You will develop and improve machine learning systems that power voice experiences end to end, including automatic speech recognition, turn detection, downstream language understanding, retrieval-augmented and agentic workflows, quality measurement, text to speech, and production optimization.

Responsibilities:

• Design, train, evaluate, and deploy machine learning systems that power real-time voice experiences, including ASR, speech understanding, turn detection, text to speech, speech to speech, classification, entity extraction, summarization, and structured insight generation.
• Improve the quality of voice AI systems through error analysis, data curation, metric design, benchmarking, and iterative model improvement, with a strong focus on real-world performance.
• Build evaluation frameworks for complex voice and agentic systems, measuring metrics such as accuracy, robustness, latency, faithfulness, naturalness, professionalism, task completion, and cost.
• Diagnose and mitigate failure modes across the voice stack, including transcription errors, hallucinations, retrieval failures, tool misuse, prompt brittleness, context drift, and multi-step reasoning breakdowns.
• Design and optimize low-latency ML workflows for live conversations, balancing model quality with system responsiveness, scalability, and reliability.
• Partner with platform and backend engineers to productionize real-time inference, streaming pipelines, quality monitoring, and continuous model iteration.
• Collaborate cross-functionally with product, design, frontend, and backend teams to integrate voice intelligence seamlessly into Cresta’s platform.
• Establish best practices for offline evaluation, online experimentation, model validation, observability, and ongoing quality monitoring in production.
• Mentor engineers, contribute to technical strategy, and help shape the roadmap for Cresta’s voice AI systems.

Qualifications:

• Bachelor’s degree in Computer Science, Mathematics, Machine Learning, AI, or a related field; Master’s or Ph.D. preferred.
• 5+ years of experience building, evaluating, and deploying machine learning systems in production.
• Strong background in one or more of the following: speech recognition, speech processing, NLP, generative AI, or conversational AI.
• Deep experience with model evaluation, benchmarking, error analysis, and quality improvement for production ML systems.
• Strong expertise with modern ML frameworks and tooling such as PyTorch, TensorFlow, and Hugging Face.
• Solid understanding of transformer-based models, embeddings, retrieval systems, and large-scale training or inference workflows.
• Experience designing and deploying real-time ML systems with strong requirements around latency, scalability, and reliability.
• Experience building data pipelines and tooling for experimentation, measurement, and large-scale quality analysis.
• Ability to work across research and engineering boundaries and translate promising ideas into production-grade systems.
• Strong communication and technical leadership skills, with the ability to influence cross-functional decisions and raise the engineering bar.

Nice to have:

• Hands-on experience with ASR quality metrics such as WER and task-level evaluation methodologies.
• Experience with RAG systems, agentic workflows, multi-step reasoning systems, or LLM-as-a-judge evaluation methods.
• Familiarity with streaming inference, real-time voice pipelines, or media systems.
• Experience working closely with infrastructure or platform teams on production ML deployment, observability, and reliability.
• Experience in contact center AI, conversational intelligence, or enterprise voice products.

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You will combine deep machine learning expertise, solid engineering execution, and business intuition to modernize our prediction stack, drive model innovation, and mentor a growing team of scientists and engineers. This role is ideal for someone who thrives in complex, high-scale systems, who brings thought leadership to ML strategy, and who raises the bar for engineering rigor, curiosity, and business-driven decision making across the team.

Microsoft’s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.

Starting January 26, 2026, Microsoft AI (MAI) employees who live within a 50- mile commute of a designated Microsoft office in the U.S. or 25-mile commute of a non-U.S., country-specific location are expected to work from the office at least four days per week. This expectation is subject to local law and may vary by jurisdiction.

Responsibilities:

ML / Modeling Leadership

Lead the end-to-end development of large-scale CTR and other user response signal models for Search and Display ads.

Design, prototype, and ship cutting-edge ML architectures (deep models, multi-task, transformer-based, LLM-assisted, multimodal).

Define long-term modeling strategy and roadmap with clear business impact.

Technical & Engineering Execution

Modernize our current modeling pipelines, addressing critical technical debt in data flows, training pipelines, and inference systems.

Partner closely with engineering teams to improve reliability, monitoring, and performance of distributed training and online serving.

Introduce best practices for experiment design, ablations, feature validation, and productionization.

Business & Product Impact

Work with PMs, monetization teams, and auction experts to translate business needs into modeling goals.

Own model performance holistically: quality, stability, latency, and revenue impact.

Develop frameworks to better understand advertiser value, user behavior, and marketplace dynamics.

Leadership & Mentorship

Mentor and up-level applied scientists and ML engineers across the organization.

Drive a culture of curiosity, deep system understanding, and high-quality scientific reasoning.

Improve collaboration norms, documentation quality, and cross-team alignment.

Innovation & Tooling

Leverage and influence LLM-based tooling (e.g., agents, copilots) to improve team productivity and model development velocity.

Identify opportunities to incorporate new modeling signals, architectures, or evaluation metrics.

Qualifications

Required/minimum qualifications

Bachelor’s Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience (e.g., statistics, predictive analytics, research)

OR Master’s Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 4+ years related experience (e.g., statistics, predictive analytics, research)

OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research)

OR equivalent experience.

Additional or preferred qualifications

Master’s Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 9+ years related experience (e.g., statistics, predictive analytics, research)

OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience (e.g., statistics, predictive analytics, research)

OR equivalent experience.

5+ years experience creating publications (e.g., patents, libraries, peer-reviewed academic papers).

2+ years experience presenting at conferences or other events in the outside research/industry community as an invited speaker.

5+ years experience conducting research as part of a research program (in academic or industry settings).

3+ years experience developing and deploying live production systems, as part of a product team.

3+ years experience developing and deploying products or systems at multiple points in the product cycle from ideation to shipping.

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You will combine deep machine learning expertise, solid engineering execution, and business intuition to modernize our prediction stack, drive model innovation, and mentor a growing team of scientists and engineers. This role is ideal for someone who thrives in complex, high-scale systems, who brings thought leadership to ML strategy, and who raises the bar for engineering rigor, curiosity, and business-driven decision making across the team.

Microsoft’s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.

Responsibilities:

ML / Modeling Leadership

• Lead the end-to-end development of large-scale CTR and other user response signal models for Search and Display ads.

• Design, prototype, and ship cutting-edge ML architectures (deep models, multi-task, transformer-based, LLM-assisted, multimodal).

• Define long-term modeling strategy and roadmap with clear business impact.

Technical & Engineering Execution

• Modernize our current modeling pipelines, addressing critical technical debt in data flows, training pipelines, and inference systems.

• Partner closely with engineering teams to improve reliability, monitoring, and performance of distributed training and online serving.

• Introduce best practices for experiment design, ablations, feature validation, and productionization.

Business & Product Impact

• Work with PMs, monetization teams, and auction experts to translate business needs into modeling goals.

• Own model performance holistically: quality, stability, latency, and revenue impact.

• Develop frameworks to better understand advertiser value, user behavior, and marketplace dynamics.

Leadership & Mentorship

• Mentor and up-level applied scientists and ML engineers across the organization.

• Drive a culture of curiosity, deep system understanding, and high-quality scientific reasoning.

• Improve collaboration norms, documentation quality, and cross-team alignment.

Innovation & Tooling

• Leverage and influence LLM-based tooling (e.g., agents, copilots) to improve team productivity and model development velocity.

• Identify opportunities to incorporate new modeling signals, architectures, or evaluation metrics.

Qualifications

• Bachelor’s Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience (e.g., statistics, predictive analytics, research)

• Master’s Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 4+ years related experience (e.g., statistics, predictive analytics, research)

• Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research)

• Equivalent experience.

Additional or preferred qualifications:

• Master’s Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 9+ years related experience (e.g., statistics, predictive analytics, research)

• Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience (e.g., statistics, predictive analytics, research)

• Equivalent experience.

• 5+ years experience creating publications (e.g., patents, libraries, peer-reviewed academic papers).

• 2+ years experience presenting at conferences or other events in the outside research/industry community as an invited speaker.

• 5+ years experience conducting research as part of a research program (in academic or industry settings).

• 3+ years experience developing and deploying live production systems, as part of a product team.

• 3+ years experience developing and deploying products or systems at multiple points in the product cycle from ideation to shipping.

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