Remote Senior Machine Learning Engineer

Description:

• The Senior Machine Learning Engineer will own the end-to-end lifecycle of models, which includes problem framing, data handling, training, evaluation, deployment, and monitoring.
• Responsibilities include designing features and labeling strategies, as well as improving data quality using heuristic and programmatic techniques such as weak supervision, active learning, and synthetic data generation via LLMs.
• The role involves training, tuning, and comparing various models including tree-based, linear/GLM, deep learning, seq2seq, recommendation, and LLMs using reproducible pipelines.
• The engineer will build and tune appropriate information retrieval for RAG use cases, engineering the right context for tasks.
• Implementing rigorous offline metrics and online A/B experiments is required, along with defining guardrails and SLOs for quality, latency, and cost.
• The position also entails developing efficient API endpoints for model inference, ensuring they are scalable and production-ready, and meeting product requirements through practices like containerization, load balancing, auto-scaling, monitoring, logging, alerting, and incident response.

Requirements:

• Candidates must have experience with AI developer productivity tools such as Windsurf, Cursor, and prompt tuning, and the ability to apply AI techniques to practical engineering problems.
• A minimum of 5 years of hands-on experience in ML/AI engineering is required, with a strong focus on building and deploying AI/GenAI applications.
• Proficiency in Python, particularly with ML libraries and GenAI/LLM frameworks, as well as Java for enterprise application development and OOP is necessary.
• Practical experience with LLM frameworks such as LangChain, LangGraph, and vendor SDKs/APIs (OpenAI, Anthropic, etc.) is essential.
• Experience in training and fine-tuning large language models using methods like distillation, supervised fine-tuning, and policy optimization is a plus.
• Strong skills in prompt engineering and designing agentic reasoning pipelines are required.
• A solid understanding of ML evaluation techniques and experience in implementing model monitoring and metrics is necessary.
• Proven ability to debug and optimize inference pipelines for performance and cost efficiency is essential.
• Strong problem-solving skills and the ability to work in fast-paced, agile development environments are required.
• Contributions to open-source projects, blogs, or technical papers in LLM/GenAI are considered a bonus.

Benefits:

• ServiceNow offers a flexible work environment, allowing for remote, flexible, or in-office work personas based on the nature of the job and employee location.
• The company is committed to creating an accessible and inclusive experience for all candidates, providing reasonable accommodations during the application process as needed.
• ServiceNow is an equal opportunity employer, ensuring that all qualified applicants receive consideration for employment without discrimination based on various protected categories.
• Employment is contingent upon obtaining any necessary export control approvals for positions requiring access to controlled technology.