Machine Learning Engineer (Evals and Voice Models)

Aircall is a unicorn, AI-powered customer communications platform used by 22,000+ companies worldwide to drive revenue, resolve issues faster, and scale customer-facing teams. We’re redefining customer communications by bringing voice, SMS, WhatsApp, and AI together into one seamless workspace.

Our momentum comes from a simple idea: help teams work smarter, not harder. Aircall’s AI Voice Agent automates routine calls, AI Assist streamlines post-call work, and AI Assist Pro delivers real-time guidance so people can do their best work. The result is higher revenue, faster resolutions, and teams that scale with confidence.

Aircall is headquartered in Paris, our European HQ, with a strong North American presence anchored in Seattle, our North American HQ, and teams across Madrid, London, Berlin, San Francisco, New York City, Sydney, and Mexico City. We’ve built a product customers love and a business that’s scaling quickly, backed by world-class investors and driven by rapid AI innovation across multiple product lines.

At Aircall, you’ll join a company in motion. We’re ambitious, product-driven, and execution-focused, with visible impact, fast decisions, and real growth.

How we work at Aircall: We’re customer-obsessed, data-driven, and focused on delivering meaningful outcomes. We value ownership, continuous learning, and thoughtful speed. If you thrive in a collaborative, fast-moving environment where trust and impact matter, you’ll feel at home here.

Aircall's AI suite includes an AI Voice Agent and AI Messaging Agent that autonomously handle calls, WhatsApp, and SMS, plus AI Assist, which delivers real-time coaching, call summaries, and CRM automation for sales and support teams. We are looking for someone that can build out the evaluation foundation across all of these products and other agentic products. You'll work on voice models, agent capability evals, benchmark design, LLM-as-judge systems, failure analysis, and the infrastructure that ties it together by establishing shared metrics, test sets, and tooling to measure accuracy, resolution quality, and safety consistently across products. You will set up repeatable pipelines for regression testing and benchmarking as models and features evolve so teams can ship confidently without re-inventing evaluation methodology for each product.

Key Responsibilities

• Design and document comprehensive evaluation frameworks for Aircall’s AI agents across voice, chat and messaging.
• Train and fine-tune voice models (TTS, ASR, speech-to-speech) using production and synthetic data, iterating on architecture, data mix, and training strategy to improve accuracy, naturalness, and latency.
• Assess AI generated solutions across training pipelines, experimentation setups, debugging processes, and optimization strategies. 
• Analyze system design decisions and identify strengths, weaknesses, and potential failure points.
• Design annotation guidelines and workflows for human-labeled evaluation data, and calibrate LLM-as-judge systems against human raters to ensure automated evals stay trustworthy over time.
• Build and maintain live quality monitoring for deployed AI agents, tracking accuracy, resolution rate, and safety signals in production, and flagging model or data drift before it impacts customers.
• Own the metric contract for every published AI metrics, including definition, population, grain, rollup, validity window.
• Build release gates, the offline regression suite each AI surface must pass before a prompt, model, or config change ships, measuring reliability across repeated trials, not just average pass rates.
• Build voice-specific evaluation: simulated callers across accents, languages, background noise, barge-in, DTMF, and tool failures, with latency and ASR accuracy as first-class quality metrics.

Minimum Qualifications

• BS in Computer Science, Machine Learning, Statistics, or related field
• 3+ years of experience in ML Engineering or Applied ML with 8+ years of overall experience
• Strong experience in evaluating supervised, unsupervised, LLMs and deep learning models.
• Hands-on experience in failure analysis and evaluating LLMs
• Experience building automated evaluation systems
• Strong communication skills to articulate complex technical concepts across technical and non-technical audiences
• Hands-on experience training or fine-tuning voice/speech models (TTS, ASR, or speech-to-speech), including data pipeline construction and experimentation.

Preferred Qualifications

• MS / PhD in Computer Science, Machine Learning, Statistics, or related field
• Experience evaluating LLMs or agentic systems (e.g., LLM-as-a-judge, RAG evaluation)
• Experience with synthetic data generation and prompt engineering
• Experience training or fine-tuning voice models at scale, with familiarity in synthetic data generation, model distillation, or low-latency inference optimization for production voice agents.