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<div class="isg-job-description“>What to Expect
What You’ll Do
Build andproductionizeML models & AI agents that power inbound network design, including cost, transit-time prediction, mode/route selection, and consolidation strategies
Design and extend our Digital Twin, integrating inboundnetworkconstraints to mirror real-world designs and enable near real-time decision support
Build and run large-scale “what-if” simulations to evaluate new inboundstrategiesquantifying the impact on cost, service levels, and DC operations
Perform sensitivity and stress testing on existing inbound network designs toidentifybottlenecks, failure modes, and resiliency gaps, and translate findings into concrete recommendations for improvements
Own the ML lifecycle for inbound use cases: data pipelines, feature engineering, model evaluation, AB-testing, monitoring, and continuous retraining based on realized performance versus modeled expectations
Collaborate closely with Material Planning, Logistics, Finance, and Warehouse Operations to turn analytical outputs into executable inbound pilots and long-term standards, tracking realized performance vs. modeled expectations
Build decision-support tools, APIs, and UIs that expose inbound simulations, recommendations, and KPIs directly in our internal Digital Twin, enabling self-serve analysis for cross-functional partners
Translate complex quantitative results into clear narratives and visualizations, enabling leadership to make informed tradeoffs between cost, service, and operational complexity in the inbound network
What You’ll Bring
Degree in computer science, AI, Applied Math, Operations Research, or related field, or equivalent experience
Strong Mathematical fundamentals and algorithms skills
Excellent programming, debugging, performance analysis, and test design skills
Strong programming skills in SQL, Python (experience with libraries such as NumPy, pandas, scikit-learn, TensorFlow, orPyTorch)
Hands-on experience developing full-stack applications using Django (Python), Java-based services, andreact, with a strong understanding of RESTful API design and integration
Knowledge ofMLOpspractices, including CI/CD pipelines, model monitoring, and experience with containerization and orchestration tools such as Docker and Kubernetes
Experience leveraging LLMs, multimodal unsupervised learning, and Generative AI to drive innovative supply chain optimization solutions
Compensation and Benefits
Along with competitive pay, as a full-time Tesla employee, you are eligible for the following benefits at day 1 of hire:
Pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. The total compensation package for this position may also include other elements dependent on the position offered. Details of participation in these benefit plans will be provided if an employee receives an offer of employment.
Tesla is an Equal Opportunity / Affirmative Action employer committed to diversity in the workplace. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, age, national origin, disability, protected veteran status, gender identity or any other factor protected by applicable federal, state or local laws.
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