About this role
Engineers who thrive on scaling machine learning solutions within a high-growth environment will find a natural home on this team. Operating within the Data organization, this group builds systems that power online therapy matching and platform personalization for millions of users globally.
Daily work centers on designing optimization models, establishing machine learning engineering best practices, and partnering with data scientists to transition experimental logic into production architectures. The role exists to scale machine learning infrastructure and unlock new modeling capabilities as the platform grows. Ideal candidates bring deep technical grounding in machine learning systems alongside an eagerness to mentor colleagues and collaborate across product and engineering functions.
What you will do
- Design, prototype, and productionize scalable machine learning and optimization models.
- Define engineering best practices and shape the future direction of the machine learning platform.
- Develop frameworks, pipelines, libraries, utilities, and tools to process massive data for machine learning tasks.
- Partner with data scientists to troubleshoot and optimize complex data pipelines.
- Collaborate with product managers and business partners to gather requirements for machine learning models.
- Build model deployment platforms that simplify implementing new models.
- Create end-to-end reusable pipelines spanning data acquisition to model output delivery.
- Mentor and guide junior data scientists and machine learning engineers to deploy their models into production.
What you need
- Bring six or more years of professional experience in machine learning.
- Demonstrate proven experience deploying and integrating machine learning models into production systems.
- Possess strong expertise in techniques including deep learning, neural networks, statistical modeling, tree-based methods, boosting, regression, and dimensionality reduction.
- Show extensive experience building personalization and recommendation systems.
- Demonstrate a working knowledge of operations research concepts such as mathematical modeling, linear and integer programming, and discrete event simulation.
- Maintain a solid foundation in computer science fundamentals including object-oriented programming, data structures, and algorithms, alongside advanced proficiency in Python and SQL.
Nice to have
- Familiarity with large language models, fine-tuning methods, and reinforcement learning from human feedback (RLHF)
- Experience developing data pipelines and machine learning libraries or tools
Benefits
- Competitive compensation with a base salary range of $170,000 to $245,000 plus performance bonus
- Excellent health, dental, and vision coverage
- 401k benefits with employer matching contribution
- Holistic perks program including free therapy and employee wellness
- Any piece of hardware or software that makes you happy and productive
- Regular in-person bonding experiences with travel expenses covered
Skills and tech stack
Questions candidates ask
Can I work remotely?
Yes, we operate on PST and candidates in any time zone are welcome to apply. We ask employees to travel to our San Jose, CA office up to three times per year plus one company-wide offsite to collaborate in person.
What is the salary range for this role?
The base salary range for this position is $170,000 to $245,000. In addition to the base salary, this position is eligible for a performance bonus and extensive benefits.
What kind of technical background is required?
Candidates need 6+ years of experience in machine learning with proven experience deploying models into production systems. Advanced proficiency in Python and SQL along with a solid foundation in computer science fundamentals are required.