Sr. Manager, Software Engineering - Machine Learning Simulations
About this role
Leaders who thrive in environments combining complex distributed systems with high-impact financial forecasting will find a natural home here. Engineering managers who enjoy scaling critical internal platforms while coaching technical talent will appreciate the chance to shape infrastructure that underpins millions of automated lending decisions.
The Machine Learning and Simulations Platform team at Upstart builds the backend systems that replay historical borrower data through production pricing logic before any model goes live. Thousands of simulations run every month to test new machine learning models, fees, and prices. The incoming senior manager takes charge of scaling this infrastructure to support double the usage while cutting operational costs, improving production fidelity, and expanding access to analytics and finance partners.
Operating within a digital-first organization across US time zones, the person in this role coordinates across machine learning, product, infrastructure, and capital markets groups. Success requires balancing rigorous reliability standards with architectural efficiency to ensure every lending strategy can be accurately sized and tested.
What you will do
- Manage the engineering roadmap for the marketplace simulation platform and determine which internal use cases receive priority.
- Lead, mentor, and expand a team of engineers building distributed services, offline data pipelines, and large-scale compute resources.
- Improve simulation reliability and accuracy through automated checks that identify drift between simulated outputs and production results.
- Optimize compute architecture to lower the cost per simulation as platform usage scales upward.
- Expand simulation coverage across the entire lending funnel in collaboration with teams owning individual stages of pricing logic.
- Translate recurring questions from analytics, machine learning, and capital markets stakeholders into repeatable, self-serve simulation workflows.
What you need
- Bring at least eight years of software engineering experience, including a minimum of three years leading engineering teams.
- Possess a bachelor's degree in Computer Science, Mathematics, Engineering, or a related field, or demonstrate equivalent practical experience.
- Demonstrate a background building and operating backend platforms or distributed production systems.
- Show hands-on experience delivering infrastructure or platform programs that span multiple cross-functional teams.
- Hold proficiency in tools and languages such as Python, Kotlin, AWS, Kubernetes, Databricks, or Spark.
Nice to have
- Knowledge of offline model evaluation systems, backtesting, or simulation
- Experience operating MLOps infrastructure or supporting machine learning teams
- Familiarity with marketplace economics, credit risk, lending, or pricing
- Track record of developing senior engineers and emerging engineering leaders
Benefits
- Competitive base pay, bonus opportunities, and quarterly vesting equity grants
- 401(k) retirement plan with a company match of $2 for every $1 contributed up to $15,000 annually
- Employee Stock Purchase Plan with discounted purchase options
- Comprehensive medical, dental, vision, and wellness coverage with Health Savings Account contributions
- Paid time off, sick leave, holidays, and paid parental leave
- Annual wellness and productivity allowances for personal development and workspace setup
Skills and tech stack
Questions candidates ask
What is the remote work policy for this role?
The position is fully remote within the United States. While employees can live and work anywhere in the country, the company encourages in-person collaboration through regular team onsites that typically occur once or twice per quarter for two to four consecutive days.
What is the salary range for this position?
The anticipated base salary range is $195,300 to $270,400 USD. Actual base pay is determined by geographic location, individual skills, relevant experience, and education.
What technologies and tools does the team use?
The engineering stack includes Python, Kotlin, AWS, Kubernetes, Databricks, and Spark. The team focuses on distributed services, offline data pipelines, and large-scale compute resources.
What does the interview process involve?
Specific details about interview stages are shared by the recruiter during the initial outreach. Candidates can expect discussions covering technical background, leadership experience, and role requirements.