• Lead end-to-end machine learning initiatives focused on improving player engagement and retention, from initial concept through production deployment. • Build scalable, reusable machine learning pipelines with a focus on reliability, maintainability, and performance. • Design and manage CI/CD workflows for machine learning using tools like MLflow, Jenkins, and GitOps to enable automated and efficient model deployment. • Monitor model performance in production, implementing retraining strategies, drift detection, and continuous optimization. • Partner with cross-functional teams to translate business goals and user insights into high-impact machine learning solutions. • Mentor other engineers and help define best practices for machine learning system design, development, and deployment.

• Develop and fine-tune large language models (LLMs) for intelligent, safe, and responsive browser interactions. • Apply retrieval-augmented generation (RAG), summarization, classification, and intent modeling techniques in real-world browser workflows. • Use modern ML tooling (e.g., Hugging Face, LangChain, Weights & Biases, Ray, or similar) to support end-to-end model development, including experimentation, evaluation, and rollout. • Collaborate closely with product and engineering partners to design, iterate, and launch features that align with user needs and Mozilla’s values. • Design and implement experiments to test model behavior in production, analyze results, and iterate based on findings. • Document and share your work, participating in code reviews and team design discussions.

• Grow out and lead a team of Deep Learning engineers • Steward the end-to-end planning, execution, and delivery of a scoped machine learning applied science agenda • Act as the primary point of contact with infrastructure and data pipeline teams • Exhibit strong judgement in derisking core hypotheses and updating research direction informed by ongoing advances in the state of the art

Role Description We are looking for a Reinforcement Learning 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. Requirements - 6+ years of experience. - 100% Remote (Continental United States). - Full-time, direct W2 with Bright Vision Technologies (no C2C, no 1099, no third-party). - No new H1B sponsorship available; H1B transfers welcomed for qualified candidates. Benefits - Competitive base salary commensurate with experience, plus benefits.