Senior Engineering Manager, Machine Learning
Leads the Machine Learning Engineering team, setting technical strategy, developing engineers, and shipping reliable ML and agentic AI systems for product intelligence, fraud detection, and workforce integrity. Requires 10+ years building production software and ML/AI systems plus substantial engineering management experience.
About the job
Responsibilities
- Lead and grow the Machine Learning Engineering team by hiring, coaching, developing engineers, and building technical leadership.
- Set a focused, multi-quarter ML strategy and roadmap tied to customer outcomes, revenue, accuracy, reliability, and cost.
- Establish production engineering standards for models and agents, including APIs, testing, CI/CD, observability, on-call ownership, and reliability targets.
- Develop operating practices for evaluation, golden sets, training-data provenance, model and prompt versioning, monitoring, retraining, latency, and cost.
- Guide product intelligence systems for classification, information extraction, entity resolution, profile integrity, and measurable production accuracy.
- Launch AI and fraud products using identity, resume, device, and employment signals.
- Lead the design of agentic AI systems combining specialized models, LLMs, tools, and governed knowledge.
- Partner with Product, Engineering, Operations, Legal, Security, go-to-market teams, and company leadership.
- Communicate technical decisions and risks clearly to senior stakeholders and drive cross-functional decisions.
Requirements
- 10+ years building software and machine learning or AI systems, with increasing scope and impact.
- 3+ years managing ML or software engineers, including hiring and developing senior and staff-level technical leaders.
- Strong software engineering foundation and experience shipping production ML systems.
- Technical depth across data and labeling, experimentation, model selection, deployment, APIs, CI/CD, observability, evaluation, retraining, and incident response.
- Experience across classical ML, deep learning, LLMs, rules, and conventional software.
- Experience setting direction for NLP, classification, extraction, entity resolution, risk, fraud, recommendation, or similar applied-ML domains.
- Experience with modern LLM systems, including structured outputs, tool use, agent orchestration, retrieval, evaluation, latency, quality, and cost trade-offs.
- Ability to translate ambiguous business problems into roadmaps, measurable outcomes, and shipped systems.
- Strong people leadership, delegation, feedback, coaching, and cross-functional influence.
- Executive-level communication and sound technical judgment.
Nice to Have
- Experience building ML infrastructure or MLOps platforms used by multiple teams.
- Experience with document intelligence, OCR, graph systems, identity resolution, trust and safety, fraud, or risk.
- Experience operating ML in compliance-sensitive domains such as fintech, legal technology, HR technology, healthcare, or security.
- Python, AWS, SageMaker, Snowflake, Spark, and modern data tooling.
- Experience launching an AI product from customer discovery through production scale.
Compensation and Benefits
- On-target earnings or base salary range: $268,000–$315,000 USD.
- Competitive cash and equity compensation.
- Learning and development allowance.
- Medical, dental, and vision coverage.
- Up to $25,000 reimbursement for fertility, adoption, and parental planning services.
- Flexible paid time off.
- Monthly wellness stipend.
- In-office perks, including meals, commuter stipend, and snacks and beverages.
Skills
Machine Learning, Python, AWS, Amazon Sagemaker, Snowflake, Spark, LLMs, NLP, MLOps, CI/CD, Observability, Agent Orchestration, Entity Resolution, Ocr, Fraud Detection
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