• Design, build, and maintain scalable MLOps infrastructure for machine learning and Generative AI applications. • Develop automated training, validation, testing, deployment, and CI/CD pipelines for machine learning models. • Implement experiment tracking, model versioning, model registries, and artifact management using MLOps best practices. • Build and maintain workflow orchestration, feature engineering, and data processing pipelines. • Monitor production ML systems, including model performance, data quality, drift detection, latency, and overall system health. • Manage the end-to-end model lifecycle, including retraining, rollback, reproducibility, governance, and auditability. • Containerize ML workloads with Docker and deploy scalable services using cloud-native technologies and orchestration platforms. • Develop and maintain Infrastructure as Code (IaC) for AI platforms and cloud resources. • Collaborate with Data Scientists and software engineers to productionize, optimize, and scale machine learning solutions. • Evaluate and implement new MLOps tools, frameworks, and best practices, including support for LLM and agentic AI applications.

Role Description We are looking for an AI Learning Systems Engineer 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) 505-3544. 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.

At Dragos, the mission is personal. The systems we protect deliver the water you drink, power your home, and keep the hospitals your community depends on running. Those critical infrastructure systems that power our civilization around the world are under attack every day by adversaries. When those systems fail, people are immediately at risk. We are the global leader in xOT cybersecurity, combining technology, threat intelligence, and expert services. The people here chose this work because they understand what is at stake. Here, you will find a remote-first mission-driven team across North America, Europe, the Middle East, and APAC built on authenticity, transparency, and trust. If safeguarding the systems that protect your family, friends, and community is the kind of work that matters to you, you are in the right place. About the Role:  We're seeking an experienced Staff Machine Learning Engineer to join our Engineering team. In this role, you'll drive the design and implementation of production machine learning systems within the Dragos platform. Working closely with Data Engineers and product teams, you'll build and deploy AI/ML capabilities that enhance threat detection, automate security analysis, and deliver actionable intelligence for Industrial Control System (ICS) and Extended Operational Technology (xOT) cybersecurity applications.  Responsibilities:  - Design and implement production-grade machine learning systems that expand Dragos product capabilities, with consideration for both cloud and resource-constrained on-premises environments.  - Build and optimize ML model architectures for ICS/xOT cybersecurity use cases, including threat detection, asset classification, behavioral analysis, anomaly detection, and natural language processing systems. - Develop robust data pipelines and ML workflows that integrate with existing data infrastructure, supporting both real-time and batch processing requirements. - Collaborate with OT detection experts to translate research concepts and prototypes into scalable, production-ready ML systems. - Partner with Data Engineers to establish data contracts and implement observability frameworks for ML pipelines, including monitoring, versioning, and deployment best practices. - Contribute to ML infrastructure improvements, including automated testing frameworks, CI/CD pipelines, and deployment strategies for containerized environments (Kubernetes, Docker). - Evaluate and adapt state-of-the-art ML research and open-source models to domain-specific cybersecurity applications. - Troubleshoot and optimize ML model performance in production environments, addressing issues related to latency, accuracy, and resource utilization.  Qualifications:  - 6+ years of engineering experience with at least 4 years focused on machine learning implementations in production environments. - Strong software engineering foundation with expertise in Python and SQL as well as experience with at least one additional language (Go, Rust, Java, or JVM-family languages). - Demonstrated experience building and deploying ML systems using modern frameworks and libraries (scikit-learn, PyTorch, TensorFlow, HuggingFace, or similar). - Experience with LLMs, retrieval-augmented generation (RAG), or advanced NLP techniques is beneficial. - Proven track record implementing ML solutions such as classification systems, time series analysis, anomaly detection, or NLP applications that deliver measurable business impact. - Experience with MLOps practices, including model versioning, monitoring, pipeline orchestration, and deployment in high-reliability environments. - Familiarity with data engineering concepts, including data pipelines, stream processing, message queuing, and working with medium-to-large scale datasets. - Knowledge of containerized deployment solutions and cloud-native architectures. - Strong communication skills with the ability to explain technical concepts to diverse stakeholders and collaborate effectively across teams. - Cybersecurity domain knowledge, particularly in threat detection, threat intelligence, or ICS/OT operations. Compensation: - Salary: $190,000.00 - Competitive Equity Package - Comprehensive Benefits Plan #LI-JF1 #LI-REMOTE #LI-NH1 #LI-REMOTE Dragos is an Equal Opportunity Employer and considers applicants for employment without regard to race, color, religion, sex, orientation, national origin, age, disability, genetics, or any other basis forbidden under federal, state, or local laws. All new hires must pass a background check as a condition of employment.

• Design and operate the pipelines that give Vibe a coherent view of users across every data source. • Own and improve the clustering algorithm that turns raw events into coherent entities like users and households. • Anticipate scaling pain before it becomes an incident, as log volumes and advertiser usage keep growing. • Run projects end-to-end, from stakeholder requirements through design, implementation, and post-launch iteration. • Build platform-level identity-resolution tooling so every team can resolve identity for the events they own. • Choose architectures that scale economically and quantify the cost impact of those choices. • Shape and execute the roadmap for Vibe's identity stack. • Surface tradeoffs, risks, and decisions to stakeholders without burying them in implementation detail. • Translate ambiguous requests from Product, Sales, ML, and Finance into concrete deliverables.