• Play a critical part in designing, building, and deploying backend generative AI solutions that power automated, intelligent workflows. • Take ambiguous, complex R&D problems and convert them into production-ready software, ensuring our models are robust, scalable, and secure. • Shape our machine learning architecture, ensuring the team maintains absolute ownership of the production-grade code that moves into deployment. • Champion technical excellence and drive clarity across teams to elevate technical capabilities. • Collaborate closely with applied scientists and engineers on cutting-edge Agentic AI implementations within an agile framework. • Focus on building AI-native user experiences and driving technical clarity in a supportive environment.

• Develop scalable Python services and production-grade APIs • Design, implement, and improve AI features using LLMs and modern ML techniques • Build specialized edge models • Create unique labeling strategies and coach labeling of data to support model building. • Take AI systems from concept and prototype to reliable, observable, production deployments • Collaborate closely with product, founders, and stakeholders to shape product direction • Continuously improve system architecture, data pipelines, and ML/AI infrastructure

• Build LLM-based applications. • Develop conversational systems and semantic search and integrate them into the domain-specific applications of the industry solution. • Set up and maintain RAG (Retrieval-Augmented Generation) systems. • Including knowledge-base management: indexing, updates, and clean, source-separated storage of structured and unstructured content. • Operation and monitoring. • MLOps in production: observability, structured logging, and error analysis. • Ensure systems run reliably in production, not just that they worked once. • Deploy agents into production. • From development through integration and connection to portal systems to stable delivery. • Improve solutions based on data. • Use monitoring data, tests, and user feedback to iteratively improve solutions. • Experiment with new approaches. • Identify new AI use cases, prototype them, and build data pipelines from preprocessing and model development to production.

Role Description We are looking for a Machine Learning Engineer - RL to design, train, and deploy RL-based systems for high-impact decision-making problems where supervised learning alone is insufficient. The role requires deep familiarity with modern reinforcement learning algorithms, simulation environments, reward modeling, and the engineering complexity of training and evaluating policies at scale. The ideal candidate has both research depth and engineering pragmatism, with experience taking RL solutions out of the lab and into production where stability, safety, and ongoing improvement are critical. Key Responsibilities - Design and implement reinforcement learning solutions for sequential decision-making problems in real and simulated environments. - Develop, calibrate, and maintain simulation environments suitable for large-scale agent training. - Implement and evaluate modern RL algorithms including policy gradient, actor-critic, off-policy, and offline RL methods. - Engineer reward functions and shaping strategies that align agent behavior with desired outcomes and safety constraints. - Apply offline RL and imitation learning techniques where exploration is costly or unsafe. - Use RLHF, DPO, and related techniques for fine-tuning large language models when relevant. - Build scalable training infrastructure for distributed RL, including efficient experience collection and replay systems. - Optimize training stability and sample efficiency through algorithmic and engineering improvements. - Design rigorous evaluation protocols, including out-of-distribution and adversarial test cases. - Implement safety mechanisms such as constraint enforcement, conservative policies, and human-in-the-loop oversight. - Collaborate with applied scientists and product teams to identify high-value RL use cases. - Monitor deployed policies and models in production for drift, regression, and unintended behaviors, building the alerting and dashboards that surface issues before they meaningfully affect users. - Document methodology, design decisions, and operational characteristics for internal stakeholders. - Stay current with RL research and translate promising techniques into production-ready solutions. Qualifications - Master’s or PhD in Computer Science, Machine Learning, or a related field; or equivalent applied experience. - Six or more years of combined RL research and engineering experience. - Strong proficiency in Python and modern deep learning frameworks. - Hands-on experience with at least one major RL library or in-house RL stack. - Solid understanding of probability, optimization, and the theoretical foundations of RL. - Experience designing and tuning reward functions in non-trivial environments. - Familiarity with simulation environments and large-scale experience collection. - Experience training neural network policies on GPU clusters. - Strong written and verbal communication skills. - Track record of shipping or publishing impactful RL work. Preferred Qualifications - Experience with RLHF for large language models. - Familiarity with multi-agent RL or hierarchical RL. - Exposure to robotics, control systems, or autonomous driving. - Publications in RL or related research venues. - Open-source contributions to RL libraries or environments. How to Apply Would you like to know more about this opportunity? For immediate consideration, please send your resume to [email protected] or contact us at (908) 676-4399. Learn more about Bright Vision Technologies at www.bvteck.com . 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.