Oportun10.03.26
AI SCORE 8.5

Senior Software Engineer - Real-Time ML Platforms

$140K–$180K/year

About the Role

We are looking for a Senior Software Engineer to join our team at Oportun, where you will play a pivotal role in developing innovative solutions for real-time ML deployment. This Senior Software Engineer remote position allows you to work from anywhere while contributing to our mission of financial inclusion.

What You'll Do

  • Design and build self-serve platforms that support real-time ML deployment and robust data engineering workflows.
  • Create APIs and backend services using Python and FastAPI to manage and monitor ML workflows and data pipelines.
  • Implement platforms for real-time ML inference using tools like AWS SageMaker and Databricks.
  • Enable model versioning, monitoring, and lifecycle management with observability tools such as New Relic.
  • Build and optimize ETL/ELT pipelines for data preprocessing, transformation, and storage using PySpark and Pandas.
  • Develop and manage feature stores to ensure consistent, high-quality data for ML model training and deployment.
  • Design scalable, distributed data pipelines on platforms like AWS, integrating tools such as DynamoDB, PostgreSQL, MongoDB, and MariaDB.
  • Use CI/CD pipelines with Jenkins, GitHub Actions, and other tools for automated deployments and testing.

Requirements

  • 5-10 years of experience in IT, with 5-8 years in platform backend engineering.
  • 1 year of experience in DevOps & data engineering roles.
  • Hands-on experience with real-time ML model deployment and data engineering workflows.
  • Strong expertise in Python and experience with Pandas, PySpark, and FastAPI.
  • Proficiency in container orchestration tools such as Kubernetes (K8s) and Docker.
  • Advanced knowledge of AWS services like SageMaker, Lambda, DynamoDB, EC2, and S3.
  • Proven experience building and optimizing distributed data pipelines using Databricks and PySpark.
  • Solid understanding of databases such as MongoDB, DynamoDB, MariaDB, and PostgreSQL.

Nice to Have

  • Experience with observability tools like New Relic for monitoring and troubleshooting.
  • Familiarity with Agile methodologies and tools like Jira.

What We Offer

  • Competitive salary and benefits package.
  • Remote work flexibility.
  • A diverse and inclusive work environment.
  • Opportunities for professional growth and development.
  • Access to cutting-edge technologies and tools.
Why This Job8.5 of 10

This Senior Software Engineer role at Oportun offers a unique opportunity to work on real-time ML platforms in a mission-driven environment. Enjoy remote work and a competitive salary.

Salary Range
Required
0/1
Optional
0/1
Bonus
0/1

Who Will Succeed Here

Expertise in Python and experience with FastAPI to build scalable APIs for real-time machine learning applications, ensuring low-latency responses and high throughput.

Strong experience with AWS services, particularly SageMaker for deploying machine learning models, coupled with familiarity in leveraging Databricks and PySpark for data processing in a cloud environment.

Proven ability to manage containerized applications using Docker and Kubernetes, along with a proactive mindset to monitor application performance using New Relic in a fully remote work setting.

Learning Resources

FastAPI Documentationguide

Career Path

Senior Software Engineer - Real-Time ML Platforms(Now)Lead Software Engineer - Machine Learning Infrastructure(1-2 years)Principal Software Engineer - AI Solutions(3-5 years)

Market Overview

Market Size 2024
$10.2B
Annual Growth
24.5%
AI Adoption
70%
Investment in ML Platforms
+150%
Labour Demand for ML Engineers
+32%
Avg Salary for Senior ML Engineers
$145K

Skills & Requirements

Required
PythonFastAPIAWS
Growing in Demand
TensorFlowApache KafkaMLOps
Declining
MapReduceR Programming

Domain Trends

Increased Demand for Real-Time Data Processing
With 65% of organizations prioritizing real-time analytics, the need for platforms that support real-time machine learning is surging.
Shift Towards Serverless Architectures
Adoption of serverless technologies like AWS Lambda has increased by 40%, allowing for more scalable ML solutions without managing servers.
Integration of ML with Edge Computing
The market for edge computing in ML applications is expected to grow by 30% by 2025, enabling real-time data processing closer to data sources.

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