Staff Machine Learning Engineer

• Own deployment and production lifecycle of models powering product experiences (coverage recommenders, user-to-channel routing, agent assignment)
• Build and maintain feature pipelines for real-time inference on consumer-facing surfaces
• Implement automated monitoring and alerting for data drift, feature quality, and model degradation
• Develop CI/CD pipelines and retraining infrastructure for data scientists
• Deploy and operationalize marketing models (attribution, incrementality, media mix)
• Build reliable data pipelines for marketing models and measurement workflows
• Own model health monitoring and dependency management across marketing model inventory
• Mentor team and raise ML engineering standards through code review and documentation

About Ethos

Ethos (Nasdaq: $LIFE) is a leading life insurance technology company on a mission to protect families by democratizing access to life insurance and empowering agents at scale. With its robust three-sided technology platform, Ethos is transforming the life insurance experience for consumers, agents, and carriers alike. Ethos offers instant, accessible products and a seamless online process that requires no medical exams and just a few health questions; it eliminates traditional barriers, making it easier than ever for everyone to protect their families. Ethos is redefining how life insurance is bought, sold, and underwritten.

About the role

We're looking for a Staff Machine Learning Engineer to own the infrastructure that gets our models from development into product and marketing workflows. You'll partner with data scientists and product engineers to build deployment pipelines, feature stores, monitoring systems, and retraining infrastructure across both product and marketing use cases. The right candidate is a data scientist that can also build scalable data architecture: you care about how a model will run at scale, not just how it performs on AUC.

Duties and Responsibilities:

Product ML Engineering

• Own the deployment and production lifecycle of models powering product experiences: coverage recommenders, user-to-channel routing, and agent assignment logic

• Build and maintain feature pipelines that feed real-time inference for consumer-facing product surfaces

• Implement automated monitoring and alerting for data drift, feature quality issues, and model degradation

• Develop CI/CD pipelines and retraining infrastructure that let data scientists ship iterative improvements without engineering bottlenecks

Marketing ML Engineering

• Deploy and operationalize marketing models: attribution, incrementality, and media mix and automate feedback loops to ad platforms

• Build reliable data pipelines that deliver clean, timely inputs to marketing models and measurement workflows

• Own model health monitoring and dependency management across the marketing model inventory

Qualifications and Skills:

• 7+ years in machine learning engineering, MLOps, or software engineering with a strong ML focus

• Experience deploying and serving real-time production ML models integrated with consumer-facing products (AWS SageMaker or equivalent)

• Strong Python engineering skills: software design, testing, CI/CD, package development, and SQL

• Experience building and maintaining feature pipelines, feature stores, and workflow orchestration (e.g., Airflow)

• Familiarity with Docker, container orchestration, and cloud infrastructure

• Strong communication skills; able to collaborate fluidly with data scientists, product engineers, and non-technical stakeholders

• Bias toward ownership; you see a gap in the infrastructure and close it

• Working knowledge of ML methods: supervised learning, Bayesian models, causal inference, enough to debug a model without a data scientist in the room

• Marketing measurement: attribution, media mix modeling, or ad platform integrations

• dbt or data warehouse tooling

• Experience in insurtech, fintech, or other regulated industries

• Gets in the weeds without losing the bigger picture

• Knows the tradeoffs between over-engineered infrastructure and pragmatic solutions, and picks the right one

• Bridges data science and engineering, fluent in both worlds and trusted by both teams

• Raises the team's ML engineering bar through code review, documentation, and mentorship

#LI-Remote #LI-TL1

The US national base salary range for this full-time position is $156,000 -$275,000. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position across all US locations. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training.

Please note that the compensation details listed in US role postings reflect the base salary only and do not include applicable bonus, equity, or benefits.

You can find further details of our US benefits at https://www.ethoslife.com/careers/ Don’t meet every single requirement? If you’re excited about this role but your past experience doesn’t align perfectly with every qualification in the job description, we encourage you to apply anyway. At Ethos we are dedicated to building a diverse, inclusive and authentic workplace.

We are an equal opportunity employer. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. Pursuant to the SF Fair Chance Ordinance, we will consider employment for qualified applicants with arrests and conviction records.

To learn more about what information we collect and how it may be used, please refer to our California Candidate Privacy Notice .

Recruitment Notice: Please be aware of recruitment scams. All legitimate communication from our team will only come from email addresses ending in @ethos.com or @getethos.com.

We will never ask for payment, banking details, or sensitive personal information during the hiring process. If you are contacted by someone claiming to represent us from a different email address, please treat it as fraudulent.

Sourced directly from the company's job board — apply on their site.