Machine Learning Engineer, Data Acquisition

As part of our early team you will focus on:

• Algorithm & Experimentation: Formulate hypotheses, build rapid prototypes, and run statistically powered offline/online experiments to validate impact.
• Evaluation & Diagnostics: Design task-specific metrics, bootstrap confidence intervals, and perform slice-based error analysis and fairness checks.
• Collaboration & Product Innovation: Work cross-functionally with engineers, product managers, and stakeholders to innovate on data collection, scaling, and normalization—delivering effective and efficient ML solutions.
• Model & Tooling Selection: Select and adapt the right models—LLMs or classical—balancing latency, cost, and quality, and integrate them into our Data Acquisition stack.
• Data Readiness: Partner with data engineers to ensure training/eval data stay clean, versioned, and bias-checked.
• End-to-End Ownership: Take prototypes to production, collaborating with engineers to deploy, monitor, and continually improve models.

You're a good fit because you have:

• 3+ years building and shipping ML models, including hands-on experience with LLMs or classical NLP methods in production environments.
• Experience partnering with software engineers to ship, monitor, and iterate on models in production.
• Proficiency in Python (must-have); Java or Go a plus.
• Strong understanding of machine learning frameworks and libraries (e.g., TensorFlow, PyTorch, Scikit-learn).
• Solid grasp of classical ML algorithms—their assumptions, strengths, and failure modes (e.g., tree ensembles, logistic/linear models, nearest-neighbor indexes).  
• Hands-on experience designing offline or online experiments: crafting task-specific metrics, computing bootstrapped confidence intervals, and conducting slice-based error analysis.
• Familiarity with cloud services (e.g., AWS, GCP, Azure) and distributed computing.
• Excellent analytical and problem-solving skills with a keen attention to detail.
• Demonstrated curiosity—comfortable jumping into unfamiliar domains, papers, or codebases and learning fast.
• Strong verbal/written skills—able to explain complex ML concepts to diverse stakeholders.
• Demonstrated ability to work effectively in a collaborative team environment.

It’s a bonus if you have:

• Experience directly deploying or monitoring models in production environments
• Healthcare domain experience (claims, EHR, clinical NLP)
• Publications, OSS contributions, or peer-reviewed talks on ML/AI
• Experience developing and even designing software for distributed data pipelines
• Bachelor's degree in Computer Science, or Statistical Science preferred, advanced degrees are a plus.