Staff Machine Learning Engineer
Ethos Life
156,000 - 275,000 / year
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Description
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.
Benefits
- stock options
- life insurance
About this role
Ethos is seeking a Staff Machine Learning Engineer to build and maintain the infrastructure that moves models from research into production across both product and marketing. You'll own deployment pipelines, feature stores, monitoring systems, and retraining workflows that power consumer-facing experiences like coverage recommendations and agent assignment, as well as marketing models for attribution and media mix optimization. This role bridges data science and engineering—you'll work closely with data scientists and product teams to eliminate bottlenecks and ensure models perform reliably at scale.
The ideal candidate has seven or more years in ML engineering or MLOps, with hands-on experience deploying real-time models to production systems like AWS SageMaker. You should be a strong Python engineer comfortable with SQL, Docker, orchestration tools like Airflow, and cloud infrastructure. Beyond technical depth, you need to understand ML methods well enough to debug models independently, know the tradeoffs between pragmatic and over-engineered solutions, and communicate fluidly across data science, engineering, and non-technical stakeholders. Experience with marketing measurement, dbt, or regulated industries like insurtech is valued but not required.
This is a remote, full-time role with a base salary range of $156,000 to $275,000, plus bonus and equity.
Pay for this role
$156,000 to $275,000 per year
That is the employer's posted range, and where you land in it is usually decided in one conversation. Before that conversation, run the numbers: analyze an offer for this role.
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