Algorithm Engineering Manager

该职位来源于猎聘 Accountabilities

• Own system-level AI/ML (Artificial Intelligence/Machine Learning) architecture and technical governance for safety and performance-critical intelligent device capabilities.
• Take explicit accountability for algorithmic risk, including model failures, edge cases, data drift, hardware variability, and unseen operating conditions.
• Define safety margins, operating envelopes, fallback strategies, and safe-state behaviors in collaboration with firmware, hardware, quality, and regulatory teams.
• Architect and govern real-time, closed-loop AI/ML systems integrated with firmware control logic and device physics.
• Lead portfolio-level technical alignment across multiple interdependent AI/ML initiatives, ensuring coherent data definitions, shared signals, interfaces, and evolution paths.
• Make and communicate system trade-off decisions (e.g. safety vs. experience, accuracy vs. power, edge vs. cloud) with clear rationale and accountability.
• Act as the primary AI/ML technical authority in cross-functional design, risk, and architecture forums, including the ability to challenge requirements that introduce unacceptable technical or safety risk.
• Define long-term AI/ML platform strategy rather than point solutions, ensuring reuse, scalability, and long-term maintainability across product generations.
• A recognized SME in the rapidly evolving AI & Machine Learning space, driving technical excellence while playing a pivotal role in knowledge sharing, coaching and building capability amongst R&D engineers and cross functional teams.

Experience, Skills, Knowledge Essential Experience Required

• Minimum 10+ years of experience in AI/ML or algorithm engineering, with demonstrated ownership of system-critical or safety-relevant intelligent systems in production.
• Proven track record of end-to-end accountability for AI/ML systems across data strategy, modelling, model deployment into embedded system, in-field performance, and lifecycle evolution.
• Demonstrated experience operating in high-uncertainty problem spaces in real-world physics modelling where no established solution or industry template exists.
• Experience defining system architectures that tightly integrate algorithms with firmware control loops, hardware behavior, and real-world physical constraints.
• Evidence of acting as a technical authority beyond a single team, influencing architecture, trade-offs, and risk decisions across functions Technical / Functional / Leadership Skills Required
• Demonstrated full chain experiences in AI/ML industry – from backend computing cluster architecture, to frontend real-world device problem solving.
• Deep expertise in AI/ML with systems architecture across algorithms, firmware, hardware, data, and user experience. Experienced in defining computing architecture connecting device local (edge) and the could AI models
• Mastering in data feature engineering, data cleansing leading to AI/ML model construction, and model selections that optimized for model accuracy and HW/SW constrain. Strong architectural judgment to define AI/ML model capability boundaries and balanced system constrain, and where firmware must retain final execution authority.
• Ability to reason about and design for failure modes, degradation, uncertainty, and worst-case scenarios, not just nominal model performance.
• Solid track records in driving collaboration with partners in the AI/ML ecosystem, and deliver tangible project results.
• Credibility and confidence to challenge product or engineering requirements that introduce unacceptable technical, safety, or regulatory risk.
• Leadership in driving AI/ML learning capabilities within an organization, and champion for influencing/teaching AI/ML within the BAT RD community
• Excellence learning capabilities for new physical problems in NC devices and formulate AI/ML models projects
• Excellent communication skills to articulate complex technical problems to senior stakeholders in a clear, decision-oriented manner. Education / Qualifications / Certifications Required
• Master’s degree or PhD preferred in Computer Science, Electrical Engineering, Physics, Applied Mathematics, Data Science or related field from a top-tier university (QS Top 100 or China 985/211 universities).
• Publications or patents in AI/ML domains are a plus.
• Interdisciplinary backgrounds combining AI/ML with embedded systems, consumer electronics, or device platforms are strongly preferred.
• Excellent English/Mandarin proficiency. Oversea profession experiences is preferred.