Data Scientist II, ML Infrastructure

About This Opportunity

We are representing a confidential technology client seeking a Data Scientist II, ML Infrastructure to join their growing machine learning organization. This is a fully remote position open to candidates anywhere in the world.

Our client is focused on advancing the science and systems behind ML measurement, feature understanding, and causal inference at scale. In this role, you'll drive foundational innovations, own end-to-end design of production ML systems, establish rigorous methodological standards, and partner cross-functionally to turn successful research into durable platform capabilities.

Responsibilities

• Translate research-grade data science workflows (such as proxy metrics and staleness models) into production ML pipelines using Airflow, WandB, and Ray while establishing reusable patterns for other teams
• Apply and productionize causal inference methods using the production ML stack (propensity scoring, IPW, TMLE) to address high-stakes measurement questions beyond experimental capabilities
• Build self-serve tooling to empower non-experts to derive rigorous causal insights at scale
• Partner with ML engineers and product teams to identify opportunities for improved tooling, metrics, and measurement methods that unlock step-change improvements in model quality and business outcomes
• Leverage rich metadata and engagement signals to build data-driven frameworks, from feature importance to content optimization, that improve platform efficiency and speed
• Design and build centralized ML platform tooling to improve feature and model creation, evaluation, and trust, including production systems that operate daily at scale

What We're Looking For

• 2+ years of hands-on experience as an applied scientist, ML engineer, research scientist, or software engineer, with significant ML production experience
• Strong Python skills and experience with PyTorch or equivalent deep learning frameworks
• Familiarity with distributed compute technologies (Spark, Ray); Ray experience is a strong plus
• Enthusiasm for building tools and platforms that multiply the impact of an entire ML organization, not just solving one-off problems
• Deep ML theory knowledge with extremely strong fundamentals to reason about ML models from first principles
• Proficiency in software development best practices including version control, code review, and reproducible ML pipelines
• Experience with workflow management tools (Airflow, Prefect, Jenkins, or similar) for reliable ML pipeline orchestration
• Bachelor's or Master's degree in Computer Science or a relevant field, or equivalent experience

How We Work With You

As Get A Job.ai recruiter candidates, here's what to expect:

• Apply directly through the Get A Job.ai platform
• Our talent team will screen your application and conduct an initial conversation to understand your background and goals
• If there's a strong match, we'll submit your profile to our client for consideration
• Please do not contact the client directly; all communication should flow through Get A Job.ai to ensure the best candidate experience

Pay

Compensation details will be discussed during the screening process with our recruiting team.

Equal Employment Opportunity: Get A Job.ai is committed to inclusive hiring practices. We consider all qualified applicants regardless of race, color, religion, sex, national origin, age, disability, veteran status, sexual orientation, gender identity, or any other protected characteristic.