Ford Motor Company — тарихы 1903 жылдан басталатын, Америка Құрама Штаттарында орналасқан автомобиль өндірушісі. Компания көліктерді әзірлеп, өндірумен қатар, ұтқырлық қызметтері, цифрлық тәжірибелер және бағдарламалық жасақтама бағыттарындағы жұмысын кеңейтіп келеді. Ford қызметі көлік, инновация, қоғамға әсер ету және жауапты бизнес тәжірибелерін өзара байланыстыратын тәсілді ұстана отырып, тұрақты даму бастамалары мен экологиялық мақсаттарды да қамтиды.
Data Scientist, Generative AI
Build production large language model and generative AI solutions for Ford’s enterprise and mobility applications. Develop evaluation frameworks, agentic workflows, and machine learning systems on Google Cloud.
Сипаттама
- Design and take LLM-powered applications from prototype to production for enterprise and customer-facing needs.
- Develop prompting, structured output, model routing, context management, and model adaptation approaches.
- Build evaluation frameworks to assess quality, accuracy, relevance, robustness, speed, cost, safety, and user experience.
- Implement agentic AI capabilities, including planning, tool use, memory, reflection, human approvals, and multi-step workflows.
- Develop and deploy scalable AI and machine learning solutions on Google Cloud using Vertex AI, BigQuery, Cloud Storage, Cloud Run, Cloud Functions, Pub/Sub, and related services.
- Create reusable Python components, APIs, experimentation pipelines, and data products that connect with enterprise platforms and applications.
- Build and maintain structured and unstructured data pipelines for modeling, experimentation, evaluation, and production.
- Conduct exploratory analysis, engineer features, and apply statistical and machine learning methods.
- Set up observability, monitoring, guardrails, automated tests, and feedback loops to improve deployed models and applications.
- Present technical findings, tradeoffs, risks, and recommendations to technical and non-technical stakeholders.
- Track developments in generative AI and apply relevant research to practical business needs.
Талаптар
- Bachelor’s degree in data science, computer science, statistics, mathematics, engineering, or a related field, or an equivalent combination of relevant education and experience.
- At least 2 years applying statistical analysis and traditional machine learning to business problems.
- At least 2 years delivering end-to-end data science solutions, from data exploration and feature engineering through model validation, deployment, and monitoring.
- At least 2 years of professional experience in data science, machine learning, applied AI, natural language processing, or a related technical role.
- At least 2 years programming in Python and proficiency in SQL, with experience writing maintainable, testable, production-quality code.
- At least 1 year developing applications with commercial or open-source LLMs.
- At least 1 year working with prompt engineering, LLM evaluation, model integration, and core NLP concepts.
- At least 1 year using GCP data and AI services, especially Vertex AI and BigQuery, or comparable experience with another major cloud platform and ability to transition to GCP.
- At least 1 year using modern software development and cloud deployment practices, including Git, APIs, containers, CI/CD, identity and access management, and production monitoring.
- Experience with agentic AI, including tool-enabled agents, workflow orchestration, or multi-agent systems, is a plus.
- A master’s degree in a related field is preferred.
- At least 5 years designing or deploying agentic AI systems is preferred.
- Ability to turn ambiguous business needs into measurable technical objectives.
- Strong analytical, problem-solving, documentation, and communication skills.
- Hands-on experience with Vertex AI features such as Model Garden, Generative AI Studio, custom training, model endpoints, pipelines, evaluation, and monitoring.
- Experience building analytics and machine learning workflows with BigQuery, BigQuery ML, Dataflow, or related GCP services.
- Experience with fine-tuning, parameter-efficient adaptation, synthetic data generation, distillation, or model serving and optimization.
- Experience creating evaluation datasets, automated evaluators, adversarial tests, red-team scenarios, and regression test suites for generative AI.
- Knowledge of LLMOps and MLOps, model and prompt versioning, experiment tracking, monitoring, scalable inference, and cost optimization.
- Experience delivering AI solutions in regulated, safety-conscious, or large-enterprise settings.
- Must be legally authorized to work in the United States.
- Visa sponsorship is unavailable.
Артықшылықтар
- Medical, dental, vision, and prescription drug coverage starts immediately.
- Flexible family care days.
- Paid parental leave.
- Programs to help new parents transition back to work.
- Subsidized backup childcare.
- Family-building support, including adoption and surrogacy expense reimbursement and fertility treatments.
- Vehicle discounts for employees and family members.
- Management leases.
- Tuition assistance.
- Active employee resource groups.
- Paid time off for individual and team community service.
- A generous paid holiday schedule, including the week between Christmas and New Year’s Day.
- Paid time off, with the option to purchase additional vacation time.