Headquarters: Remote - India

Who We Are

Oportun (Nasdaq: OPRT) is a mission-driven financial services company that puts its members' financial goals within reach. With intelligent borrowing, savings, and budgeting capabilities, Oportun empowers members with the confidence to build a better financial future. Since inception, Oportun has provided more than $22.7 billion in responsible and affordable credit, saved its members more than $2.5 billion in interest and fees, and helped its members set aside an average of more than $1,800 annually.

A Quick Note on the Role's Focus

While the title is Senior ML Engineer, this opportunity is specifically focused on ML Platform Engineering rather than ML model development or data science.

We're looking for engineers with strong software engineering fundamentals and hands-on experience building, scaling, and operating the platforms and infrastructure that enable machine learning in production. The emphasis is on ML infrastructure, MLOps, model serving, platform reliability, and production engineering, rather than primarily developing, training, or fine-tuning ML models.

Location Note

We’re excited to consider talent currently based in India, as well as candidates who are open to relocating to India for this opportunity. Please note that, as this is a "Remote–India" position, the role must be performed from within India.

The Opportunity

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. In your first year, success means leading meaningful platform initiatives, improving reliability and developer experience, and becoming a trusted technical leader for the ML platform.

At Oportun, we move fast and hold each other to a high bar. You’ll partner across Data Science, Product, Risk, Fraud, and Engineering to solve ambiguous problems and move the needle for members and the business.

What You'll Do

Your day-to-day

• You’ll lead the design, implementation, deployment, and operation of complex platform capabilities that support the machine-learning lifecycle.

• You’ll design scalable systems for model deployment and serving, feature computation, data validation, workflow orchestration, monitoring, and production support.

• You’ll build reusable platform components, standards, and automation while improving reliability, scalability, observability, and operational readiness across ML systems.

• You’ll partner with Data Science, Product, Risk, Fraud, and Engineering teams to translate ambiguous business needs into practical technical solutions.

• You’ll own significant technical initiatives end to end and lead complex production investigations—clarifying requirements, leading design, delivering safely, measuring results, and implementing durable fixes.

• You’ll raise engineering quality through thoughtful code and design reviews, mentor engineers through pairing and technical guidance, and contribute to platform technical strategy.

• You’ll look for ways to use AI tools thoughtfully to work smarter, reduce manual work, and make better decisions faster.

Who You Are

The right fit looks like this

You do not need detailed implementation direction to be effective. Given an ambiguous problem and sufficient context, you can define the technical approach, explain trade-offs, align stakeholders, lead delivery, and own the outcome after launch.

You’re comfortable navigating complexity and don’t wait for perfect information to move forward. You communicate clearly, give direct feedback, mentor others, and connect engineering work to member, business, and operational outcomes.

What You Bring

Skills & experience

• You’ll have 5+ years of professional software-engineering experience building and operating production systems, with strong programming skills in Python and deep knowledge of API design, distributed systems, service architecture, testing, and software-design fundamentals.

• You’ve demonstrated experience designing and operating reliable, scalable cloud-native services or data-intensive systems, with experience in ML platforms, model serving, feature engineering, data platforms, MLOps, or related systems.

• You bring practical experience with SQL, databases, caching, and tracing data or requests across multiple systems, plus familiarity with the ML lifecycle from feature engineering and model training through evaluation, deployment, serving, monitoring, and governance.

• You have experience with Docker, Kubernetes, CI/CD, Git-based workflows, and production observability, and you can evaluate technical alternatives and clearly communicate recommendations, trade-offs, risks, and dependencies.

• You’ve demonstrated the ability to mentor engineers, improve engineering practices, lead through technical influence, and connect engineering work to member, business, product, and operational outcomes.

Nice to Have

• 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, and experience working with sensitive data in a regulated or financial-services environment.

• Experience with Java or another backend language in addition to Python, and familiarity with AI tools such as ChatGPT or Copilot to improve personal productivity.

Our AI Mindset

We build with AI. We lead with judgment.

AI isn't a feature here. It's how we work.

We're excited about what AI makes possible, and we're looking for people who share that