Principal Machine Learning Engineer
Posted Today by Cloud Destinations LLC
Negotiable
Undetermined
Remote
Remote
Summary: The Principal Machine Learning Engineer will architect multi-model systems for matchmaking by integrating skill, preference, trust, and safety signals. This role involves developing models for skill inference and player behavior prediction, as well as optimizing real-time inference systems at a global scale. The engineer will also drive the adoption of advanced modeling approaches and define Responsible AI standards. Collaboration with Data Engineering and Product teams is essential to shape data schemas and ensure model observability throughout the ML lifecycle.
Key Responsibilities:
- Architect multi-model systems combining skill, preference, trust, and safety signals for fair and meaningful matchmaking
- Develop models for skill inference, player behavior prediction, trust & safety signals, and multi-objective optimization across fairness, latency, and experience quality.
- Build and optimize real-time inference systems for personalized content, store offers, matchmaking, and player interactions at global scale.
- Drive adoption of advanced modeling approaches including contextual bandits, reinforcement learning, graph ML, and session-aware personalization.
- Partner with Data Engineering and Product to shape data schemas, feature pipelines, telemetry standards, and model observability across the ML lifecycle.
- Define Responsible AI standards and implement fairness audits, bias mitigation, transparency, and safety mechanisms for matchmaking and social systems.
- Lead post-launch evaluations of algorithmic impact on player sentiment, community health, and ecosystem stability.
- Set organization-wide standards for model optimization (latency, throughput, memory), multi-model orchestration, and drift detection.
Key Skills:
- 10+ years in ML/Applied AI; 3+ years in principal/staff-level technical leadership.
- Experience with large-scale, real-time ML systems (recommendations, personalization, matchmaking).
- Expertise in graph ML, RL, and representation learning.
- Proficiency in PyTorch, TensorFlow, JAX, and modern data/serving tools (Ray, Kafka, Flink, Redis).
- Strong grounding in A/B testing, experiment design, and experience metrics.
- Track record of setting ML strategy and standards across teams.
Salary (Rate): £60,000 yearly
City: undetermined
Country: undetermined
Working Arrangements: remote
IR35 Status: undetermined
Seniority Level: undetermined
Industry: IT
Detailed Description From Employer:
Job Description:
- Architect multi-model systems combining skill, preference, trust, and safety signals for fair and meaningful matchmaking
- Develop models for skill inference, player behavior prediction, trust & safety signals, and multi-objective optimization across fairness, latency, and experience quality.
- Build and optimize real-time inference systems for personalized content, store offers, matchmaking, and player interactions at global scale.
- Drive adoption of advanced modeling approaches including contextual bandits, reinforcement learning, graph ML, and session-aware personalization.
- Partner with Data Engineering and Product to shape data schemas, feature pipelines, telemetry standards, and model observability across the ML lifecycle.
- Define Responsible AI standards and implement fairness audits, bias mitigation, transparency, and safety mechanisms for matchmaking and social systems.
- Lead post-launch evaluations of algorithmic impact on player sentiment, community health, and ecosystem stability.
- Set organization-wide standards for model optimization (latency, throughput, memory), multi-model orchestration, and drift detection.
Required Qualifications:
- 10+ years in ML/Applied AI; 3+ years in principal/staff-level technical leadership.
- Experience with large-scale, real-time ML systems (recommendations, personalization, matchmaking).
- Expertise in graph ML, RL, and representation learning.
- Proficiency in PyTorch, TensorFlow, JAX, and modern data/serving tools (Ray, Kafka, Flink, Redis).
- Strong grounding in A/B testing, experiment design, and experience metrics.
- Track record of setting ML strategy and standards across teams.
Desired Qualifications:
- Professional background in gaming
- Familiarity with Vertex AI, SageMaker, or internal large-scale inference systems.