• Help develop AI-based biomarkers that support the personalization of cancer therapy. • Work on multimodal deep-learning models that integrate large-scale pathology whole-slide images with diverse modalities to predict patient risk and response to therapy. • Contribute to developing robust, scalable solutions in this space. • Collaborate closely with fellow ML engineers, biostatisticians, scientific directors, and product partners to build, validate, and deploy models that ultimately impact patient care.

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description We are seeking a talented ML Research Consultant to help us on our mission. You will work closely with our ML team to ideate, implement, and validate the machine learning classifiers and generative models that will ultimately improve our therapeutic design platform, translating functional activity data into breakthrough biologics. - Evaluate Sequence-Function Models: - Design, implement, and evaluate state-of-the-art machine learning models, including Protein Large Language Models (PLLMs), to predict functional activity of GPCR agonist antibodies. - Implement Generative Models: - Design, implement, and evaluate state-of-the-art generative models using Reinforcement Learning or Bayesian Optimization for de novo GPCR agonist antibody design. - Experimentation: - Design and execute rigorous ML experiments with clear hypotheses and documented outcomes and next steps. Qualifications - Bachelor’s or Master’s (5+ years of experience) or Ph. D. degree (2+ years of experience) in Bioinformatics, Biophysics, Computational Biology, or a related quantitative field. - Experience solving complex business problems in the life sciences using ML or statistical methods. - Excellent communication skills particularly in the context of cross-functional life science teams. - Understanding of modern deep learning architectures and optimization techniques as well as fundamentals of machine learning. - 2+ years of hands-on experience developing deep learning models using frameworks like PyTorch. - Strong proficiency in python and the scientific computing stack (NumPy, Pandas, scikit-learn). - Proficiency with software engineering best practices, version control (Git), and testing frameworks. - Experience with Transformer architectures, Recurrent Neural Networks (RNNs), or Graph Neural Networks (GNNs) applied to biological sequence or molecular/structural data. Preferred Qualifications - Theoretical foundations and practical experience with methods in computational structural biology, e.g., protein structure prediction, molecular dynamics, coarse-grained modeling, etc. - Experience with high-throughput screening data, particularly NGS analysis, flow cytometry, or other assays that link genotype to phenotype. - Direct experience with Protein Large Language Models (PLLMs), generative modeling, and/or Reinforcement Learning (RL) in a protein engineering context. - Background in developing models predicting antibody developability characteristics (e.g., stability, aggregation). - Background in molecular/cell biology, antibody discovery, bioinformatics. Benefits - Rate: 125-250 per hour depending on experience, up to 10 hours / week.

• Design, implement, and tune reinforcement learning algorithms to support decision making and behavior modeling in simulation environments. • Define and refine reward functions, state representations, and action spaces aligned to mission objectives. • Build and maintain training environments and experimentation pipelines for reinforcement learning agents. • Evaluate agent performance using rigorous metrics such as convergence, sample efficiency, robustness, and stability. • Analyze training outcomes and simulation results to recommend changes to models, scenarios, or learning approaches. • Collaborate closely with modeling and simulation engineers and software teams to integrate RL components into the baseline. • Ensure AI and ML components are transparent, testable, and maintainable within the broader system architecture. • Support test events, demonstrations, and technical reviews by clearly communicating AI and ML behavior and results.

Role Description We’re seeking a highly motivated and technically skilled Senior Machine Learning Engineer to join our Automation and AI team. This role requires strong machine learning and data science expertise, excellent coding practices, and the ability to translate real-world business challenges into impactful AI solutions while operating within the core values and operating principles of the organization. The ideal candidate is a hands-on contributor who thrives in a fast-paced, purpose-driven environment. - Develop and coach individual MLE’s. Effectively balance multiple projects and provide guidance to MLE’s on best practices and aligning with organizational objectives. - Architecting solutions to solve business problems. - Partnering with Business Units to assess and implement vendor AI tools. - Assessment of intellectual property and company assets utilized by partners with their AI tools. - Design and implement intelligent systems using Generative AI, Retrieval-Augmented Generation (RAG), and Agentic AI to enhance operational efficiency and decision-making. - Develop AI agents that assist internal teams (e.g., Claims, Underwriting, Member Services) with tasks like summarization, document processing, and knowledge retrieval. - Partner with strategic vendors and platform providers to explore and integrate enterprise-grade GenAI capabilities into AAA Life’s ecosystem. - Translate business problems into practical AI solutions, leveraging internal data and LLMs to create scalable tools. - Implement and refine MLOps practices to support the deployment and monitoring of AI agents and services. - Collaborate with stakeholders across operations, IT, and automation to align AI initiatives with business goals. - Mentor junior engineers and advocate for best practices in responsible, sustainable AI implementation. Qualifications - Bachelor’s degree in a quantitative discipline (Computer Science, Engineering, Statistics, etc.) or related field and 7+ years of hands-on experience developing and deploying machine learning models in production. - OR Master’s degree in a quantitative discipline (Computer Science, Engineering, Statistics, etc.) or related field and 5+ years of hands-on experience developing and deploying machine learning models in production. - Experience working with NLP and/or large language models (LLMs). Requirements - Excellent communication skills and ability to explain ML results to non-technical audiences. - Strong knowledge of MLOps tools and model monitoring. - Proficiency in Python and ML libraries such as Scikit-learn, TensorFlow, or PyTorch. Essential Job Functions While performing the duties of this job, the employee is frequently required to stand, walk, sit, use hands to finger, handle, or feel, talk, hear and concentrate. Specific vision abilities required by this job include close vision, distance vision, depth perception, and ability to adjust focus. This job requires the ability to perform duties contained in the job description for this position, including, but not limited to, the above requirements. Reasonable accommodation will be made for otherwise qualified applicants as needed to enable them to fulfill these requirements.