Machine Learning Scientist, AI Explainability

Description • Lead the Prometheus team’s mission to make AI-driven battery science transparent and trustworthy. You will design, train, and deploy interpretable Large Language Models and multi-agent systems that explain how AI-discovered electrolyte chemistries translate into record-breaking Li-Metal and Li-ion performance. • Own the full research lifecycle—from literature mining and dataset curation to model architecture, training, evaluation, and deployment—ensuring every insight is reproducible, auditable, and aligned with SES’s safety and sustainability standards.• Pioneer novel explainability techniques (attention visualizations, concept activation vectors, counterfactual generators, causal graphs) that allow electrochemists, cell engineers, and regulatory bodies to understand why an AI-recommended additive stabilizes the SEI layer or suppresses dendrite growth. • Build interactive dashboards and APIs that surface model rationales in real time; integrate them into SES’s internal experimentation platform so scientists can query, “Why did the model suggest this solvent ratio?” and receive human-readable evidence plus statistical confidence scores.• Collaborate cross-functionally with battery modeling, robotics, and cloud infrastructure teams to embed interpretability hooks at every layer—from high-throughput robotic synthesis logs to cloud-scale molecular dynamics simulations—creating a seamless feedback loop between lab data and model explanations. • Establish rigorous benchmarking protocols and open-source evaluation suites that set the industry standard for AI explainability in materials science; publish findings at NeurIPS, ICML, and domain-specific journals such as Nature Energy or Joule.• Mentor junior researchers and PhD interns, fostering a culture of scientific rigor, ethical AI, and fearless experimentation; host weekly reading groups on interpretability, fairness, and robustness to accelerate collective learning. • Translate complex technical insights into executive-level narratives that guide strategic decisions on which chemistries to scale, which patents to file, and which partnerships to pursue—directly influencing SES’s multi-gigawatt-hour pipeline. • Champion responsible AI governance by working with legal, compliance, and ESG teams to ensure our models meet emerging global regulations (EU AI Act, SEC climate disclosures, DOE critical-material guidelines) while maintaining competitive advantage.• Contribute to SES’s broader AI-for-science roadmap, identifying adjacent opportunities where explainable models can unlock new physics-informed discoveries in solid-state batteries, sodium-ion chemistries, and next-generation cathode coatings. Requirements • PhD or MS in Computer Science, Machine Learning, Statistics, or related quantitative field with 3+ years of post-graduate experience developing interpretable or explainable AI systems. • Demonstrated expertise in transformer architectures, attention mechanisms, and large-scale pre-training; hands-on experience with PyTorch, JAX, or TensorFlow in multi-GPU or TPU environments.• Proven publication record (NeurIPS, ICML, ICLR, or top-tier domain journals) in explainability, causal inference, or trustworthy ML applied to scientific data. • Strong software engineering practices: version control (Git), containerization (Docker), bolthires/CD, and cloud platforms (AWS, GCP, or Azure); ability to productionize models at scale. • Nice-to-have: domain knowledge in chemistry, materials science, or battery technology; experience with graph neural networks, molecular representations (SMILES, SELFIES, 3D point clouds), or lab automation datasets.️ Benefits • Fully remote-first culture with flexible hours and asynchronous collaboration, plus quarterly on-site summits in Boston or Singapore to align with lab teams. • Competitive equity package (NYSE: SES) and performance bonus tied to breakthrough milestones in battery energy density and cycle life. • Annual $5,000 professional development stipend for conferences, courses, or certifications; dedicated 20 % “innovation time” to pursue blue-sky research. • Comprehensive health, dental, vision, and mental-wellness coverage for employees and dependents, plus 12 weeks of gender-neutral parental leave.Apply tot his job