Senior AI/ML Platform Engineer

AI/ML Infrastructure Engineer – Cost Optimization & Predictive Analytics

About the Role:

Join a fast-scaling data platform company at the forefront of modern analytics engineering. This is a rare opportunity to own the end-to-end AI/ML infrastructure for a greenfield product aimed at solving one of the industry’s biggest challenges: cost visibility and optimization in data warehouses.

You’ll lead the design and implementation of intelligent systems that detect anomalies, predict spend, and automate remediation — all embedded directly into the data workflow layer. If you’re excited by building foundational AI/ML architecture from scratch, working with massive datasets, and delivering real business impact, this role is for you.

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What You'll Do:

• Architect and build ML infrastructure for anomaly detection and predictive cost modeling at scale

• Design automated remediation systems to proactively manage and reduce data platform spend

• Work with massive data volumes using technologies like Snowflake, Kafka, PyTorch, TensorFlow, Ibis, and Pandas

• Partner with Product, Engineering, and Design teams to embed AI directly into core data workflows

• Provide technical leadership in shaping ML strategy and influencing broader product direction

• Help grow and mentor a high-performing AI/ML team

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What We're Looking For:

• Deep hands-on experience building scalable ML infrastructure and pipelines from the ground up

• Strong background in anomaly/fraud detection, predictive analytics, and confidence scoring

• Proficient in Python, with hands-on expertise in PyTorch, TensorFlow, and preferably PySpark

• Experience with cloud data platforms (especially Snowflake), real-time streaming (Kafka), and distributed ML systems

• Proven track record of shipping production-grade ML systems — not just research or prototype work

• Comfort working in fast-paced, fully remote environments with asynchronous communication

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Bonus Points For:

• Experience benchmarking ML models and automating remediation workflows

• Familiarity with CostOps/FinOps principles and optimizing spend in cloud-based data systems

• Previous experience at a high-growth startup or within a large-scale data platform environment

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Qualifications:

• 5+ years in ML infrastructure, AI/ML engineering, or data engineering

• Bachelor's degree in a relevant technical field (or equivalent professional experience)

• Bootcamp or alternative education pathways also considered

• Experience in high-scale ML environments with remote/distributed teams

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Compensation & Perks:

• Base Salary Range (Typical): $172,000 – $207,900 USD

• Base Salary in Select Locations: $191,000 – $231,000 USD

• Equity included, with additional performance-based components

• Unlimited vacation (and a culture that encourages using it!)

• 401k with 3% guaranteed contribution

• Comprehensive healthcare, paid parental leave

• Wellness and home office stipends

• Remote-first team with global flexibility

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Hiring Process:

1. Intro call with Talent Acquisition

2. Technical interview with Hiring Manager

3. Hands-on coding challenge

4. Systems design + product collaboration interview

5. Final conversation with senior leadership