Senior Software Engineer, Machine Learning Platform

United States Digital Space LLC; San Francisco (CA); Hybrid; USD 187,000 - 259,000; Full time

Benefits offered by this job

Four days in office per week

Fridays work from home

Wellbeing and commuting benefits

Job summary

United States Digital Space LLC is seeking an experienced Machine Learning Platform Engineer to design, build, and operate scalable ML infrastructure on AWS. You will work across distributed systems, cloud, and ML, enabling teams to train, deploy, and monitor models with reliability and cost efficiency.

You will partner with Data Science and ML Engineering teams to evolve feature stores, pipelines, and CI/CD workflows, while owning platform components and governance.

Qualifications

• 5+ years of experience in ML infrastructure, platform engineering, or production ML systems.
• Knowledge of the ML model development lifecycle including data preprocessing, training, evaluation, and deployment.
• Experience with distributed systems, cloud computing, or large-scale data processing.
• Strong CS fundamentals and software engineering principles.
• Hands-on CI/CD and DevOps practices with infrastructure as code.

Responsibilities

• Design, build, and operate scalable ML infrastructure on AWS.
• Develop distributed training and batch processing systems using Ray.
• Build and maintain infrastructure-as-code with Terraform.
• Support and evolve feature stores and pipelines.
• Develop data ingestion and streaming systems (Kinesis, Kafka, Flink, Spark).
• Improve CI/CD workflows for ML models and platform components.
• Enhance observability, reliability, and cost visibility across ML workloads.
• Collaborate with Data Science and ML Engineering teams to improve developer experience.

Skills

Python

Go

Scala

Java

CI/CD

DevOps

Observability

Tools

Docker

Kubernetes

Terraform

Ray

Spark

Kinesis

Flink

Job description

About the role

the company’s Machine Learning Platform (MLP) team builds and operates the infrastructure, tooling, and developer experience that powers machine learning across the company. We enable data scientists and ML engineers to develop, train, deploy, and monitor models reliably and efficiently.

As a Machine Learning Platform Engineer, you will design and build scalable systems that support model training, feature computation, real-time inference, and experimentation. You’ll work at the intersection of distributed systems, cloud infrastructure, and applied machine learning.

This role focuses on building robust foundations that allow ML teams to move quickly while maintaining reliability, governance, and cost efficiency.

The base salary offered for this role and level of experience will begin at $187,000.00 and goes up to $259,000.00. Full-time employees are also eligible for a bonus, competitive equity package, and benefits. The actual base salary offered may be higher, depending on your location, skills, qualifications, and experience.

In this role, you can expect to

• Design, build, and operate scalable ML infrastructure on AWS
• Develop distributed training and batch processing systems using Ray
• Build and maintain infrastructure-as-code using Terraform
• Support and evolve the feature store and feature pipelines
• Develop data ingestion and streaming systems (e.g., Kinesis, Kafka, Flink, Spark, or similar technologies)
• Improve CI/CD workflows for ML models and platform components
• Enhance observability, reliability, and cost visibility across ML workloads
• Partner closely with Data Science and ML Engineering teams to improve developer experience
• Contribute to platform architecture decisions and technical roadmaps
• Participate in on-call rotations to support production systems

To thrive in this role, you have

• 5+ years of experience in ML infrastructure, platform engineering, or production ML systems
• Knowledge of the machine learning model development lifecycle, including data preprocessing, model training, evaluation, and deployment
• Experience with distributed systems, cloud computing, or large-scale data processing
• Strong foundation in computer science and software engineering principles
• Deeply interested in the impact and evolution of advanced AI technologies
• Hands‑on experience with CI/CD pipelines, DevOps practices, and infrastructure as code
• Experience with containerization technologies such as Docker and Kubernetes, and orchestration systems
• Knowledge of cloud platforms such as AWS and distributed computing frameworks such as Spark and Ray
• Experience with GPU programming(CUDA) and GPU costs/optimization
• Strong programming skills in Python, Go, Scala, Java or similar languages
• Familiarity with infrastructure-as-code (e.g., Terraform, CloudFormation)
• Solid understanding of software engineering fundamentals (testing, version control, code review, observability)

Nice-to-have

• Experience with distributed compute frameworks such as Ray
• Experience building or operating a feature store
• Experience with real‑time ML systems or model serving
• Familiarity with streaming technologies (Kafka, Kinesis, Flink, Spark Streaming, etc.)
• Experience supporting ML lifecycle workflows (training, evaluation, deployment, monitoring)
• Knowledge of ML experimentation platforms and model governance practices

#LI-GC1 #LI-SF

A little about us

At the company, we believe that everyone can achieve financial progress. We created the company—a financial technology company, not a bank*—on the premise that core banking services should be helpful, easy, and free. Through our user-friendly tools and intuitive platforms, we empower our members to take control of their finances and work towards their goals. Whether it's starting a savings account, purchasing a first car or home, launching a business, or pursuing higher education, we're proud to have helped millions unlock their financial potential.

We're a team of problem solvers, dreamers, and builders with one shared obsession: our members. From day one, Chimers have worked tirelessly to out-hustle and out-execute competitors to bring our mission to life. Their grit and determination inspire us to work harder every day to deliver the very best experience possible. We each bring an owner's mindset to our work, refusing to be outdone and holding ourselves accountable to meet and exceed the highest bars for our teams, our company, and our members.

We believe in being bold, dreaming big, and taking risks, while also working together, embracing our diverse perspectives, and giving each other honest feedback. Our culture remains deeply entrepreneurial, encouraging every Chimer to see themselves as stewards of our mission to help everyday Americans unlock their financial progress.

We know that to achieve our mission, we must earn and keep people's trust—so we hold ourselves to the highest standards of integrity in everything we do. These aren't just words on a wall—our values are embedded in every aspect of our business, serving as a north star that guides us as we work to help millions achieve their financial potential.

Because if we don’t—who will?

*the company is a financial technology company, not a bank. Banking services provided by The Bancorp Bank, N.A. or Stride Bank, N.A., Members FDIC.

What we offer for our full-time, regular employees

• Our in‑office work policy is designed to keep you connected - with four days a week in the office and Fridays from home for those near one of our offices, plus team and company-wide events depending on location. Whether you’re coming in regularly or are part of our fully remote program, you’ll stay engaged with your work and teammates.
• Benefits that support your work and life, including backup child, elder, and pet care and subsidized commuter benefits for eligible employees.
• Comprehensive health, financial, and wellbeing benefits designed to support you at every stage of life.
• Generous vacation policy and company-wide paid days off
• 1% of your time off to support local community organizations of your choice
• Annual wellness stipend to use towards eligible wellness related expenses