Senior Applied AI/ML Scientist — Production-Ready

Dynatron; United States; Remote; USD 160,000 - 210,000; Full time

Benefits offered by this job

Health insurance

Equity participation

401(k) with match

Remote-first environment

Professional development opportunities

Job summary

Dynatron seeks a Lead Applied AI/ML Data Scientist to serve as a technical authority for AI capabilities within our SaaS platform. You will own modeling across core prediction and classification use cases, shape the AI roadmap, and work with PMs, engineers, and leadership to translate business problems into robust AI solutions.

You will lead production AI capabilities, design retrieval and grounding architectures, manage evaluation standards, and ensure maintainability, scalability, and cost

Qualifications

• 10+ years in Data Science or Applied ML.
• Experience shipping AI/ML capabilities in commercial products.
• Strong understanding of experimentation and model evaluation.

Responsibilities

• Lead modeling across core classification and prediction use cases.
• Design features for complex automotive data and scalable solutions.
• Shape AI product roadmap with product leadership and engineers.
• Develop production AI capabilities with ground rules for quality, latency, and cost.
• Evaluate and ground AI capabilities for real customer value.
• Collaborate with Product, Engineering, and MLOps from concept to production.

Skills

Applied ML

Model evaluation

Experiment design

Production ML

LLMs & Generative AI

Python

SQL

Education

Master’s degree in CS/DS/Statistics/Engineering

Tools

Cloud data warehouses

Cloud ML platforms

Transformer models

Job description

Dynatron seeks a Lead Applied AI/ML Data Scientist to serve as a technical authority for AI capabilities within our SaaS platform. You will own modeling across core prediction and classification use cases, shape the AI roadmap, and work with PMs, engineers, and leadership to translate business problems into robust AI solutions.

You will lead production AI capabilities, design retrieval and grounding architectures, manage evaluation standards, and ensure maintainability, scalability, and cost