Shape the AI Product Roadmap

• Serve as a technical authority on the feasibility of proposed AI capabilities.
• Partner with Product leadership to evaluate opportunities before significant engineering investment is made.
• Clearly articulate what is technically achievable, what requires additional data or sequencing, and what is unlikely to deliver the intended result.
• Recommend alternative approaches when AI isn't the appropriate solution.
• Help prioritize opportunities based on customer value, technical feasibility, data readiness, and implementation complexity.

Build with Generative AI & Agentic Systems

• Develop production capabilities using LLMs and modern agentic frameworks where they provide meaningful product value.
• Design retrieval architectures, tool-use patterns, and other approaches for grounding AI systems in Dynatron's proprietary data.
• Evaluate and adapt foundation models for domain-specific applications, including fine-tuning where appropriate.
• Establish appropriate controls around quality, latency, token usage, and cost per interaction.
• Stay current with emerging AI capabilities while applying disciplined judgment about where they belong in production.

Define AI Evaluation Standards

• Establish rigorous evaluation methodologies for traditional ML and non-deterministic generative AI systems.
• Define appropriate offline and production metrics for individual use cases.
• Design evaluation frameworks that measure accuracy, reliability, business usefulness, and other relevant quality dimensions.
• Monitor production performance and use real-world results to guide model improvement.
• Help establish consistent standards for determining when an AI capability is ready for customers.

Partner Through Production

• Work closely with Engineering and MLOps/DevOps partners to establish production-readiness criteria.
• Define requirements for deployment, monitoring, retraining, and model lifecycle management.
• Ensure appropriate handoffs without treating productionization as someone else's problem.
• Collaborate across Data Engineering, Product, and Engineering to ensure AI solutions have the data and infrastructure required to perform reliably.

What You Bring

Applied Data Science & Machine Learning Expertise

• 10+ years of experience in Data Science, Applied Machine Learning, or a closely related discipline.
• Deep expertise in traditional machine learning, including classification, feature engineering, class imbalance, and model evaluation.
• Significant experience working with complex, imperfect real-world datasets rather than exclusively curated research data.
• Strong understanding of experimental design and how to determine whether a model is actually improving an outcome.

Production AI Experience — Critical

• Demonstrated experience shipping AI/ML capabilities into commercial products used by real customers.
• Ability to speak specifically about systems you've built, their scale, how they performed after release, and what you changed based on production evidence.
• Experience supporting and improving models throughout their production lifecycle.
• Strong understanding of the differences between building a successful prototype and operating a successful AI product.

Generative AI & LLM Expertise

• Production experience building with LLMs and agentic frameworks.
• Experience designing retrieval architectures and grounded AI applications.
• Strong understanding of evaluation methodologies for non-deterministic systems.
• Experience managing quality, latency, token consumption, and cost per interaction in production.
• Experience fine-tuning or otherwise adapting transformer models for domain-specific use cases.

Product & Business Judgment

• Demonstrated experience scoping AI initiatives directly with Product Managers and business stakeholders.
• Track record of identifying technically weak or commercially impractical AI concepts and influencing stakeholders toward better solutions.
• Ability to translate business problems into modeling problems—and recognize when the underlying problem doesn't require AI.
• Strong customer orientation with curiosity about the business problem behind the requested capability.

Technical Skills

• Expert-level Python and strong SQL skills.
• Comfortable working directly with large datasets in cloud data warehouse environments.
• Experience collaborating within modern cloud-based data and ML ecosystems.
• Strong understanding of the data requirements and dependencies necessary to support production AI.

Communication & Technical Leadership

• Ability to communicate sophisticated AI concepts, limitations, and trade-offs clearly to non-technical stakeholders.
• Strong influence skills and confidence challenging assumptions constructively.
• Ability to establish technical standards and raise the quality of work without direct people-management authority.
• Strong documentation habits and commitment to making technical decisions understandable and reproducible.

Education

• Master's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or another quantitative discipline, or equivalent practical experience.

Nice to Have

• Experience working with automotive, dealership, or Fixed Operations data.
• Experience in another domain involving complex operational records, industry-specific taxonomies, or similarly challenging datasets.
• Experience with cloud-managed AI/ML services and modern production model lifecycle practices.
• Experience designing, managing, or governing large-scale expert labeling programs.
• Experience working with proprietary datasets as a foundation for differentiated AI products.

What Success Looks Like

• Turn difficult customer problems into AI capabilities that perform reliably in production.
• Raise the technical standard for classification, prediction, generative AI, and model evaluation.
• Help Product distinguish compelling AI opportunities from ideas that aren't technically or commercially sound.
• Build solutions appropriate to the problem rather than defaulting to the newest technology.
• Establish clear evidence that AI capabilities work before—and after—they reach customers.
• Improve models based on real-world production performance rather than treating deployment as the finish line.
• Partner effectively with Product, Engineering, Data Engineering, and MLOps from concept through production.
• Use Dynatron's proprietary automotive data to create differentiated capabilities that deliver measurable customer value.

Why Dynatron

• Help shape the AI capabilities at the center of Dynatron's next generation of products.
• Work with rich, complex automotive datasets that create opportunities for differentiated machine learning and AI.
• Influence the AI product roadmap as a senior technical authority, not simply execute predefined requirements.
• Build across traditional machine learning, generative AI, LLMs, and emerging agentic technologies.
• High-impact Lead IC role with significant technical autonomy and organizational influence.
• Partner directly with Product, Engineering, Data, and technology leadership as Dynatron continues its evolution toward an AI-first organization.
• Remote-first environment offering autonomy, ownership, and flexibility.

Compensation & Benefits

• Comprehensive health, dental, and vision insurance
• Equity participation through Dynatron's Equity Incentive Plan
• 401(k) with competitive company match
• Flexible vacation policy and 11 paid company holidays
• Employer-paid short- and long-term disability and life insurance
• Home office setup support
• Remote-first working environment
• Ongoing professional development opportunities

Originally posted on Himalayas