Staff Machine Learning Engineer

Indeed•Remote,

2d•$163,000 - $341,000

About The Position

As a Staff Machine Learning Engineer, you will be a team lead on the Employer Agents Team. Your team will be responsible for driving value and better customer experiences for our employer-facing Agentic solutions, helping redefine the employer hiring journey through AI. You will own one of the team's major workstreams, partner with cross-functional teams to develop and deliver ML and AI projects, including Agentic solutions, LLMs-as-a-Judge, evaluation capabilities, and ML systems and models, and help drive technical direction for the team while guiding other members to achieve product and technical goals. On a daily basis, you will explore data, formulate problem statements, build new agentic experiences, and drive improvements in our LLMOps reliability and infrastructure.

Requirements

Requires a Bachelor’s degree in Computer Science, Mathematics, Statistics, or related field and a minimum of 8 years of related experience; or a Master’s degree with a minimum of 6 years of experience; or a PhD with 3 years experience
Familiarity with agent orchestration frameworks, LLM observability tools, and prompt optimization techniques (e.g. GEPA)
Prior success in deploying impactful Machine Learning solutions to large-scale production systems, while partnering across teams
Solid knowledge of data structures and algorithms
Sense of ownership and accountability as a key contributor in the technical and product domains
Knowledge and practical experience working on Deep Learning Libraries (like Torch, Tensorflow, etc.)
Excellent written and verbal communication in English, effective with technical and business audiences

Responsibilities

Partner with cross-functional teams to enhance and optimize search algorithms for improved accuracy, relevance, and overall user experience.
Experiment with Proof of Concept Machine Learning model improvements, scale them to production, and run iterative A/B experiments to improve our matching technology while partnering with other teams
Define and clarify project priorities, deliverables, and success criteria in partnership with cross-functional teams.
Act as a bridge between technical and non-technical collaborators, facilitating effective communication and comprehension of project goals and outcomes.
Mentor and grow other software engineers and Machine Learning Engineers across teams
Break down larger Machine Learning initiatives into pieces that deliver incremental business value and guide the team through implementing them
Represent Indeed at major Machine Learning conferences, such as Neural Information Processing Systems (NeurIPS), the International Conference on Machine Learning (ICML), and the International Conference on Learning Representations (ICLR).