Senior Machine Learning Engineer

Careaccess; United States; Remote; USD 140,000 - 190,000; Full time

Benefits offered by this job

Remote work within United States

Competitive compensation: $140,000–${

Job summary

Careaccess is seeking a senior individual contributor to design, build, and operate machine learning products that support clinical research, including recruitment, enrollment forecasting, and study feasibility. You will translate modeling and AI techniques into scalable services with ownership of data pipelines, deployment, monitoring, and cross‑functional collaboration.

The role emphasizes production ML, cloud and MLOps practices, and working with data scientists, product teams, engineers, and

Qualifications

• 5+ years building and deploying machine learning solutions in production at scale.
• Strong Python skills and familiarity with ML libraries (pandas, scikit-learn, TensorFlow, PyTorch).
• Solid understanding of ML fundamentals, statistical modeling, evaluation, and feature engineering.
• Experience with cloud infrastructure (AWS), containerization (Docker), and MLOps including CI/CD and model monitoring.
• Ability to work with large datasets and write complex SQL queries.

Responsibilities

• Develop and deploy machine learning models and AI agents for clinical research workflows.
• Build end-to-end data and model pipelines using cloud services, workflow orchestration, and CI/CD practices.
• Integrate AI capabilities into products in partnership with data scientists, product managers, designers, engineers, and subject matter experts.
• Maintain model reliability through automated checks, production monitoring, and attention to data or model drift.
• Evaluate emerging approaches in areas such as language models, NLP, and probabilistic modeling, and bring suitable ideas into production.

Skills

Python programming

Pandas

Scikit-learn

TensorFlow

PyTorch

SQL

AWS

Docker

CI/CD

MLOps

Tools

Docker

AWS

CI/CD tooling

Kubernetes

Job description

Role overview

Build and operate machine learning products that support clinical research, including tools for recruitment, enrollment forecasting, and study feasibility. This senior individual contributor role turns modeling and AI techniques into scalable services, with responsibility across data pipelines, deployment, monitoring, and collaboration with product and domain teams.

Responsibilities

• Develop and deploy machine learning models and AI agents for clinical research workflows.
• Build end-to-end data and model pipelines using cloud services, workflow orchestration, and CI/CD practices.
• Integrate AI capabilities into products in partnership with data scientists, product managers, designers, engineers, and subject matter experts.
• Maintain model reliability through automated checks, production monitoring, and attention to data or model drift.
• Evaluate emerging approaches in areas such as language models, natural language processing, and probabilistic modeling, and bring suitable ideas into production.

Requirements

• At least 5 years building and deploying machine learning solutions in production at scale, including end-to-end pipelines from data ingestion to model serving.
• Strong Python skills and familiarity with machine learning libraries such as pandas, scikit-learn, TensorFlow, or PyTorch.
• Solid understanding of machine learning fundamentals, statistical modeling, model evaluation, and feature engineering.
• Experience with cloud infrastructure such as AWS, containerization such as Docker, and MLOps practices including CI/CD and model monitoring.
• Ability to work with large datasets and write complex SQL queries; experience with modern data-processing frameworks is expected.

Nice to have

• Experience with functional programming languages such as Clojure.
• Background in clinical research, healthcare, or other privacy-sensitive work.

Benefits and work setup

• Remote within the United States, with less than 10% travel and occasional planned trips.
• Expected annual pay range: $140,000–$190,000 for full-time team members.
• paid time off and holidays
• employer-paid medical, dental, and vision plan options
• HSA and FSA accounts
• short- and long-term disability coverage
• a 401(k) with company match