Staff Software Engineer, Machine Learning

Cleerly•Denver, CO

7d•Remote

About The Position

We are seeking an experienced Staff Machine Learning Engineer to architect, scale, and advance our machine learning platforms that bridge AI innovation and production in regulated healthcare. In this high-impact role, you will define and implement core platform capabilities, enabling scalable, secure, and compliant deployment of ML models that directly impact the care pathway for heart disease diagnosis and prognosis. You will tackle complex engineering challenges across end-to-end ML pipelines, ensuring reproducibility, efficiency, and compliance while driving the technical evolution of the platform.

Requirements

12+ years of experience (Bachelor’s; 8+ with Master’s; 5+ with PhD) designing, implementing, and optimizing AI and ML systems, ideally in regulated healthcare or clinical domains.
Deep technical expertise in ML pipelines, distributed model serving architectures, and production ML lifecycle management, with a track record of solving high-impact system challenges.
Proficiency in Python, Java, or similar, with extensive programming experience establishing reproducible ML workflows, coding standards, and software engineering best practices for AI/ML applications.
Proficiency with ML infrastructure and orchestration tools (Kubernetes, Helm, Airflow) and data platforms (Snowflake, PostgreSQL, Airbyte), and building scalable pipelines that support ML data processing and model workflows.
Advanced experience with AWS (including SageMaker and S3), ML infrastructure frameworks such as MLflow and Terraform, and exposure to platforms like Databricks, with a proven track record of implementing end-to-end ML systems and optimizing platform performance, scalability, and operational efficiency.
Proven ability to influence technical approaches and operational practices in AI/ML workflows, elevating system efficiency, reproducibility, and reliability.
Strong expertise in regulatory and compliance requirements for AI/ML (FDA, HIPAA), able to design systems that are inherently compliant, reproducible, and auditable.

Nice To Haves

(Preferred) The base salary range for this role varies by location and is aligned to market benchmarks. Candidates located in higher-cost labor markets , including California, Washington, New York, New Jersey, Connecticut, Massachusetts, and Washington, DC represent the middle to high end of the range, while candidates located in all other U.S. locations represent the low to middle end of the range. Final compensation is determined based on location, experience, skills, and internal equity. This role is eligible for a 15% target annual bonus , resulting in the following base salary and Total Target Compensation (TTC) ranges: Base Salary: $175,000 - $201,000 TTC: $200,000 - $231,000 Total Target Compensation (TTC): Total Cash Compensation (including base pay, variable pay, commission, bonuses, etc.) Additionally, stock options, paid benefits, and employee perks are part of your total rewards.

Responsibilities

Architect and develop scalable AI/ML platforms and end-to-end pipelines, covering data ingestion, preprocessing, model training, evaluation, deployment, monitoring, drift detection, and automated retraining, while ensuring reproducibility, compliance with FDA/HIPAA, and alignment with organizational and regulatory goals.
Optimize and operationalize production ML systems, including monitoring, drift detection, automated retraining, and workflow execution, to achieve high performance, reliability, scalability, and regulatory adherence.
Evolve the ML stack through integration and refinement of frameworks, libraries, and infrastructure, improving system efficiency, maintainability, and the ability to support clinical ML workflows.
Ensure operational readiness of ML pipelines and platforms, verifying data quality, throughput, reproducibility, and compliance across production workflows.
Drive improvements in processes, tooling, and collaboration to streamline the transition of ML models from research to production, enhancing efficiency, reproducibility, and compliance across the platform.