Senior Machine Learning Engineer (AI-Native Evaluation & Search)

Location: Remote or hybrid; hirify.global can hire in any country where it has a legal entity. US office locations include San Francisco, Mountain View, and Seattle.

Salary: $171,063–$269,075 annual base pay in the United States, depending on geographic pay zone. Additional benefits, bonuses, commissions, and equity may apply.

Company

hirify.global develops collaboration and productivity software that helps teams work together.

What you will do

• Build and productionize machine learning models and AI systems for Search & Intelligence products.
• Own ML projects from problem definition and data development through deployment, monitoring, evaluation, and iteration.
• Develop search, retrieval, ranking, recommendation, conversational, and agentic experiences.
• Design evaluation-driven development processes, including quality metrics, benchmarks, test cases, acceptance criteria, and regression suites.
• Conduct offline and online evaluations, human assessments, model-based evaluations, and error analysis.
• Design scalable systems that meet requirements for quality, reliability, latency, privacy, and cost; communicate decisions and mentor engineers.

Requirements

• Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Science, or a related field, or equivalent experience.
• At least 4 years of experience developing and deploying machine learning or AI systems in production.
• Strong Python skills and experience with Java, Kotlin, TypeScript, or another production language.
• Experience with SQL, Spark or similar large-scale data processing technologies, and large datasets.
• Experience designing evaluation strategies, analyzing model quality, and using results to guide improvements.
• Ability to solve ambiguous problems independently and deliver production-quality solutions; strong communication and collaboration skills.

Nice to have

• Experience building AI-native products, RAG systems, conversational assistants, or tool-using agents.
• Experience with evaluation-driven development, benchmarks, regression testing, human evaluation, or LLM-as-a-judge.
• Experience with search relevance, ranking, recommendations, personalization, embeddings, NLP, or large language model optimization.
• Experience with ML platforms, model serving, data pipelines, observability, responsible AI, or mentoring engineers.

Culture & Benefits

• Flexible choice of office, home, or a combination of both.
• Health and wellbeing resources.
• Paid volunteer days and community support programs.
• Benefits may include additional compensation, bonuses, commissions, and equity.
• Accommodations and adjustments are available during the recruitment process.