• Set and evolve the research direction for the core intelligence, including context representation, memory, reasoning, planning, and orchestration. • Decide when to design new model architectures versus adapting or leveraging frontier open-source or commercial models. • Define evaluation frameworks that measure real-world usefulness, robustness, safety, and long-term behavior, not benchmark vanity. • Own alignment, safety, and guardrail strategy as first-class product concerns. • Guide exploration of frontier techniques such as retrieval-augmented training, mixture-of-experts, distillation, multi-agent orchestration, and multimodal systems. • Shape early product intelligence direction in close partnership with product and application engineering. • Set the technical bar for research rigor, judgment, and taste across the organization.

Role Description We are seeking a Machine Learning Researcher with expertise in machine learning for biological systems, with a particular focus on genomic and multi-omic data modeling. This role is centered on building and deploying state-of-the-art AI models, including large-scale genomic language models and deep representation learning architectures, that extract actionable biological insight from complex molecular datasets. Your work will directly enable transformative applications in genomic selection and genome editing target identification. This position is being hired at the entry level. Depending on the candidate's depth of experience and demonstrated research impact, the role may be filled at the Senior Machine Learning Researcher level. YOUR TASKS AND RESPONSIBILITIES - Genomic & Omic Model Development: - Design, train, and evaluate deep learning models on diverse omic datasets. - Genomic Language Models: - Develop and fine-tune foundation models for DNA/RNA sequences. - Genomic Selection & Editing Enablement: - Build predictive models that connect genotype to phenotype across environments. - Functional Data Integration: - Integrate heterogeneous biological data types into unified predictive frameworks. - Interdisciplinary Collaboration: - Work closely with molecular biologists, geneticists, breeders, bioinformaticians, and computational scientists. - Scalable Deployment: - Partner with engineering and IT teams to operationalize models within genomic selection pipelines. - Research Contribution: - Advance the state of the art through publications and engagement with the broader computational biology and AI research community. - Documentation & Communication: - Communicate complex modeling results to diverse audiences and prepare technical reports. Qualifications - PhD in one of the following or closely related fields: - Computational Biology / Bioinformatics - Machine Learning / Deep Learning - Genomics / Statistical Genetics - Computer Science (with focus on biological or sequential data) - Biostatistics / Quantitative Genetics - Systems Biology - Another related quantitative discipline with demonstrated application to biological data - Demonstrated research experience building and training deep learning models on biological sequence data or high-dimensional omic datasets. - Proficiency in modern deep learning frameworks (PyTorch, JAX, or TensorFlow). - Working knowledge of molecular biology fundamentals sufficient to interpret model outputs in biological context. - Strong communication skills and ability to collaborate effectively across disciplines. Requirements - Hands-on experience developing or fine-tuning genomic language models or biological foundation models. - Experience with transformer architectures, long-context sequence modeling, or attention mechanisms applied to biological sequences. - Familiarity with multi-omic data integration methods. - Background in quantitative genetics or genomic prediction. - Experience with functional genomics data. - Knowledge of pangenomics, structural variant calling, or comparative genomics across crop species. - Experience with self-supervised, semi-supervised, or transfer learning strategies for data-efficient modeling in biology. - Familiarity with interpretability/explainability methods. - Exposure to classical ML approaches. - Experience with model deployment in production. - Track record of interdisciplinary collaboration with experimental biologists. Benefits - Salary of approximately $110k-150k. - Additional compensation may include a bonus or incentive program. - Additional benefits include health care, vision, dental, retirement, PTO, sick leave, etc. Company Description Bayer is committed to providing access and reasonable accommodations in its application process for individuals with disabilities and encourages applicants with disabilities to request any needed accommodation(s).

• Conduct original research in embodied AI, including learning from warfighter demonstration, reinforcement learning, memory, reward, vision-language modeling, and world modeling • Build, test, and benchmark large-scale models for perception, decision-making, and control in simulated and physical environments • Investigate transfer learning and continual learning paradigms across diverse robotic domains • Collaborate with engineering, robotics, and field teams to integrate research into operational systems • Lead research strategy and roadmap across key areas of autonomy and machine learning • Support field tests and data collection campaigns to evaluate system performance in realistic environments

• Identify emerging AI tools and platforms relevant to legal services • Conduct structured research across vendors, technologies, and use cases • Evaluate tools based on ROI, scalability, compliance, and integration capability • Stay current on advancements in AI, automation, and legal technology • Design and execute pilot programs for selected AI tools • Build workflows and SOPs for deployment across departments • Coordinate implementation across teams (legal, intake, marketing, operations) • Ensure proper integration with existing systems (CRM, case management, communication tools) • Implement AI tools for lead generation, ad optimization, and content creation • Improve conversion rates through AI-driven targeting and messaging • Deploy AI for call handling, chatbots, and lead qualification • Optimize intake workflows to reduce response time and increase signed cases • Implement AI solutions for document automation, OCR, and data extraction • Reduce manual handling and improve turnaround times • Optimize workflows using AI for task automation, reminders, and case tracking • Improve efficiency and reduce case cycle time • Utilize AI for legal research, deposition summaries, and document review • Assist attorneys in trial prep through automation and data organization • Implement AI tools for billing, forecasting, collections, and financial reporting • Improve visibility into case profitability and firm performance • Establish and monitor KPIs across all implemented AI systems • Build dashboards and reporting systems to track performance and ROI • Continuously optimize tools based on data insights