Staff Machine Learning Engineer

Indeed•Remote,

23h•$163,000 - $341,000•Remote

About The Position

As a Staff Machine Learning Engineer in Indeed’s Staffing Technology Marketplace group, you will own the source data, models, and business impact behind an AI-first staffing platform used by agencies, employers, and managed service providers (MSPs) to manage orders, worker bookings, and shifts. You will set technical direction for models that predict worker attendance, order fill rates, and at-risk orders, then translate those predictions into actionable decisions such as how many backup workers to book, how MSPs should distribute orders among agencies, and what pay rate is likely to meet a client’s fill and quality goals without overspending. You will work with customers and business, product, and engineering teams to define, build, and evaluate production models for markets in the US and UK. The role includes monitoring model health and detecting degradation for individual customers; designing ranking and reliability scores that balance immediate fill needs with long-term worker-pool health; and developing models that can serve new agencies with no history and customers with sparse data. Recommendations should be explainable and configurable to each agency’s risk appetite. You will use model insights to shape the product roadmap and collaborate with legal and engineering partners on questions such as what information can be learned across agencies and what must remain separate. You will also contribute to search and matching improvements, test machine-learning concepts and scale successful approaches to production, and run iterative experiments. The role includes setting project priorities and success criteria, bridging technical and non-technical collaborators, mentoring engineers, breaking initiatives into incremental business-value milestones, and representing Indeed at major machine-learning conferences. The position is U.S. remote only. Indeed lists annual base salary ranges by U.S. pay tier, from $163,000 to $341,000 where a range is provided; Tier 4 is listed as N/A. Actual compensation varies by location and experience, and total compensation may include quarterly bonuses, RSUs, paid time off, and region-specific benefits.

Requirements

Bachelor’s degree in Computer Science, Mathematics, Statistics, or a related field and at least 8 years of related experience; or a master’s degree and at least 6 years of experience; or a PhD and 3 years of experience.
Proven experience building machine-learning systems for two-sided marketplaces, staffing, on-demand labor, or similar operational businesses.
Experience designing experiments in marketplaces where interference can rule out clean A/B tests.
Proven experience solving cold-start and sparse-data problems in production in multi-tenant B2B settings with differing customer data and configurations.
Experience building models that work for new customers with no history and perform well with thin data, using well-calibrated probabilistic predictions such as classification or forecasting under uncertainty.
Prior success deploying impactful machine-learning solutions to large-scale production systems while partnering across teams.
Solid knowledge of data structures and algorithms.
Ownership and accountability as a contributor in technical and product domains.
Practical experience with deep-learning libraries such as Torch or TensorFlow.
Excellent written and verbal English communication skills, effective with technical and business audiences.

Responsibilities

Own source data, machine-learning models, and business impact for Indeed’s staffing technology marketplace.
Set technical direction for models that predict worker attendance, order fill rates, and orders at risk.
Turn model predictions into customer-facing decisions, including backup booking levels, agency order-release sequencing, and pay-rate recommendations.
Work with customers and business, product, and engineering teams to define, build, and evaluate production models for US and UK markets.
Monitor model health and identify degradation affecting individual customers.
Design ranking and reliability scoring that balances immediate staffing needs with long-term worker-pool health.
Develop models that work for new agencies without historical data and remain effective with sparse data.
Ensure recommendations are explainable and configurable to each agency’s risk appetite.
Use model results to inform the product roadmap and work with legal and engineering partners on cross-agency data boundaries.
Partner with cross-functional teams to improve search algorithms, accuracy, relevance, and user experience.
Experiment with proof-of-concept machine-learning improvements, scale them to production, and run iterative A/B experiments to improve matching technology.
Define project priorities, deliverables, and success criteria with cross-functional teams.
Facilitate communication between technical and non-technical collaborators about project goals and outcomes.
Mentor and develop software engineers and machine-learning engineers across teams.
Break larger machine-learning initiatives into increments that deliver business value and guide implementation.
Represent Indeed at major machine-learning conferences, including NeurIPS, ICML, and ICLR.