Machine Learning Engineering Manager

Machine Learning Engineering Manager, Responsible AI

As a Machine Learning Engineering Manager on the Responsible AI team (RAI), you will oversee multiple data scientists and machine learning engineers towards the goal of ensuring safe, compliant, and fair AI products for all users of the Indeed platform. You will also work collaboratively with partner teams across the business, including Product, Software Engineering, UX, Legal, and Trust teams. You will develop sustainable methods that ensure strict standards for all AI products  will design and build data science tools supporting Indeed’s Responsible AI goals, including classifiers for verifying safe AI output and preventing sensitive data leakage;

red‑testing agents for stress‑testing AI systems for harmful content; and evaluation suites for evaluating AI for compliance, fairness and safety before they launch.

At Indeed, we are committed to delivering exceptional experiences that connect job seekers with opportunities through innovative technology. We integrate machine learning at every step to create consistent, engaging, and secure experiences that meet the needs of our users. Our teams consist of Software Engineers, UX Designers, Product Managers, and Machine Learning professionals collaborating across regions to drive impactful business outcomes.

• Coach Machine Learning Engineers and Data Scientists on the Journey team to improve their performance, advise them on their career direction, and develop their qualifications.
• Work to understand, prioritize, and plan the team’s work items without external guidance.
• Ensure delivery of machine learning solutions, set expectations for what can be done and by when, and prioritize incoming projects.
• Improve existing Agile, ML, and A/B testing processes and develop new ones.
• Scope projects, gather and improve on requirements, and delegate work effectively.
• Partner with and provide project direction and feedback to cross‑functional peers, including Product Managers, Software Engineers.
• Remove roadblocks and give individual contributors autonomy and ownership.
• Brainstorm with teammates about practical experimental design, navigating production codebases, and model development.
• Be prepared to closely engage and contribute directly to implementation when necessary.

• 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 a minimum of 3 years experience.
• Demonstrated achievement as a Manager in Machine Learning Engineering, overseeing teams of 3 or more, and addressing intricate, large‑scale problems.
• Well‑versed in coding (Python, Java, Go, or C++) and experience with SQL Databases like Presto, and data processing frameworks like Spark.
• Have full‑stack experience in data collection, aggregation, analysis, visualization, productionisation, and monitoring.
• Highly effective in coaching Machine Learning Engineers, facilitating qualification enhancement, and fostering career development.

Tier 1 - United States of America 163, USD per year

Tier 2 - United States of America 182, USD per year

Tier 3 - United States of America 199, USD per year

Tier 4 - United States of America - N/A

Tier 5 - United States of America - 227, USD per year

The salary range for this role reflects the minimum and maximum compensation for the role. Offers are typically made between the range minimum and the range midpoint. Actual compensation will be determined based on job‑related skills, experience, and expertise, as evaluated during the interview process. The range(s) listed is just one component of Indeed’s total compensation package for employees. Other rewards may include quarterly bonuses, Restricted Stock Units (RSUs), a Paid Time Off policy, and many region‑specific benefits.

Compensation may also vary based on where a role is performed, as work locations are grouped into geographic pay tiers to reflect cost of labor differences in different geographic markets. Candidates can view geographic pay…