipelines, monitor models, and collaborate with clinical and engineering teams.

Qualifications

• 8+ years in software engineering and ML.
• Healthcare data handling under HIPAA competency.
• End-to-end ML lifecycle expertise.
• MLOps ownership and CI/CD implementation.
• Python and SQL proficiency.

Responsibilities

• Own end-to-end ML lifecycle from data prep to deployment.
• Design and implement production ML pipelines and feature stores.
• Develop REST APIs to expose ML services in cloud apps.
• Monitor models for drift and performance; optimize costs.
• Collaborate with Data/Software Engineers, PMs, and clinical teams.
• Ensure HIPAA, PHI, and security standards compliance.

Skills

Python

SQL

Machine Learning

MLOps

REST APIs

Git

Docker

Kubernetes

Communication skills

Tools

Databricks

Apache Spark

MLflow

Feature Store

Model Registry

CI/CD Pipelines

REST APIs

Job description

About the Role

We are an IT services consultancy placing a Senior Machine Learning Engineer with one of our end clients - a growing healthcare technology organization focused on AI and Data Science. This is a W2 contract engagement ideal for an experienced ML engineer who thrives in fast-paced environments, takes strong ownership of complex initiatives, and has a proven track record building production-grade ML solutions within the healthcare industry.

You will join the client's AI and Data Science team and lead end-to-end machine learning efforts spanning the full model lifecycle - from data preparation and feature engineering through to deployment, monitoring, and optimization - all within a HIPAA-compliant, enterprise-scale environment.

What You'll Do

• Take complete ownership of designing, developing, deploying, and maintaining enterprise-scale machine learning solutions.
• Build end-to-end ML pipelines covering data ingestion, feature engineering, model training, evaluation, deployment, monitoring, and retraining.
• Design scalable, production-ready ML systems with a focus on high availability, performance, and reliability.
• Develop and maintain MLOps pipelines including CI/CD, model registry, feature stores, automated deployment, monitoring, and rollback strategies.
• Monitor production models for model drift, data drift, accuracy degradation, and overall system health.
• Collaborate cross-functionally with Data Engineers, Software Engineers, Product Managers, Clinical teams, and business stakeholders.
• Develop REST APIs and integrate ML services into enterprise cloud applications.
• Optimize models for latency, scalability, reliability, and operational cost.
• Provide technical leadership on AI/ML initiatives across the team.
• Ensure compliance with HIPAA, PHI, PII, and enterprise security standards at all stages of development.

What We're Looking For

Required Qualifications

• 8+ years of professional software engineering and machine learning experience.
• Strong healthcare industry experience is mandatory ; demonstrated ability to work with sensitive healthcare data under HIPAA and related compliance frameworks.
• Full ML lifecycle expertise: data preprocessing, feature engineering, model development, calibration, deployment, monitoring, and maintenance.
• Hands-on MLOps experience with a strong ownership mindset.
• Proficiency in Python and SQL .
• Experience with distributed computing ( Apache Spark ) and Databricks in production environments.
• Practical experience with major cloud platforms: Azure, AWS, and/or GCP .
• API development and integration skills; strong debugging and performance-tuning capabilities.
• Excellent communication skills for collaborating with technical and non-technical stakeholders.

Required Technical Skills

• Python, SQL, Machine Learning, MLOps
• Databricks, Apache Spark, MLflow
• Feature Store, Model Registry
• CI/CD Pipelines, REST APIs
• Git, Docker; Kubernetes (preferred)
• Azure / AWS / GCP

Preferred / Nice-to-Have

• LLMs in production; prompt engineering, RAG, and GenAI experience.
• Scala proficiency.
• Managed ML platform experience: Azure ML, Amazon SageMaker, and/or Google Vertex AI.
• Experience designing HIPAA-compliant AI solutions and distributed ML architectures.

Compensation & Benefits

Rate: $70-75/hr on W2 (contract engagement).

Visa Sponsorship

Not available - US work authorization required.

Location

Based in Palo Alto, CA . On-site / hybrid arrangement at the client's location.