Senior AI/Machine Learning Engineer

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August 16

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Manila Recruitment

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Recruitment • Enterprise

Manila Recruitment is a leading recruitment agency based in the Philippines, specializing in innovative talent sourcing and headhunting services. The agency provides a wide range of recruitment solutions including executive search, IT recruitment, offshore staffing solutions, remote staffing, and more. With a focus on understanding clients' strategic business objectives, Manila Recruitment offers tailored recruitment strategies for multinationals, corporations, and start-ups entering the Filipino market. The firm prides itself on a process-driven approach, offering comprehensive candidate guarantees and a database of over 250,000 candidates. Certified headhunters at Manila Recruitment are dedicated to passive candidate sourcing, ensuring quality hires that align with company culture and requirements.

11 - 50 employees

Founded 2010

🎯 Recruiter

🏢 Enterprise

📋 Description

• Design and implement agent evaluation pipelines that benchmark AI capabilities across real-world enterprise use cases • Build domain-specific benchmarks for product support, engineering ops, GTM insights, and other verticals relevant to modern SaaS • Develop performance benchmarks that measure and optimize latency, safety, cost-efficiency, and user-perceived quality • Create search- and retrieval-oriented benchmarks, including multilingual query handling, annotation-aware scoring, and context relevance • Partner with AI and infra teams to instrument models and agents with detailed telemetry for outcome-based evaluation (Member of Technical Staff: AI Performance1) • Drive human-in-the-loop and programmatic testing methodologies for fuzzy metrics like helpfulness, intent alignment, and resolution effectiveness • Contribute to company’s open evaluation tooling and benchmarking frameworks, shaping how the broader ecosystem thinks about SaaS AI performance

🎯 Requirements

• 3 to 7 years of experience in systems, infra, or performance engineering roles with strong ownership of metrics and benchmarking • Fluency in Python and comfort working across full-stack and backend services • Experience building or using LLMs, vector-based search, or agentic frameworks in production environments • Familiarity with LLM model serving infrastructure (e.g., vLLM, Triton, Ray, or custom -Kubernetes-based deployments), including observability, autoscaling, and token streaming • Experience working with model tuning workflows, including prompt engineering, fine-tuning (e.g., LoRA, DPO, or evaluation loops for post-training optimization) • Deep appreciation for measuring what matters — whether itʼs latency under load, degradation in retrieval precision, or regression in AI output quality • Familiarity with evaluation techniques in NLP, information retrieval, or human-centered AI (e.g. RAGAS, Recall@K, BLEU, etc.) • Strong product and user intuition — you care about what the benchmark represents, not just what it measures • Experience contributing to academic or open-source benchmarking projects