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.
