Role Description As one of Alice's CBRN experts, you'll focus on ensuring the safety and integrity of Generative AI (GenAI) systems by leveraging your specialized knowledge in Chemical, Biological, Radiological, and Nuclear domains. You will create domain-specific prompts and evaluate AI-generated responses to help identify vulnerabilities and ensure content accuracy, reliability, and ethical integrity. We are seeking experts in one or more of the following specializations: - Biology – Molecular biology, genetics, biotechnology, and related fields - Chemistry – Organic, inorganic, physical, and analytical chemistry - Virology – Epidemiology, immunology, and infectious diseases - Nuclear Energy – Nuclear power generation, safety, and regulations - Radioactive Materials – Radioactive energy applications, safety, and compliance Key Responsibilities: - Write domain-specific CBRN prompts, including adversarial prompts designed to test model vulnerabilities and resistance to generating dangerous outputs - Evaluate AI-generated responses for accuracy, scientific validity, and regulatory compliance - Identify and flag content that is incorrect, harmful, misleading, or ethically concerning - Provide constructive feedback to improve prompt design and content generation - Stay updated with current research, regulations, and best practices within your area of expertise Qualifications - Advanced degree in a relevant CBRN field (Ph.D. preferred; Master's degree with significant experience will be considered) - Deep expertise in at least one of the following areas: Biology, Chemistry, Virology, Nuclear Energy, or Radioactive Materials - Excellent analytical skills with keen attention to detail - Strong written communication skills to articulate findings and recommendations clearly - Ability to work independently and manage time effectively in a remote environment Requirements - Expertise in multiple CBRN domains - Knowledge of AI prompt engineering concepts and practices, including adversarial prompt writing - Experience with AI or machine learning projects - Familiarity with ethical considerations and challenges in AI-generated content Benefits - The salary range for this role is $146K - $155K OTE - Range may vary based on experience. Salary at the time of offer will be commensurate with experience. Company Description Alice is a trust, safety, and security company built for the AI era. We safeguard the communicative technologies people use to create, collaborate, and interact—whether with each other or with machines. In a world where AI has fundamentally changed the nature of risk, Alice provides end-to-end coverage across the entire AI lifecycle. We support frontier model labs, enterprises, and UGC platforms with a comprehensive suite of solutions: from model hardening evaluations and pre-deployment red-teaming to runtime guardrails and ongoing drift detection.

• Identify high-value opportunities to apply AI, ML and DL across Photonic’s technical workflows, with a focus on research, development and engineering. • Lead engineers through the design, prototyping, development and deployment of AI-enabled tools that support quantum and other technical workflows. • Prioritize AI initiatives based on technical feasibility, business value, adoption potential, and risk. • Establish evaluation methods and benchmarks to determine whether AI solutions are accurate, useful, and trustworthy in production settings. • Partner with scientists, engineers, and technical leaders to understand difficult problems and determine where AI, ML and DL can materially improve outcomes. • Integrate AI capabilities into existing software platforms, engineering workflows, and technical processes where appropriate. • Advise technical opportunities, tradeoffs, risks, and outcomes clearly to both technical and non-technical stakeholders. • Help shape AI standards, governance, and safe usage practices in partnership with relevant stakeholders. • Mentor teams in the practical use of AI methods, tools, and workflows.

Role Description We are seeking an AI Performance Engineer to focus on extracting maximum throughput, minimizing latency, and reducing cost across training and inference workloads for large neural network systems. The role spans the full stack from low-level kernel optimization to distributed system tuning, requiring deep understanding of GPU architecture, model parallelism, memory management, and compiler-level optimization. - The ideal candidate has demonstrated impact on production AI workloads. - Strong instrumentation and measurement discipline that enables rigorous, data-driven optimization decisions. - Work closely with cross-functional partners — product, design, engineering, operations, and business stakeholders — to translate ambiguous requirements into well-engineered solutions. - Expected to raise the bar through code review, design review, and mentorship of more junior engineers. - Brings strong engineering discipline, a clear communication style, and a track record of shipping meaningful work that holds up well in production. Qualifications - Bachelor’s or Master’s degree in Computer Science, Computer Engineering, or a related field. - Six or more years of experience in performance engineering, ML systems, or HPC. - Strong proficiency in Python and C++. - Hands-on experience optimizing deep learning workloads on modern GPUs. - Deep understanding of distributed training and inference techniques. - Experience with profiling tools across CPU, GPU, and distributed systems. - Familiarity with model compression techniques and their accuracy implications. - Strong grasp of memory hierarchies, communication primitives, and parallelism strategies. - Excellent measurement, debugging, and analytical reasoning skills. - Strong communication and collaboration skills. Requirements - Experience optimizing LLM inference at production scale. - Contributions to vLLM, TensorRT-LLM, DeepSpeed, or similar projects. - Familiarity with custom kernel authoring in Triton or CUTLASS. - Experience with FinOps for AI workloads. - Publications or talks on AI systems performance. How to Apply Would you like to know more about this opportunity? For immediate consideration, please send your resume to [email protected] . Equal Employment Opportunity (EEO) Statement Bright Vision Technologies (BV Teck) is committed to equal employment opportunity (EEO) for all employees and applicants without regard to race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, veteran status, or any other protected status as defined by applicable federal, state, or local laws. This commitment extends to all aspects of employment, including recruitment, hiring, training, compensation, promotion, transfer, leaves of absence, termination, layoffs, and recall. - BV Teck expressly prohibits any form of workplace harassment or discrimination. - Any improper interference with employees' ability to perform their job duties may result in disciplinary action up to and including termination of employment.

• Develop, train, deploy, and maintain machine learning and AI solutions focused on Natural Language Processing (NLP), Large Language Models (LLMs), generative AI, and other advanced AI use cases • Design and implement generative AI solutions, including LLM APIs and machine learning techniques • Research and improve machine translation (MT) quality • Manage and improve data quality and integrity • Develop automated anomaly detection and monitoring solutions • Design and propose technical architectures for NLP, ML, and AI solutions • Implement MLOps and AI governance practices • Communicate technical concepts and drive innovation