About Our Client:

Our clients infrastructure powers financial services for marketplaces, vertical SaaS, payment platforms, and more. With a single integration, platforms can launch a full suite of financial products for their small business sellers, including capital, spend management, and savings tools. These services provide access to financing and help businesses thrive by leveraging real-time performance data to offer customized financial solutions.

Small businesses are the backbone of our economy, but traditional banks often don’t have their backs. Our client builds tech that makes it simple for small businesses to access the financial tools they need through the platforms they already sell on. They partner with companies like DoorDash, Amazon, Worldpay, and Mindbody to offer fast and flexible funding, spend management, and savings tools to their small business users via a simple integration. They take on all the complexity of capital markets, underwriting, servicing, compliance, and customer service for our partners.

They are backed by prominent venture capitalists, a Series C company, and have raised more than $194M in equity and $340M in debt facilities.

About the role:

 Our client is hiring a Senior Software Engineer, ML Platform to join their team and lead the evolution of their ML Platform. This role is critical to building reliable, scalable, and developer-friendly systems for model experimentation, training, evaluation, inference, and retraining that power underwriting and other ML-driven products for small businesses.

As a Software Engineer, you’ll design, build, and maintain the core abstractions and platforms that let data scientists ship high-quality models to production safely and quickly. You’ll partner closely with Data Science and Platform Engineering, own the ML platform end-to-end, and develop batch and real-time underwriting infrastructure.

What You'll Own:

•  Funnel ownership: Model, measure, and optimize acquisition, activation, and retention end-to-end
• Campaign execution: Launch high-leverage experiments across viral content, community programs, outbound sequences, and paid channels—then double down on what works
• Positioning and messaging: Craft value propositions, competitive differentiation, and collateral that resonate with Sales and RevOps leaders
• Process and tooling: Build attribution models, marketing automation, and sales enablement workflows that scale
• Strategic planning: Translate revenue goals into channel strategy with clear rationale, projected impact, and execution plans
• Content production: Consistently publish across LinkedIn, X, Reddit, and other channels where Sales and RevOps leaders engage
• Team building: Identify gaps, manage contractors or agencies, and help hire and scale the GTM team 

Requirements:

 Must have:

• 5+ years of software engineering experience, including experience on ML platform/MLOps systems (training, deployment, and/or feature pipelines).
• Strong Python; solid software design and testing fundamentals. Proficiency with SQL; hands-on Spark/PySpark experience.
• Knowledge of ML fundamentals—probability & statistics, supervised vs. unsupervised learning, bias/variance & regularization, feature engineering, model evaluation metrics, validation strategies, and production concerns like drift, stability, and monitoring.
• Expertise with modern data/ML stacks—AWS, Databricks (workflows, lakehouse, MLflow/registry, Model Serving), and Airflow (or equivalent orchestration).
• Experience building real-time systems (service design, caching, rate limiting, backpressure) and batch pipelines at scale.
• Practical knowledge of feature-store concepts (offline/online stores, backfills, point-in-time correctness), model registries, experiment tracking, and evaluation frameworks.
• Strong problem-solving skills and a proactive attitude toward ownership and platform health.
• Excellent communication and collaboration skills, especially in cross-functional settings.

Nice-to-have:

• Databricks experience (MLflow, Model Serving).
• Experience with feature stores (e.g., Tecton, Feast) and streaming (Kafka/Kinesis).
• Experience with fintech, risk, or underwriting systems; familiarity with model safety checks, rejection/override flows, and auditability.
• Background with A/B testing platforms, shadow/canary deployments, and automated rollback.
• Experience with low-latency inference systems.

Benefit's and Perks:

• Medical, dental & vision insurance
• Work from home flexibility
• Unlimited PTO
• Commuter benefits
• Free lunches
• Paid parental leave
• 401(k)
• Employee assistance program

Compensation:

$230K to $330K with equity grant

Job Details:

Location: San Francisco, CA

Type: Hybrid

Employment: Full-Time

Visa Sponsorship: H-1B transfers and O-1 visas are supported.