Senior Principal AI Engineer
Posted Today by Sriven Systems Inc.
Negotiable
Undetermined
Remote
Remote
Summary: The role of Principal Data Scientist (ML Systems) involves architecting multi-model systems for matchmaking, developing predictive models, and optimizing real-time inference systems at a global scale. The position requires collaboration with Data Engineering and Product teams to establish data standards and implement Responsible AI practices. The candidate will also lead evaluations of algorithmic impacts and set organization-wide optimization standards. This is a senior-level position requiring extensive experience in machine learning and technical leadership.
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: Senior
Industry: IT
Role:Principal Data Scientist (ML Systems)
Location:Remote
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.
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.