{"id":193358,"url":"https://alion.io/job/attain-machine-learning-engineer","title":"Senior Machine Learning Engineer","company":{"id":28882,"name":"Attain","domain":"attain.ai","url":"https://alion.io/company/attain-ai","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"A","score":95,"open_postings":7,"ghost_share":0,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":129,"computed_at":"2026-10-20T05:45:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":null,"work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Chicago, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":132000,"max_usd":252000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":1268},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"CI/CD","optional":false},{"name":"GCP","optional":false},{"name":"Grafana","optional":false},{"name":"Kubernetes","optional":false},{"name":"Machine Learning","optional":false},{"name":"Platform Engineering","optional":false},{"name":"Prometheus","optional":false},{"name":"Terraform","optional":false},{"name":"BigQuery","optional":true},{"name":"Claude Code","optional":true},{"name":"Cursor","optional":true},{"name":"Dask","optional":true},{"name":"Datadog","optional":true},{"name":"Docker","optional":true},{"name":"Go","optional":true},{"name":"Google BigQuery","optional":true},{"name":"Google Cloud Spanner","optional":true},{"name":"gRPC","optional":true},{"name":"Istio","optional":true},{"name":"Python","optional":true},{"name":"Ray","optional":true},{"name":"Rollup","optional":true},{"name":"Rust","optional":true},{"name":"Service Mesh","optional":true},{"name":"Spark","optional":true},{"name":"SQL","optional":true}],"status":"live","first_seen_at":"2026-09-02T14:58:28Z","employer_posted_date":"2026-09-17","last_verified_at":"2026-10-20T22:38:28Z","board_verified":true,"closed_at":null,"days_open":48,"trust":{"level":"ok","repost_count":0,"flags":[],"days_open":47},"description":"About Attain\nBuilt for consumers and companies, alike.\nKlover's engineering team powers one of the fastest-growing fintech platforms in the U.S., supporting over one million active users each month. Our systems process and move more than $1.5 billion annually, enabling real-time access to financial tools, rewards, and services that help people improve their day-to-day lives.\nAs part of this team, you'll help design, build, and scale the systems that underpin Klover's core products and platform. You'll work on high-impact, production-grade systems that prioritize reliability, security, and performance, and that integrate with a broad ecosystem of internal and external services. The work you do will directly shape how users interact with Klover's products, access their money, and experience transparent, low-fee financial services.\nKlover engineers collaborate closely with colleagues across backend, frontend, data science, and product teams to deliver scalable, high-quality solutions for a rapidly growing user base. You'll have the opportunity to work with modern technologies and architectures while helping define and evolve the next generation of inclusive, data-powered financial products-building systems and interfaces that emphasize reliability, privacy, and performance at scale.\nAbout the role\nAttain is seeking a Senior Machine Learning Engineer to own our production ML systems and build out the MLOps platform infrastructure that powers our suite of B2C financial services. This role will be highly hands-on and infrastructure-first, focused on designing, building, and operating the pipelines, platforms, and tooling that take models from experiment to reliable production service across our app portfolio-and on keeping those systems healthy, performant, and cost-effective once they're live.\nYou will work on the systems and infrastructure behind our high-impact predictive models, including the pipelines, feature infrastructure, model-serving, CI/CD, and observability that keep them reproducible, automated, monitored, and fast in production. Day to day, this means building the platform and automation that let us move fast without sacrificing performance-streamlining retraining and rollouts, tuning systems for speed and efficiency, and building the metrics and alerting that give us confidence to ship-while enabling data scientists to deploy and iterate on models quickly and safely. The ideal candidate combines strong software and platform engineering fundamentals with practical MLOps experience building and operating production ML systems from scratch, and treats modern AI tooling as a first-class part of how the work gets done-directing coding agents to write, test, and ship infrastructure code, with the judgment to know when to verify their work.\nAttain Office Hybrid Schedule:\nChicago, IL: 4 days in-office; 1 day remote\nWhat a typical week might look like\nBuild, deploy, and operate the production ML systems at the core of our EWA product, with a focus on reliability, performance, and fast, high-quality execution\nBuild and improve the pipelines and serving infrastructure behind our predictive models across consumer decisioning, fraud, churn, transaction intelligence, and other business-critical use cases\nOwn the production side of the model lifecycle: feature pipelines, deployment, CI/CD, monitoring, and automated retraining\nBuild and maintain reusable modeling pipelines, feature engineering systems, model-serving infrastructure, and production-quality code, deployed via Terraform and CI/CD into our GCP + Kubernetes environment\nInstrument models and pipelines with monitoring, alerting, and automated retraining-defining the metrics and dashboards (e.g., Prometheus/Grafana) that surface drift and degradation and give us confidence to ship\nDirect AI coding agents as a force multiplier to write, test, and ship infrastructure and pipeline code-and apply strong judgment about when to trust their output and when to verify it yourself\nAutomate manual, repetitive steps in the ML lifecycle so the team can move faster without sacrificing reliability\nPartner with data scientists to give them fast, safe paths to deploy, iterate on, and retrain models in production\nCollaborate with analysts, platform engineers, product managers, and business stakeholders to deliver ML systems with quality, efficiency, and precision\nIdentify new areas where platform improvements, automation, and MLOps tooling can improve product velocity and business outcomes\nPreferred Qualifications \n5+ years of direct experience as a Machine Learning Engineer, ML Platform Engineer, MLOps Engineer, Applied Scientist or similar role building and operating production ML systems\nStrongly preferred: degree in STEM field such as Computer Science, Statistics, Economics, Mathematics, Engineering, Physics, Operations Research, or a related quantitative field\nDemonstrated ability to apply critical thinking, abstract reasoning, and sound engineering judgment to complex, ambiguous technical and business problems\nStrong expertise deploying, serving, monitoring, and operating ML models in production-including feature engineering systems, training/serving parity, retraining, and model performance diagnostics\nExperience building low-latency online model serving (e.g., gRPC/microservices, ideally with a service mesh such as Istio) for real-time decisioning\nHands-on MLOps experience: pipelines, CI/CD for ML, containerization (Docker), orchestration (Kubernetes), infrastructure-as-code (e.g., Terraform), and workflow schedulers (e.g., Airflow)\nExperience with model versioning, reproducibility, and safe progressive rollout (shadow, canary, champion-challenger) of models in production\nDemonstrated fluency directing AI coding agents (e.g., Claude Code, Cursor, or similar) to build, operate, and debug real ML systems-with experienced judgment on verifying their work\nA track record of replacing manual, repetitive ML workflows with durable automation\nExperience building the infrastructure behind high-impact applied ML use cases such as credit decisioning, risk modeling, fraud, churn, or consumer behavior modeling\nFamiliarity with model explainability, auditability, and the compliance considerations of regulated decisioning (a plus for credit/fintech contexts)\nStrong software and platform engineering fundamentals\nStrong Python coding skills, with the ability to build pipelines, services, and production-quality tooling from scratch; experience with a systems or backend language such as Go or Rust is a plus\nExperience with distributed computing and GPU-accelerated workloads (e.g., Spark, Ray, Dask, or distributed training/inference), including scaling data and model pipelines across clusters\nStrong SQL skills and experience with cloud data warehouses and operational databases (e.g., BigQuery, Spanner), including working with large, messy, real-world datasets\nExperience with observability tools such as Prometheus, Grafana, or Datadog\nExperience with cloud computing services or platforms; GCP preferred\nWillingness to roll up your sleeves and wear multiple hats across engineering, infrastructure, and ML execution based on business needs\nStrong written and verbal communication skills, including the ability to explain technical topics to both technical and non-technical audiences\nWe are excited to hear from you. \nAt Attain, we are passionate about finding people to continuously help us grow our organization. We encourage you to apply, even if your experience doesn’t match every detail on the job description. 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