{"id":20162,"url":"https://alion.io/job/dailypay-senior-machine-learning-engineer","title":"Senior Software Engineer, ML Platform","company":{"id":5728,"name":"DailyPay","domain":"dailypay.com","url":"https://alion.io/company/dailypay","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"B","score":81,"open_postings":4,"ghost_share":0,"stale_share":0.75,"repost_share":0,"time_to_fill_p50_days":27,"computed_at":"2026-10-03T05:45:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"posting_text","remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":["United States"],"countries":["US"],"hiring_countries":["US"],"hiring_countries_total":1,"salary":{"min":170000,"max":190000,"currency":"USD","period":"year","gross":null,"usd_annual":190000},"salary_estimate":null,"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":true,"technologies":[{"name":"Amazon EC2","optional":false},{"name":"Amazon ECS","optional":false},{"name":"Amazon S3","optional":false},{"name":"Amazon SageMaker","optional":false},{"name":"AWS","optional":false},{"name":"AWS Lambda","optional":false},{"name":"Azure","optional":false},{"name":"Azure AKS","optional":false},{"name":"CI/CD","optional":false},{"name":"CloudFormation","optional":false},{"name":"Datadog","optional":false},{"name":"dbt","optional":false},{"name":"Docker","optional":false},{"name":"Feature Store","optional":false},{"name":"GCP","optional":false},{"name":"GitHub Actions","optional":false},{"name":"Google Cloud Run","optional":false},{"name":"Grafana","optional":false},{"name":"IAM","optional":false},{"name":"Kubernetes","optional":false},{"name":"Machine Learning","optional":false},{"name":"Prometheus","optional":false},{"name":"Python","optional":false},{"name":"Snowflake","optional":false},{"name":"SQL","optional":false},{"name":"Terraform","optional":false},{"name":"Vertex AI","optional":false},{"name":"A/B Testing","optional":true},{"name":"Apache Kafka","optional":true},{"name":"PyTorch","optional":true},{"name":"Scikit-learn","optional":true},{"name":"XGBoost","optional":true}],"status":"live","first_seen_at":"2026-07-06T14:10:07Z","employer_posted_date":"2026-09-24","last_verified_at":"2026-10-04T01:36:43Z","board_verified":true,"closed_at":null,"days_open":89,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":89},"description":"About Us:\nDailyPay is the leader in On-Demand Pay, helping employers modernize how people get their pay. DailyPay serves more than 1,900 employers and over 6 million employees, including many of the world's most recognized brands. By providing real-time access to earned pay and financial wellness tools, DailyPay helps employees manage their finances and helps employers attract and retain talent. DailyPay is helping define the future of pay, where money moves at the speed of work.. Learn more at DailyPay's Press Center.\nThe Role\nWe are seeking a Senior Software Engineer to build DailyPay's ML platform from the ground up. You will design and build the infrastructure that every machine learning model at DailyPay runs on: feature engineering platform, model training and deployment, serving infrastructure, and the monitoring that keeps it all reliable in production.\nThis is a software engineering role. You will build the platform that data scientists use to ship models, not build the models themselves. You own the infrastructure that makes their work reproducible, testable, observable, and production-safe at scale.\nYou will work closely with data scientists, engineers, and product stakeholders to deliver high-quality ML solutions that directly impact DailyPay's core products. You are expected to operate with significant autonomy: defining work, identifying dependencies, and raising the bar for the team around you.\nHow You Will Make an Impact\nPlatform Ownership: Help architect and build DailyPay's unified ML platform - a unified system for model development, deployment, and monitoring that serves as the backbone for every AI and ML capability at the company.\n\nSystems Design & Delivery: Design and build scalable, reliable services and pipelines covering feature generation, model training, deployment, and inference. Own end-to-end delivery with minimal oversight.\n\nSelf-Service Infrastructure: Build the tooling and guardrails that let data scientists define, test, and ship features and models independently, without needing an engineer in the loop and without bypassing validation, lineage, or rollback safeguards.\n\nCloud Infrastructure: Manage and optimize AWS infrastructure for machine learning workloads, balancing cost-effectiveness, security, and availability.\n\nCI/CD Pipeline Development: Build and maintain robust CI/CD pipelines for continuous integration and deployment of ML models and related infrastructure.\n\nMonitoring & Observability: Design monitoring and alerting systems for ML infrastructure and models using tools like Datadog. Proactively identify and resolve issues before they impact production.\n\nTechnical Leadership: Lead design discussions, contribute to architectural decisions, and establish team norms for how ML systems are built, tested, and maintained. Help identify and remove blockers.\n\nMentorship: Mentor junior engineers. Share domain knowledge and help build genuine technical depth on the team.\n\nSecurity & Compliance: Approach all engineering work with a security lens. Actively look for vulnerabilities in code and during peer reviews. Ensure ML pipelines handle sensitive data in accordance with company policy.\n\nWhat You Bring to the Team\n5+ years of professional software engineering experience building and operating production services\n\nStrong background in distributed systems, service-oriented architecture, and API design\n\nExperience across the full software lifecycle: design, testing, deployment, and on-call operations\n\nProficiency in Python, with a track record of writing production-quality, tested, maintainable code\n\nExperience with infrastructure-as-code (Terraform or CloudFormation), including module design and environment separation\n\nSolid CI/CD experience: GitHub Actions or equivalent; designing and operating deployment pipelines\n\nExperience with containerization and orchestration (Docker, and Kubernetes or ECS)\n\nExperience building or operating ML infrastructure: training pipelines, model serving, feature stores, or model registries\n\nStrong cloud platform proficiency: AWS preferred (SageMaker, Lambda, S3, EC2, IAM, ECS), or equivalent GCP (Vertex AI, Cloud Functions, GCS, Compute Engine, Cloud Run) or Azure (Azure ML, Functions, Blob Storage, VMs, AKS) experience\n\nExperience with monitoring and observability tooling (Datadog, Prometheus, or Grafana)\n\nStrong SQL skills and experience with data pipeline tooling (dbt, Glue, Snowflake)\n\nExcellent communication skills; comfortable working across data science, engineering, and product teams\n\nNice to Haves\nFamiliarity with ML frameworks (scikit-learn, XGBoost, PyTorch), enough to reason about what data scientists hand you\n\nKnowledge of event streaming platforms (Apache Kafka or equivalent)\n\nExperience with experimentation infrastructure and A/B testing systems\n\nExperience in fintech or other regulated industries\n\nContributions to open-source infrastructure, platform, or MLOps projects\n\nWhat We Offer:\nExceptional health, vision, and dental care\n\nOpportunity for equity ownership\n\nLife and AD&D, short- and long-term disability\n\nEmployee Assistance Program\n\nEmployee Resource Groups\n\nFun company outings and events\n\nUnlimited PTO\n\n401K with company match\n\nHigh-performing cultures aren't built in silos, they thrive on partnership. At DailyPay, we Commit Together to an inclusive, professional environment where multifaceted perspectives are our greatest competitive advantage. We recognize that our team members don’t live “single-issue lives,” and we lean into the wide-ranging backgrounds and life stages that sharpen our collective decision-making.\nIn our high-trust environment, we empower you to Challenge Norms. We’ve created a space where it is safe to ask difficult questions, disrupt the status quo, and share bold perspectives without fear of professional fallout. We believe that by checking our own assumptions and staying curious about the experiences of others, we arrive at better, more innovative results.\nWe provide the space for you to do your best work through peer advocacy and transparent career development. If you are looking for a culture that values intellectual honesty, celebrates the unique lived experiences of its people, and thrives on collective success, you’ll find it here.\nIf you require reasonable accommodation for any aspect of the recruitment process, please send a request to . All requests for accommodation will be addressed as confidentially as practicable.\nDailyPay is an equal opportunity employer. All qualified applicants will receive consideration without regard to race, color, religion or creed, alienage or citizenship status, political affiliation, marital or partnership status, age, national origin, ancestry, physical or mental disability, medical condition, veteran status, gender, gender identity, pregnancy, childbirth (or related medical conditions), sex, sexual orientation, sexual and other reproductive health decisions, genetic disorder, genetic predisposition, carrier status, military status, familial status, or domestic violence victim status and any other basis protected under federal, state, or local laws.","description_format":"text","description_chars":7153,"description_truncated":false,"requirements":{"experience_years_min":5,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":["401k plan","Equity","Unlimited PTO"],"hiring_locations":[{"name":"United States","iso":"US","kind":"country"},{"name":"New York","iso":null,"kind":"city"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Payment Processing & Gateways","Personal Finance Apps & Education"],"lifecycle":[{"event":"open","at":"2026-07-06T14:10:07Z"}],"visa":[{"country":"US","licensed_sponsor":true,"evidence":"H-1B filings in 12 months: 3","filings_12m":3,"filings_prev_12m":2,"green_card_filings_12m":0,"median_offered_wage_usd":147500,"route":null,"cap_exempt":false,"checked_at":"2026-10-03T21:08:04+00:00","sources":["US Department of Labor: LCA disclosure data (H-1B, H-1B1, E-3)"],"filings_for_role_12m":0}],"liveness":{"score":19,"band":"cold","label":"Long shot","p_open":1,"p_active":0.678,"p_room":0.28,"age_days":88,"expected_fill_days":27,"reasons":["conf:6","velocity","win:tail","crowd:"],"computed_at":"2026-10-03T05:45:00Z"},"pay":{"stated_usd_annual":190000,"is_top_pay":false},"html_url":"https://alion.io/job/dailypay-senior-machine-learning-engineer","json_url":"https://alion.io/job/dailypay-senior-machine-learning-engineer.json","meta":{"generated_at":"2026-10-04T01:56:57Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","usage":{"tier":"crawler","counted_by":"address","units_charged":1,"used_today":2637,"day_limit":5000,"remaining_today":2363,"minute_limit":60,"resets_at":"2026-10-05T00:00:00Z"}}}