Senior Machine Learning Engineer

About Attain Attain is a finance AI copilot designed to empower homebuyers as they navigate the complexities of home financing and get mortgage-ready. In today’s world, achieving homeownership has never been more difficult—it’s one of the biggest challenges facing society. More than ever, buying a home requires careful preparation, expert guidance, and solid support. Attain securely connects to users’ financial information—including income, credit, debt, and assets—along with other relevant data sources, to deliver personalized, transparent advice on credit optimization, savings plans, down payment assistance, debt reduction, mortgage options, budgeting, taxes, and more. Additionally, Attain enables homebuyers to collaborate effortlessly with their real estate agent, mortgage broker, or lender directly in the app.

Attain

AI coach helping first-time homebuyers optimize their finances for a mortgage.

A

attain.app

2-10 employees

Financial Services

Montreal

2022

Privately Held

445 LinkedIn followers

• 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.
• Build, 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
• Build and improve the pipelines and serving infrastructure behind our
• predictive models across consumer decisioning, fraud, churn, transaction
• intelligence, and other business-critical use cases
• Own the production side of the model lifecycle: feature pipelines,
• deployment, CI/CD, monitoring, and automated retraining
• Build 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
• Instrument 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
• Direct 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
• Automate manual, repetitive steps in the ML lifecycle so the team can move
• faster without sacrificing reliability
• Partner with data scientists to give them fast, safe paths to deploy, iterate
• on, and retrain models in production
• Collaborate with analysts, platform engineers, product managers, and business
• stakeholders to deliver ML systems with quality, efficiency, and precision
• Identify new areas where platform improvements, automation, and MLOps tooling
• can improve product velocity and business outcomes

Preferred

• 5+ 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
• Strongly preferred: degree in STEM field such as Computer Science,
• Research, or a related quantitative field
• Demonstrated ability to apply critical thinking, abstract reasoning, and
• sound engineering judgment to complex, ambiguous technical and business
• problems
• Strong expertise deploying, serving, monitoring, and operating ML models in
• production—including feature engineering systems, training/serving parity,
• retraining, and model performance diagnostics
• Experience building low-latency online model serving (e.g.,
• gRPC/microservices, ideally with a service mesh such as Istio) for real-time
• decisioning
• Hands-on MLOps experience: pipelines, CI/CD for ML, containerization
• (Docker), orchestration (Kubernetes), infrastructure-as-code (e.g.,
• Terraform), and workflow schedulers (e.g., Airflow)
• Experience with model versioning, reproducibility, and safe progressive
• rollout (shadow, canary, champion-challenger) of models in production
• Demonstrated 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
• A track record of replacing manual, repetitive ML workflows with durable
• automation
• Experience building the infrastructure behind high-impact applied ML use
• cases such as credit decisioning, risk modeling, fraud, churn, or consumer
• behavior modeling
• Familiarity with model explainability, auditability, and the compliance
• considerations of regulated decisioning (a plus for credit/fintech contexts)
• Strong software and platform engineering fundamentals
• Strong 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
• Experience with distributed computing and GPU-accelerated workloads (e.g.,
• Spark, Ray, Dask, or distributed training/inference), including scaling data
• and model pipelines across clusters
• Strong SQL skills and experience with cloud data warehouses and operational
• databases (e.g., BigQuery, Spanner), including working with large, messy,
• real-world datasets
• Experience with observability tools such as Prometheus, Grafana, or Datadog
• Experience with cloud computing services or platforms; GCP preferred
• Willingness to roll up your sleeves and wear multiple hats across
• engineering, infrastructure, and ML execution based on business needs
• Strong written and verbal communication skills, including the ability to
• explain technical topics to both technical and non-technical audiences
• We are excited to hear from you.
• At 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. If we don’t see something that
• immediately fits, we will keep your resume on file for future opportunities.