Senior Machine Learning Engineer (AI)

Location: Remote or hirify.global offices in Mountain View, San Francisco, Seattle, Austin, and New York, United States. Hiring is available in countries where hirify.global has a legal entity.

Base salary in the United States: $171,063–$223,332 for Zone C, $185,490–$242,168 for Zone B, or $206,100–$269,075 for Zone A per year. The role may also include benefits, bonuses, commissions, and equity.

Company

hirify.global develops software products that help teams collaborate and manage different types of work.

What you will do

• Develop machine learning and LLM capabilities for intent understanding, query rewriting, query decomposition, entity understanding, and structured search constraints.
• Build agentic search planners that select sources and tools, create search strategies, and adapt to retrieved evidence.
• Improve models and agents through prompt development, model selection, training data improvements, and fine-tuning.
• Own projects from problem definition and prototyping through experimentation, production deployment, and ongoing measurement.
• Build datasets and evaluation methods for retrieval relevance, evidence coverage, task success, and grounding.
• Collaborate with search, platform, relevance, Rovo Chat, product, and AI teams; contribute to design reviews, code reviews, and mentoring.

Requirements

• Bachelor’s or master’s degree in Computer Science or a related field, or equivalent practical experience.
• 4+ years of industry experience in machine learning, including delivering ML capabilities into production.
• Strong Python skills and the ability to write reliable, maintainable, production-quality code.
• Experience with natural language processing, information retrieval, search relevance, or LLM applications.
• Experience designing experiments, building evaluation datasets, analyzing model behavior, and applying the ML development lifecycle from data preparation through monitoring.
• Ability to own ambiguous problems, make technical trade-offs, and communicate with engineering and product partners.

Nice to have

• Experience with AI agents, tool-use workflows, multi-step search systems, or retrieval-augmented generation.
• Experience with query understanding, semantic or hybrid retrieval, ranking, personalization, or context-aware search.
• Experience with LLM fine-tuning, supervised fine-tuning, preference optimization, or reinforcement learning.
• Experience evaluating agents through trajectory analysis, human evaluation, or model-based judging.
• Experience with Spark, AWS, Databricks, distributed data processing, or cloud ML environments.

Culture & Benefits

• Flexible work from an office, home, or a combination of both.
• Health and wellbeing resources.
• Paid volunteer days and community support programs.
• Potential eligibility for benefits, bonuses, commissions, and equity.
• Accommodations and adjustments are available throughout the recruitment process.

Hiring process

• Identity verification may be required as a condition of employment for employment fraud prevention.