Senior Software Engineer, ML Platform

Senior Software Engineer ML Platform

Location: San Francisco CA / Remote Flexible
Employment Type: Full-time
Focus: ML Platform MLOps Model Serving Feature Stores Underwriting Infrastructure

About Our Client

Our client is building financial infrastructure that helps small businesses access the capital and products they need to grow.

Their platform uses data machine learning and modern underwriting systems to power financial products at scale. As the company continues to expand the infrastructure behind model experimentation training evaluation inference and retraining is becoming increasingly critical.

This is an opportunity to join a high-impact infrastructure team and own the ML platform that enables data scientists to safely and quickly ship high-quality models into production.

About the Role

Our client is hiring a Senior Software Engineer ML Platform to lead the evolution of its machine learning platform.

This person will design build and maintain the core systems that support model development production deployment batch inference real-time inference feature stores observability and underwriting infrastructure.

Youll work closely with Data Science and Platform Engineering to turn research workflows into reliable software systems. This is a strong fit for an engineer who enjoys building developer-friendly platforms creating clean abstractions and owning infrastructure that powers real business decisions.

What Youll Do

• Own and evolve the companys ML platform end-to-end
• Turn data science notebooks into reusable tested production-ready software components
• Build libraries pipelines templates SDKs and CLIs that help data scientists move faster
• Create developer-friendly abstractions for feature definition model training evaluation deployment and monitoring
• Build and scale low-latency real-time model serving infrastructure
• Expand batch ML inference systems across scheduling parallelism cost controls observability failure handling and rollback
• Own and improve the feature store including offline and online feature definitions
• Design systems for high read/write throughput and consistent offline/online semantics
• Instrument training and inference workflows for latency throughput accuracy drift data quality and cost
• Build alerting dashboards and observability systems for platform health
• Support production underwriting systems across batch and real-time workflows
• Partner with Data Science on model interfaces SLAs safety checks and product integrations
• Drive incident response postmortems and long-term reliability improvements

What Were Looking For

• 5 years of software engineering experience
• Experience building ML platform MLOps model training model deployment or feature pipeline systems
• Strong Python experience
• Strong software design testing and platform engineering fundamentals
• Proficiency with SQL
• Hands-on experience with Spark or PySpark
• Strong understanding of ML fundamentals including probability statistics supervised and unsupervised learning feature engineering validation strategies model evaluation drift stability and monitoring
• Experience with modern data and ML infrastructure such as AWS Databricks MLflow model registries model serving Airflow or similar orchestration tools
• Experience building real-time systems including service design caching rate limiting backpressure and low-latency architecture
• Experience building batch pipelines at scale
• Practical knowledge of feature store concepts including offline and online stores backfills point-in-time correctness experiment tracking and evaluation frameworks
• Strong ownership mindset and proactive approach to platform reliability
• Excellent communication and collaboration skills across engineering and data science teams

Bonus Experience

• Deep Databricks experience including MLflow workflows lakehouse architecture or model serving
• Experience with feature stores such as Tecton Feast or similar platforms
• Experience with streaming technologies such as Kafka or Kinesis
• Experience in fintech risk lending underwriting or regulated financial systems
• Familiarity with model safety checks rejection flows override flows and auditability
• Experience with A/B testing platforms shadow deployments canary releases and automated rollback
• Experience building low-latency inference systems

Why This Opportunity

• Own a critical ML platform that powers underwriting and other ML-driven products
• Build infrastructure that helps data scientists ship models safely and quickly
• Work across real-time inference batch inference feature stores model evaluation and platform observability
• Partner closely with Data Science and Platform Engineering on high-impact systems
• Build developer-friendly tools that create leverage across the technical organization
• Work on meaningful infrastructure tied directly to financial access for small businesses
• Step into a senior role with end-to-end ownership over core ML platform systems

Required Experience:

Senior IC