Sr. Machine Learning Platform Engineer

Job Overview

Dave vs. Goliath. We’re Dave. Dave is a financial app on a mission to build products that level the financial playing field. It is redefining the financial landscape by leveraging technology to create an affordable, transparent, and user-centric access to liquidity for millions of Americans. As a leading innovator in the U.S. financial services sector, Dave’s digital financial platform offers products designed to meet the credit needs of those underserved by traditional financial institutions. Dave’s offerings include its flagship ExtraCash product, providing members up to $500 within minutes. The company is on track to launch several new product offerings in 2026, including a Buy Now Pay Later (BNPL) option.

Dave is focused on serving Americans who are financially vulnerable or living paycheck to paycheck. Dave is leading the charge in creating a new era of credit products that prioritizes speed, affordability, and accessibility, making it the go-to financial partner for those who need it most.

Our team is growing, and we’re ready to bring in a passionate Senior Machine Learning Platform Engineer to our Engineering organization. This is a senior individual contributor role focused on machine learning infrastructure.

You’ll lead technical efforts on the platform and systems that allow machine learning models to be deployed, operated, and trusted in real-world, member-facing environments.

As a Senior Engineer on the Machine Learning Platform Engineering team, you’ll drive architectural decisions, set technical standards, and mentor other engineers while remaining hands-on in the codebase.

What You’ll Build and Own

• Design, build, and evolve core ML platform infrastructure, including:
• Feature stores
• Real-time model scoring services
• Systems supporting the full model development, deployment, and monitoring lifecycle
• Drive technical decision-making for complex initiatives, choosing solutions that scale, are testable, and reduce long-term maintenance burden.
• Lead and influence system design discussions, clearly articulating trade-offs and aligning solutions with product and business goals.
• Set a high bar for code quality and system reliability through exemplary contributions and thoughtful, constructive code reviews.
• Identify, communicate, and mitigate technical risks across platform components before they impact members.
• Partner closely with data scientists, engineers, and product stakeholders to translate modeling and business needs into durable platform capabilities.
• Provide clear, reliable estimates for complex projects, including assumptions, risks, and dependencies.
• Improve team processes, tooling, and standards to increase engineering quality and delivery velocity.
• Mentor and support other engineers through design feedback, code reviews, and onboarding.
• Participate in hiring and interviews, helping raise the technical bar through well-calibrated feedback.

The Impact

The infrastructure you design and maintain enables machine learning to operate reliably at scale—powering decisions that directly affect how millions of members access fair, fast financial tools. Your work ensures ML at Dave is production-ready, observable, and resilient.

What We’re Looking For

Experience

• Bachelor’s degree in Computer Science or a related field, or equivalent practical experience. Advanced degrees are a plus.
• 5+ years of professional software engineering experience, with a focus on backend, platform, or infrastructure engineering.
• Deep expertise in Python; proficiency in an additional language is a plus.
• Strong experience building or operating scalable, high-availability distributed systems in a cloud environment (GCP, AWS).
• Experience working with ML systems from an infrastructure perspective, including deployment, serving, monitoring, and data access.
• Proficiency with SQL and relational databases; familiarity with Snowflake or non-relational systems is a plus.
• Experience leading complex technical projects from design through production.

Nice to Have

• Experience with MLOps tooling or feature store architectures.
• Experience with workflow orchestration tools (e.g., Airflow) and large-scale data processing frameworks (e.g., Spark, Beam).
• Background building data-intensive or real-time systems.

What Makes Someone Successful Here

You think in systems and long-term trade-offs. You anticipate failure modes, design for scale, and care deeply about reliability in production. You’re comfortable making decisions with incomplete information and explaining the rationale behind them.

You elevate the engineers around you through clear communication, mentorship, and strong technical judgment. You collaborate effectively across functions, seek to understand the “why” behind priorities, and adapt as the business evolves.

What to Expect

Significant technical ownership on shared infrastructure used across the company. You’ll influence architecture, set standards, and remain deeply hands-on. The work is complex, impactful, and visible—and it rewards engineers who care about building platforms that last.

Technologies We Use (and Teach)

Kubernetes, Docker, Terraform, ArgoCD, Google Cloud Storage, Pub/Sub, BigQuery, Bigtable, Firestore, Redis, Snowflake, Apache Beam, Airflow, Vertex AI, Python, Java, Node.js, FastAPI, SQL, D