AI/ML Engineer

Build and ship AI/ML features. Work across the model lifecycle: data preparation, training or fine-tuning, evaluation, deployment, and monitoring. Own features end-to-end and iterate based on real production feedback. Contribute to GenAI systems. Build RAG pipelines, work with LLM APIs and open-source models, design prompts for reliability, and contribute to agentic workflows. You will work on the full GenAI stack hands-on. Build data and evaluation infrastructure. Write data pipelines, labeling workflows, and evaluation frameworks. Reliable evals are a first-class deliverable on this team. Work on manufacturing, supply chain, and physical operations problems. Slate operates a factory with a real supply chain. You will be exposed to AI problems in both areas: computer vision for quality inspection, predictive maintenance, and sensor data on the physical side; demand forecasting, inventory planning, supplier risk, and logistics on the supply chain side. We are particularly interested in candidates who are drawn to this kind of work. Collaborate across the organization. Work with Vehicle Engineering, Manufacturing, and Operations to understand requirements and translate them into AI systems that produce measurable output.

Technically grounded in ML. You understand how models are trained and evaluated, not just how to call an API. You have built something end-to-end — a project, a thesis, a production system — that demonstrates that. Interested in physical and operational AI. Candidates drawn to the intersection of AI and the physical world — manufacturing systems, robotics, logistics, industrial data — will find the most to work on and will ramp fastest. Not a requirement, but a clear differentiator. A fast learner. The GenAI landscape moves quickly and so does Slate. You pick up new tools and domains without needing everything handed to you. Hands-on. You write code, run experiments, and ship things. Research interest without engineering follow-through is not a fit for this role. Collaborative and clear. You work well across disciplines and can explain technical decisions to non-technical stakeholders. Be willing to directly interreact with stakeholders to build product without the need for a product manager.

At Slate, we’re fueled by grit, determination, and attention to detail. The start-up spirit of ingenuity and resourcefulness move our business forward. Team Slate fosters a culture of excellence, innovation, and mutual respect, and is motivated by shared principles. •

ABOUT SLATE At Slate, we’re building safe, reliable vehicles that people can afford, personalize and love—and doing it here in the USA as part of our commitment to reindustrialization. The spirit of DIY and customization runs throughout every element of a Slate, because people should have control over how their trucks look, feel, and represent them. Position Overview Slate Automotive is building an AI-native vehicle platform from the ground up. This role is for an engineer ready to work on real AI/ML problems in production: GenAI features, data pipelines, and AI applied to manufacturing and supply chain operations. We are open to candidates early in their careers if they have the right foundation. A PhD in a relevant field is a strong signal; engineers without one should bring equivalent depth through demonstrated project work, research, or professional experience. What matters is that you can build things, learn fast, and are genuinely interested in applying AI to physical and operational systems. The role reports to the Distinguished Engineer of Generative AI. What You Will Do Build and ship AI/ML features. Work across the model lifecycle: data preparation, training or fine-tuning, evaluation, deployment, and monitoring. Own features end-to-end and iterate based on real production feedback. Contribute to GenAI systems. Build RAG pipelines, work with LLM APIs and open-source models, design prompts for reliability, and contribute to agentic workflows. You will work on the full GenAI stack hands-on. Build data and evaluation infrastructure. Write data pipelines, labeling workflows, and evaluation frameworks. Reliable evals are a first-class deliverable on this team. Work on manufacturing, supply chain, and physical operations problems. Slate operates a factory with a real supply chain. You will be exposed to AI problems in both areas: computer vision for quality inspection, predictive maintenance, and sensor data on the physical side; demand forecasting, inventory planning, supplier risk, and logistics on the supply chain side. We are particularly interested in candidates who are drawn to this kind of work. Collaborate across the organization. Work with Vehicle Engineering, Manufacturing, and Operations to understand requirements and translate them into AI systems that produce measurable output. Who You Are Technically grounded in ML. You understand how models are trained and evaluated, not just how to call an API. You have built something end-to-end — a project, a thesis, a production system — that demonstrates that. Interested in physical and operational AI. Candidates drawn to the intersection of AI and the physical world — manufacturing systems, robotics, logistics, industrial data — will find the most to work on and will ramp fastest. Not a requirement, but a clear differentiator. A fast learner. The GenAI landscape moves quickly and so does Slate. You pick up new tools and domains without needing everything handed to you. Hands-on. You write code, run experiments, and ship things. Research interest without engineering follow-through is not a fit for this role. Collaborative and clear. You work well across disciplines and can explain technical decisions to non-technical stakeholders. Be willing to directly interreact with stakeholders to build product without the need for a product manager. Technical Requirements A PhD in a relevant field is a strong foundation for someone early in their career and is treated as such. Candidates without a PhD should bring 3+ years of professional or research experience working directly on ML systems. In either case, the bar is the same: you need to demonstrate you can build. Machine Learning and Generative AI • Foundational understanding of ML: model training, loss functions, evaluation metrics, overfitting, and regularization. • Practical experience with: supervised learning, NLP, computer vision, and time-series modeling. • Familiarity with LLM APIs (OpenAI, Anthropic, Gemini, or similar) and how to build reliably on top of them. • Basic exposure to RAG, embeddings, or retrieval systems — including in a project or research context. • Ability to evaluate model quality rigorously, not just report accuracy on a held-out set. Software Engineering • Python proficiency: comfortable with the ML stack (PyTorch or JAX, Hugging Face, pandas, scikit-learn). • Ability to write production-quality code, not just notebook code. • Familiarity with cloud platforms (AWS, GCP, or Azure) at a working level. • Version control, experiment tracking, and basic MLOps practices. Manufacturing, Physical AI, and Supply Chain (Valued) Preferred. Candidates with background or genuine interest in any of the following will stand out. Academic or professional background in Mechanical Engineering, Electrical Engineering, Robotics, Industrial Engineering, or a related physical discipline. Exposure to computer vision, sensor data, or real-time systems — including coursework or personal projects. Familiarity with supply chain, logistics, or operations research problems. Experience with simulation environments or physical hardware in a research or lab setting. Academic Grounding BS required. MS or PhD in Computer Science, Machine Learning, Robotics, Electrical Engineering, Mechanical Engineering, Industrial Engineering, or a related field preferred. A PhD is treated as a strong signal for early-career candidates. Candidates without advanced degrees should bring equivalent depth through their project or professional work. SALARY RANGE The compensation for this position is the range Slate reasonably and in good faith expects to pay for the position taking into account the wide variety of factors that are considered in making compensation decisions, including job-related knowledge; skillset; experience, education and training; certifications; work location; and other relevant business and organizational factors. 123,339.00 - 154,174.00 - 185,009.00