Re-architect ML pipelines and AWS infrastructure (EC2/ECS) to slash training times and cloud overhead
Iterate on our underwriting model to maximize cash advance disbursements while maintaining target risk thresholds and default rates
Standardize the MLOps stack, implementing robust CI/CD, model monitoring, and automated scaling for our bare-metal environment
Engineer high-throughput data pipelines using Airflow and Python that can handle massive card-market datasets with minimal memory footprints
Own the model's AWS infrastructure, writing code for our pricing API to ensure the model can serve at scale and with low latency

Requirements

8+ years of software and/or data engineering experience with at least 4-5 years of direct machine learning platform experience.
Expertise in High-Performance Python and Linux systems
Deep experience with Docker/ECS and optimizing workloads on bare-metal EC2 instances
A strong foundation in ML Ops and infrastructure, with experience deploying models on AWS using tools like ECS and Docker.
Experience in data orchestration using Airflow for model training and batch jobs.
Proven track record of scaling ML systems in production, specifically solving for memory bottlenecks and compute-heavy training cycles
Experience with ML platforms that support pricing and/or underwriting models in a similar marketplace environment (nice to have)

Benefits

A seat at the table to help shape the future of Alt and the alternative asset space
Autonomy and ownership on projects that matter
$100/month work-from-home stipend
$200/month wellness stipend
WeWork office stipend
401(k) retirement benefits
Flexible vacation policy
Generous paid parental leave
Competitive healthcare benefits, including HSA, for you and your dependent(s)

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

machine learningMLOpsPythonAWSEC2ECSDockerAirflowdata engineeringhigh-performance computing