We are a fast-growing Bay Area-based startup backed by leading investors like Menlo Ventures, Accel and Y-Combinator. We create AI and ML-powered software for the finance and lending industries, focused on repayment and debt recovery. We work to improve conversations between call center agents and customers and provide actionable insights. Our products use AI to create next-generation speech analytics that support real-time agent guidance, automated notes after conversations, and call analysis for quality assurance and compliance.
The team has deep technical talent today and we believe there is an opportunity to build an iconic vertical software business that will fundamentally impact how the multi-trillion-dollar debt industry is managed. Debt has such a massive impact on consumers and prior to Prodigal, the industry was saddled with painful, low value manual workflows, and poor customer experience- our mission is to humanize the debt repayment process through automation and data.
We are looking for a scrappy and detail-oriented Machine Learning Engineer to join our Data Science and Machine Learning team. In this role, you will work closely with data scientists and engineers to deploy and optimize ML models in production.
Responsibilities:
- Collaborate with fellow data scientists to implement and productionize machine learning models
- Write clean, scalable code to integrate models into our tech stack
- Perform model evaluations and testing to ensure robust, accurate performance
- Monitor models post-deployment and tweak as needed to improve metrics
- Troubleshoot issues with model pipelines and infrastructure
- Contribute to the improvement of our ML architecture and frameworks
- Prototype and fine-tune LLMs
Requirements:
- 6+ months of industry experience in a ML engineering role
- Proficiency in Python; experience with frameworks like PyTorch
- Understanding of ML fundamentals
- Ability to debug and optimize model performance
- Strong communication skills and ability to work cross-functionally
- Attention to detail with a passion for metrics and measurement
- Experience deploying models on AWS or other cloud platforms (preferred)
- BS/MS degree in Computer Science, Statistics, Math or related field
What will you do:
30 Days
- Onboard and get familiar with our company systems and tech stack
- Review existing ML pipelines and models in production
- Setup development environment on AWS and reproduce/re-train existing models
- Create your first PR
60 Days
- Take ownership of maintaining and improving 1-2 existing models
- Take part in on-call rotations and troubleshoot issues in a timely manner
- Contribute to ML design discussions on new projects
- Prototype and fine-tune ML models on AWS
90 Days
- Demonstrate impact by shipping high-quality projects
- Deploy an ML model end-to-end
- Fine-tune LLMs on AWS using compute efficient techniques
- Identify areas for improvement and growth
About you:
You're the type of engineer who digs into problems hands-on and fixes things. You can focus on the small details but also see the full picture. You get excited when you figure out hard ML challenges. You can geek out with fellow data scientists but also explain things simply to other teams. You're always learning - whether it's the latest research or new engineering skills.
If you want to leave generic behind and grow your expertise in an exciting startup environment, we want to hear from you!
From day 1, Prodigal has been defined by talented, humble, and hungry leaders and we want this mindset and culture to continue to blossom from top to bottom in the company. If you have an entrepreneurial spirit and want to work in a fast-paced, intellectually-stimulating environment where you will be pushed to grow, then please reach out because we are looking to build a transformational company that reinvents one of the biggest industries in the US.
To learn more about us - please visit the following
Our Story
What shapes our thinking
Our website
Prodigal
