This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Machine Learning Engineer based in United States.
This role offers the opportunity to design and scale production machine learning systems that create meaningful impact in healthcare.
You will work at the intersection of ML engineering, software development, and product innovation.
The position focuses on transforming advanced models into reliable, scalable, and measurable solutions.
You will collaborate with cross-functional teams including engineering, data science, product, and clinical stakeholders.
Your work will directly influence how machine learning improves user experiences, operational efficiency, and care outcomes.
This is an opportunity to shape ML infrastructure, establish best practices, and drive technical excellence in a high-impact environment.
This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Machine Learning Engineer based in United States.
This role offers the opportunity to design and scale production machine learning systems that create meaningful impact in healthcare.
You will work at the intersection of ML engineering, software development, and product innovation.
The position focuses on transforming advanced models into reliable, scalable, and measurable solutions.
You will collaborate with cross-functional teams including engineering, data science, product, and clinical stakeholders.
Your work will directly influence how machine learning improves user experiences, operational efficiency, and care outcomes.
This is an opportunity to shape ML infrastructure, establish best practices, and drive technical excellence in a high-impact environment.
Accountabilities:
- Design, deploy, and maintain production machine learning systems supporting both batch and real-time use cases.
- Build and improve ML lifecycle infrastructure, including training pipelines, inference workflows, deployment processes, monitoring, alerting, and automated retraining systems.
- Partner with engineering, product, data science, and domain experts to translate complex business challenges into effective ML solutions with measurable outcomes.
- Drive the transition of models from prototypes into robust production systems through strong architecture, documentation, testing, and operational practices.
- Develop reusable ML engineering patterns, tools, templates, and documentation to improve developer productivity and engineering quality.
- Create workflows for model evaluation, monitoring, performance optimization, and quality measurement.
- Build systems that support explainability, auditability, and safe integration of ML outputs into products and operational processes.
- Collaborate with data and application engineering teams to establish effective connections between data platforms, ML pipelines, and product applications.
- Make thoughtful technical decisions balancing performance, cost, scalability, complexity, and long-term maintainability.
- Provide technical mentorship and guidance to engineers while promoting strong engineering practices and operational excellence.
- Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Science, or a related field, or equivalent practical experience.
- 4+ years of experience developing and deploying machine learning systems in production environments.
- Proven experience managing the complete ML lifecycle, including training, evaluation, deployment, monitoring, and iteration.
- Experience designing or working with ML infrastructure, including training pipelines, inference systems, model registries, deployment workflows, and monitoring solutions.
- Strong programming skills in Python and experience with ML frameworks such as PyTorch, scikit-learn, or TensorFlow.
- Experience with cloud-based ML platforms and infrastructure, such as AWS SageMaker, Vertex AI, MLflow, or similar technologies.
- Strong SQL and data modeling skills, with experience handling complex and large-scale datasets.
- Ability to design reliable, maintainable, and observable systems while making effective technical tradeoffs.
- Product-oriented mindset with the ability to define success metrics, validate assumptions, and determine when machine learning is the right solution.
- Strong communication and collaboration skills across engineering, product, analytics, and domain teams.
- Experience in healthcare, clinical, claims, or other high-impact industries is a plus.
- Competitive annual salary range of $161,000 - $273,000, depending on location, skills, experience, and internal equity.
- Remote-first work culture with flexibility to work from home.
- Comprehensive medical, dental, and vision coverage options.
- Disability insurance and additional wellness-focused benefits.
- 401(k) savings plan through Fidelity.
- Paid Time Off (PTO) and discretionary time off policies.
- 12 weeks of fully paid parental leave.
- Family building and compassionate leave benefits, including fertility support and adoption/surrogacy assistance.
- Work-from-home reimbursement to support an effective remote work environment.
The Senior Machine Learning Engineer will lead the development and operation of production ML systems, ensuring models are reliable, scalable, observable, and aligned with business and user needs. The role requires strong technical ownership, collaboration across disciplines, and the ability to turn complex challenges into practical solutions.
Requirements:
The ideal candidate brings strong experience building production-grade machine learning systems, excellent engineering judgment, and the ability to collaborate effectively across technical and business teams.