{"id":3762815,"url":"https://alion.io/job/ethos-staff-machine-learning-engineer","title":"Staff Machine Learning Engineer","company":{"id":6048,"name":"Ethos","domain":"ethos.com","url":"https://alion.io/company/ethos","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"B","score":80,"open_postings":16,"ghost_share":0,"stale_share":0.813,"repost_share":0,"time_to_fill_p50_days":55,"computed_at":"2026-11-06T05:58:05Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"staff","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":[],"countries":[],"hiring_countries":["US"],"hiring_countries_total":1,"salary":{"min":156000,"max":275000,"currency":"USD","period":"year","gross":null,"usd_annual":275000},"salary_estimate":null,"experience_years_min":7,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon SageMaker","optional":false},{"name":"AWS","optional":false},{"name":"CI/CD","optional":false},{"name":"dbt","optional":false},{"name":"Docker","optional":false},{"name":"Feature Store","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"SQL","optional":false}],"status":"live","first_seen_at":"2026-11-04T18:05:46Z","employer_posted_date":"2026-11-04","last_verified_at":"2026-11-06T23:36:51Z","board_verified":true,"closed_at":null,"days_open":2,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":2},"description":"About Ethos\nEthos (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.\nAbout the role\nWe'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.\nDuties and Responsibilities:\nProduct ML Engineering\nOwn the deployment and production lifecycle of models powering product experiences: coverage recommenders, user-to-channel routing, and agent assignment logic\nBuild and maintain feature pipelines that feed real-time inference for consumer-facing product surfaces\nImplement automated monitoring and alerting for data drift, feature quality issues, and model degradation\nDevelop CI/CD pipelines and retraining infrastructure that let data scientists ship iterative improvements without engineering bottlenecks\nMarketing ML Engineering\nDeploy and operationalize marketing models: attribution, incrementality, and media mix and automate feedback loops to ad platforms\nBuild reliable data pipelines that deliver clean, timely inputs to marketing models and measurement workflows\nOwn model health monitoring and dependency management across the marketing model inventory\nQualifications and Skills:\n7+ years in machine learning engineering, MLOps, or software engineering with a strong ML focus\nExperience deploying and serving real-time production ML models integrated with consumer-facing products (AWS SageMaker or equivalent)\nStrong Python engineering skills: software design, testing, CI/CD, package development, and SQL\nExperience building and maintaining feature pipelines, feature stores, and workflow orchestration (e.g., Airflow)\nFamiliarity with Docker, container orchestration, and cloud infrastructure\nStrong communication skills; able to collaborate fluidly with data scientists, product engineers, and non-technical stakeholders\nBias toward ownership; you see a gap in the infrastructure and close it\nWorking knowledge of ML methods: supervised learning, Bayesian models, causal inference, enough to debug a model without a data scientist in the room\nMarketing measurement: attribution, media mix modeling, or ad platform integrations\ndbt or data warehouse tooling\nExperience in insurtech, fintech, or other regulated industries\nGets in the weeds without losing the bigger picture\nKnows the tradeoffs between over-engineered infrastructure and pragmatic solutions, and picks the right one\nBridges data science and engineering, fluent in both worlds and trusted by both teams\nRaises the team's ML engineering bar through code review, documentation, and mentorship\n#LI-Remote #LI-TL1\n\nThe 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. \nPlease note that the compensation details listed in US role postings reflect the base salary only and do not include applicable bonus, equity, or benefits. \nYou can find further details of our US benefits at https://www.ethoslife.com/careers/\nDon’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.\nWe 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.\nTo learn more about what information we collect and how it may be used, please refer to our California Candidate Privacy Notice.\nRecruitment 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.\n We will never ask for payment, banking details, or sensitive personal information during the hiring process. 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