[Hiring] Senior ML Engineer @Oportun

Role Description

The opportunity is for a Senior ML Engineer focused on ML Platform Engineering rather than ML model development or data science. The role emphasizes ML infrastructure, MLOps, model serving, platform reliability, and production engineering.

The Machine Learning Platform team builds the software foundations that enable data scientists and machine learning engineers to develop, validate, deploy, and operate models safely and efficiently in production. In this role, you’ll lead complex platform work from business context through production outcomes, combining hands-on engineering, architectural judgment, production ownership, and mentorship.

Your day-to-day responsibilities include:

• Leading the design, implementation, deployment, and operation of complex platform capabilities that support the machine-learning lifecycle.
• Designing scalable systems for model deployment and serving, feature computation, data validation, workflow orchestration, monitoring, and production support.
• Building reusable platform components, standards, and automation while improving reliability, scalability, observability, and operational readiness across ML systems.
• Partnering with Data Science, Product, Risk, Fraud, and Engineering teams to translate ambiguous business needs into practical technical solutions.
• Owning significant technical initiatives end to end and leading complex production investigations—clarifying requirements, leading design, delivering safely, measuring results, and implementing durable fixes.
• Raising engineering quality through thoughtful code and design reviews, mentoring engineers through pairing and technical guidance, and contributing to platform technical strategy.
• Looking for ways to use AI tools thoughtfully to work smarter, reduce manual work, and make better decisions faster.

Qualifications

• 5+ years of professional software-engineering experience building and operating production systems.
• Strong programming skills in Python and deep knowledge of API design, distributed systems, service architecture, testing, and software-design fundamentals.
• Experience designing and operating reliable, scalable cloud-native services or data-intensive systems.
• Practical experience with SQL, databases, caching, and tracing data or requests across multiple systems.
• Familiarity with the ML lifecycle from feature engineering and model training through evaluation, deployment, serving, monitoring, and governance.
• Experience with Docker, Kubernetes, CI/CD, Git-based workflows, and production observability.
• Ability to mentor engineers, improve engineering practices, lead through technical influence, and connect engineering work to member, business, product, and operational outcomes.

Requirements

• Experience with AWS, Databricks, MLflow, SageMaker, Spark/PySpark, Kafka, or streaming systems.
• Experience building feature platforms, model-serving systems, workflow-orchestration platforms, or internal developer platforms.
• Familiarity with monitoring model quality, data quality, feature coverage, latency, reliability, or cost efficiency.
• Experience working with sensitive data in a regulated or financial-services environment.
• Experience with Java or another backend language in addition to Python.
• Familiarity with AI tools such as ChatGPT or Copilot to improve personal productivity.

Benefits

• Medical, Dental & Vision Insurance
• Provident Fund
• Life & Accident Insurance
• Internet, Fuel, Meal & Telephone Allowances