Software Engineer 2 (Hybrid)
•Wyetechllc· 5h
💻 Engineering⏰ Full-time📈 Mid Level💰 $133k-250k🇺🇸 Laurel, USA
5h ago
Dave·11 days ago
Our team is growing, and we’re ready to bring in a passionate Machine Learning Platform Engineer to our Engineering organization. This role is focused on machine learning infrastructure - the platform and systems that allow machine learning models to move from experimentation into reliable, production-grade services.
The Machine Learning Platform Engineering team builds and maintains the shared infrastructure used by data scientists and product teams to deploy, monitor, and operate ML models in real-world, member-facing environments.
If you enjoy building distributed systems, developer platforms, and runtime infrastructure—and want that work to directly power financial decisioning at scale—this role is for you.
Write high-quality, production-ready Python code for core ML platform infrastructure, including:
In-house feature stores
Real-time model scoring services
Systems supporting the full model development and deployment lifecycle
Build and maintain infrastructure that enables safe, repeatable, and observable ML deployment into production.
Improve platform reliability by writing thorough unit and integration tests and participating in thoughtful code reviews.
Identify and resolve performance, scalability, and reliability bottlenecks across platform components.
Partner closely with data scientists and software engineers to understand modeling workflows and translate those needs into robust platform capabilities.
Review system designs, receive feedback, and iterate with long-term maintainability in mind.
Communicate technical concepts clearly to different audiences, from platform engineers to data scientists and product partners.
Evaluate trade-offs in infrastructure and system design, and seek clarity on priorities to ensure we’re building the right platform capabilities at the right time.
Contribute ideas to improve engineering processes, code quality, and team velocity.
The Impact
The infrastructure you build enables machine learning models to operate reliably in production—powering decisions that directly affect how members access fair, fast financial tools. Your work makes ML at Dave not just possible, but dependable at scale.
Experience
Bachelor’s or Master’s degree in Computer Science or a related field, or equivalent practical experience.
3+ years of professional software engineering experience.
Strong proficiency in Python.
Familiarity with the machine learning lifecycle from an infrastructure perspective (deployment, serving, monitoring, data access).
Experience working with SQL and relational databases; familiarity with Snowflake or non-relational systems is a plus.
Strong analytical and problem-solving skills, with a focus on building maintainable, reliable systems.
Nice to Have
Experience with ML platform or MLOps tooling (feature stores, model serving, monitoring, CI/CD for ML).
Experience with real-time or streaming data systems.
Exposure to workflow orchestration tools (e.g., Airflow) or large-scale data processing frameworks (e.g., Spark, Beam).
What Makes Someone Successful Here
You enjoy building systems that other engineers rely on. You care deeply about reliability, correctness, and clarity. You think about how code behaves in production, not just how it works locally.
You collaborate well across disciplines, ask thoughtful questions, and improve systems incrementally. You balance pragmatism with long-term design, and you make trade-offs consciously as requirements evolve.
What to Expect
Meaningful technical ownership on shared infrastructure used across the company. You’ll work alongside experienced platform engineers, learn deeply about production ML systems, and ship work that has a visible, member-facing impact.
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, Datadog.
Build the ML infrastructure behind Dave’s core financial products.
Work on production systems that operate at real-world scale.
Collaborate closely with data scientists and engineers without owning modeling itself.
Grow your depth in ML infrastructure and platform engineering.
Don’t let imposter syndrome get in your way of an incredible opportunity. We’re looking for people who can help us achieve our mission and vision, not just check off the boxes. If you’re excited about this role, we encourage you to apply. You may just be the right candidate for this or other roles.
Why you’ll love working here:
At Dave, our people are just as important as our product. Our culture is a reflection of our values that guide who we are, how we work, and what we aspire to be. Daves are member centric, helpful, transparent, persistent, and better together. We strive to create an environment where all Daves feel valued, heard, and empowered to do their best work. As a virtual first company, team members can live and work anywhere in the United States, with the exception of Hawaii.
A few of our benefits & perks:
💚 Opportunity to tackle tough challenges, learn and grow from fellow top talent, and help millions of people reach their personal financial goals
💻 Flexible hours and virtual first work culture with a home office stipend
🏥 Premium Medical, Dental, and Vision Insurance plans
👶 Generous paid parental and caregiver leave
💰 401(k) savings plan with matching contributions
📈 Financial advisor and financial wellness support
🏖️ Flexible PTO and generous company holidays, including Juneteenth and Winter Break
🎉 All-company in-person events once or twice a year and virtual events throughout to connect with your team members and leadership team
Dave Operating LLC is proud to be an Equal Employment Opportunity employer and is dedicated to cultivating a diverse and inclusive workplace. We will consider for employment all qualified applicants and do not discriminate on any basis protected by federal, state, or local law, including the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance relating to an applicant's criminal history.
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