What are the responsibilities and job description for the Manager, Applied Machine Learning Scientist position at Burtch Works?
Job Title: Manager, Applied Machine Learning Scientist
Location: Remote
About The Company
This organization is a global leader in distribution, serving millions of customers through a combination of deep customer relationships and advanced technology solutions. With a strong focus on innovation, the company is investing heavily in AI-driven product discovery and customer experience capabilities. Its teams operate at the intersection of data, machine learning, and real-world operational systems to build scalable, high-impact solutions.
Job Summary
We are seeking a Manager, Applied Machine Learning Scientist to lead the development of cutting-edge generative AI solutions supporting real-time customer service operations. This role will lead a team responsible for building AI agents that assist human agents during live customer interactions, including surfacing product information, detecting sentiment, recommending next-best actions, and automating post-call workflows. The ideal candidate will combine strong technical expertise with leadership capabilities, contributing hands-on while guiding a team in building production-grade machine learning systems. This role sits at the intersection of real-time ML inference, event-driven architectures, and contact center technologies.
Key Responsibilities
Team Leadership & Development
Build, lead, and scale a team of machine learning scientists and engineers to support AI-driven customer service initiatives.
Stakeholder Collaboration
Partner with product leaders to translate business objectives into technical roadmaps and guide team execution.
Cross-Functional Alignment
Manage relationships across software engineering, MLOps, and data engineering teams to ensure seamless delivery of ML systems.
Machine Learning Development
Design, train, and deploy machine learning models for voice-based use cases such as intent classification, sentiment detection, escalation prediction, and conversational Q&A.
Real-Time Inference Systems
Build and optimize production inference pipelines that meet strict latency requirements.
Streaming Data Pipelines
Develop event-driven data pipelines that process real-time transcription data into persistent storage systems.
Model Monitoring & Optimization
Implement monitoring frameworks, evaluation strategies, and drift detection to maintain model performance in production.
System Integration
Collaborate with engineering teams on API design, WebSocket integrations, and UI data contracts.
Continuous Learning Systems
Develop automated retraining pipelines using feedback loops and labeled datasets to improve model accuracy over time.
Requirements
Education:
Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Science, or a related technical field (or equivalent experience)
Experience
5 years of experience in Applied Machine Learning or ML Engineering, including at least 2 years deploying models into production at scale
Skills
None required
Other
Must be authorized to work in the United States without current or future sponsorship.
Preferred Qualifications
Health And Wellness
Medical, dental, vision, and life insurance starting on day one, plus access to mental health support.
Work-Life Balance
18 PTO days annually plus six company holidays.
Retirement Benefits
6% company contribution to a 401(k) plan with no employee contribution required.
Professional Development
Tuition reimbursement, financial counseling, and access to learning resources.
Additional Perks
Parental leave benefits, employee discounts, and a flexible hybrid work environment.
Location: Remote
About The Company
This organization is a global leader in distribution, serving millions of customers through a combination of deep customer relationships and advanced technology solutions. With a strong focus on innovation, the company is investing heavily in AI-driven product discovery and customer experience capabilities. Its teams operate at the intersection of data, machine learning, and real-world operational systems to build scalable, high-impact solutions.
Job Summary
We are seeking a Manager, Applied Machine Learning Scientist to lead the development of cutting-edge generative AI solutions supporting real-time customer service operations. This role will lead a team responsible for building AI agents that assist human agents during live customer interactions, including surfacing product information, detecting sentiment, recommending next-best actions, and automating post-call workflows. The ideal candidate will combine strong technical expertise with leadership capabilities, contributing hands-on while guiding a team in building production-grade machine learning systems. This role sits at the intersection of real-time ML inference, event-driven architectures, and contact center technologies.
Key Responsibilities
Team Leadership & Development
Build, lead, and scale a team of machine learning scientists and engineers to support AI-driven customer service initiatives.
Stakeholder Collaboration
Partner with product leaders to translate business objectives into technical roadmaps and guide team execution.
Cross-Functional Alignment
Manage relationships across software engineering, MLOps, and data engineering teams to ensure seamless delivery of ML systems.
Machine Learning Development
Design, train, and deploy machine learning models for voice-based use cases such as intent classification, sentiment detection, escalation prediction, and conversational Q&A.
Real-Time Inference Systems
Build and optimize production inference pipelines that meet strict latency requirements.
Streaming Data Pipelines
Develop event-driven data pipelines that process real-time transcription data into persistent storage systems.
Model Monitoring & Optimization
Implement monitoring frameworks, evaluation strategies, and drift detection to maintain model performance in production.
System Integration
Collaborate with engineering teams on API design, WebSocket integrations, and UI data contracts.
Continuous Learning Systems
Develop automated retraining pipelines using feedback loops and labeled datasets to improve model accuracy over time.
Requirements
Education:
Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Science, or a related technical field (or equivalent experience)
Experience
5 years of experience in Applied Machine Learning or ML Engineering, including at least 2 years deploying models into production at scale
Skills
- Strong Python programming skills with experience in PyTorch
- Experience fine-tuning and deploying LLMs or smaller language models with an understanding of latency and cost tradeoffs
- Experience with agentic AI frameworks such as LangChain, LangGraph, or similar orchestration tools
- Experience with production model serving technologies such as Triton Inference Server, vLLM, TensorRT, or similar systems
- Experience building real-time inference pipelines
- Strong experience with event-driven architecture (Kafka, AWS EventBridge, SQS, or similar)
- Experience building data pipelines using Spark, Airflow, or similar tools
- AWS cloud expertise including Lambda, EventBridge, SQS, ElastiCache, Aurora, and S3
- Experience with MLOps tools such as MLflow, Weights & Biases, or similar
None required
Other
Must be authorized to work in the United States without current or future sponsorship.
Preferred Qualifications
- Experience building and scaling machine learning teams
- Experience working with contact center or telephony platforms such as Genesys, Amazon Connect, Twilio, or Five9
- Experience working with speech-to-text or ASR systems and handling transcription noise
- Experience building real-time streaming machine learning systems
- Familiarity with Infrastructure-as-Code tools such as Terraform and CI/CD pipelines
- Experience with distributed training frameworks such as DeepSpeed
Health And Wellness
Medical, dental, vision, and life insurance starting on day one, plus access to mental health support.
Work-Life Balance
18 PTO days annually plus six company holidays.
Retirement Benefits
6% company contribution to a 401(k) plan with no employee contribution required.
Professional Development
Tuition reimbursement, financial counseling, and access to learning resources.
Additional Perks
Parental leave benefits, employee discounts, and a flexible hybrid work environment.
Salary : $148,900 - $248,200