Description
Compensation
The base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. The salary range for this position is $381K – $510K, and total compensation also includes generous equity, performance-related bonus(es) for eligible employees, and the following benefits.
- Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts
- Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit)
- 401(k) retirement plan with employer match
- Paid parental leave (up to 24 weeks for birth parents and 20 weeks for non-birthing parents), plus paid medical and caregiver leave (up to 8 weeks)
- Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees
- 13+ paid company holidays, and multiple paid coordinated company office closures throughout the year for focus and recharge, plus paid sick or safe time (1 hour per 30 hours worked, or more, as required by applicable state or local law)
- Mental health and wellness support
- Employer-paid basic life and disability coverage
- Annual learning and development stipend to fuel your professional growth
- Daily meals in our offices, and meal delivery credits as eligible
- Relocation support for eligible employees
- Additional taxable fringe benefits, such as charitable donation matching and wellness stipends, may also be provided.
About the Team
Our team builds classification and evaluation systems that give post-training and research teams fast, reliable feedback on model behavior. We combine machine learning and production engineering to make these capabilities accurate, efficient, and useful at scale, helping researchers assess changes and improve models and products.
About the Role
We're looking for a machine learning engineer to develop new classification and evaluation capabilities and bring them into production. You'll help turn open-ended questions about model behavior into well-defined ML problems, build methods that address them, and establish how reliably those methods work.
Responsibilities
- Build, deploy, and operate production classification and evaluation systems that provide useful feedback to post-training and research teams.
- Optimize production inference, including model serving, batching, caching, throughput, latency, compute utilization, and cost.
- Train and optimize classifiers using supervised fine-tuning and distillation, validating quality and efficiency before deployment.
- Own the reliability and observability of deployed classifiers, diagnose production issues, and improve their performance over time.
- Design rigorous evaluations that assess accuracy, calibration, generalization, and potential measurement bias while guiding production tradeoffs.
- Partner with software engineers to integrate models into reliable execution systems and support safe rollouts.
- Build reusable engineering tools that make classifier training, evaluation, deployment, and iteration faster and more repeatable.
- Work with post-training researchers and data scientists to translate their needs into modeling objectives, evaluation criteria, and production requirements.
Requirements
- Experience taking ML projects from problem formulation and experimentation through production deployment.
- Strong foundations in supervised learning, statistics, and experimental design.
- Practical experience evaluating models, investigating systematic errors, and working with imperfect labels.
- Strong software engineering skills and the ability to build maintainable production ML systems.
- Experience reasoning about inference performance and the tradeoffs between quality, latency, and compute cost.
- The ability to combine careful scientific judgment with a practical approach to shipping and operating software.
- Comfort working closely with researchers and engineers on open-ended problems.
- Fluency in using AI agents to move quickly across multiple workstreams in a fast-paced environment.
- Experience with model distillation, representation learning, calibration, or efficient inference is a plus.