Staff ML Engineer

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November 4

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BrightHire

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HR Tech • AI • Recruitment

BrightHire is an interview intelligence platform that uses AI to enhance the hiring process for companies. By providing tools for interview planning, automated note-taking, and actionable insights, BrightHire helps organizations improve the quality and efficiency of their hiring efforts. The platform focuses on structured interviews, reduces bias, and supports diverse hiring practices, ultimately enabling teams to make data-driven decisions and nurture a better candidate experience.

11 - 50 employees

Founded 2019

👥 HR Tech

🎯 Recruiter

💰 $20.5M Series B on 2021-10

📋 Description

• Design and own comprehensive eval harnesses that measure accuracy, completeness, style, hallucination rate, bias, and safety across every release • Tune and iterate on RAG pipelines, prompt chains, conversation loops, provider selections, and fine-tunes until quality bars are met or exceeded • Build reusable data and evaluation pipelines, a shared semantic layer, and monitoring dashboards that make it easy for product teams to ship reliable AI quickly • Optimize for cost and latency, continuously benchmarking models and negotiating trade-offs between performance and spend • Implement robust data governance, lineage, and AIOps practices that satisfy enterprise compliance requirements and support our AI bias audit process • Collaborate daily with the data science team and cross-functional squads to embed evaluations into CI/CD and ensure every deploy meets our candidate-first standards • Contribute to a modular, model-agnostic architecture that lets us switch or upgrade LLMs with minimal friction • Document best practices and share knowledge to raise the bar for AI development across BrightHire

🎯 Requirements

• 5+ years building production machine-learning or NLP systems, including at least 1 year focused on generative-AI or LLM applications • Demonstrated experience creating automated evaluation suites for LLM outputs (accuracy, safety, bias, tone, style) and using results to guide iterative improvements • Deep knowledge of prompt engineering, RAG techniques, vector search, embeddings, fine-tuning, and model selection across multiple providers • Strong Python skills and familiarity with modern LLMOps/MLOps stacks (e.g., Prefect, dbt, vector databases, promptfoo or other evaluation harnesses) • Comfort working with both structured (SQL) and unstructured (text, audio, embeddings) data, and building pipelines that join them seamlessly • A mindset for governance: understanding of data privacy, AI ethics, bias mitigation, and enterprise compliance frameworks • Ability to communicate complex AI trade-offs clearly to engineers, designers, and executives alike • Bias toward action, curiosity, and a passion for building high-quality user experiences.

🏖️ Benefits

• Work-from-home stipend and quarterly snack deliveries • Annual learning stipend and generous vacation stipend to recharge • Competitive compensation, equity, and benefits package