Embedded Machine Learning Engineer Jobs in Louisiana

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Day to Day

The Machine Learning Engineer I role partners closely with business partners across various functions to help execute strategic initiatives that increase revenue, drive operational scale, and improve efficiency for continuous growth. As a Machine Learning Engineer, you will prepare datasets, train and optimize models, and maintain and improve model inference services. You will learn and apply new techniques from open source packages and research publications, and creatively adapt models for solving business problems across Indeed.

Work spans classical ML through LLM systems. You improve search and retrieval quality using real user signals. Execution includes experiments, iteration, and production reliability at scale. You collaborate with engineers, data scientists, and product teams to define problems, test approaches, and ship measurable improvements.

Responsibilities

• Build AI/ML systems for search, ranking, and recommendations

• Develop LLM retrieval and generation workflows

• Improve search and ranking relevance

• Design metrics and run experiments

• Monitor model quality, latency, and cost

• Debug data, models, and system issues

• Build training, inference, and eval pipelines

Skills/Competencies

• Requires a Bachelor's degree in Computer Science, Mathematics, or Statistics, and a minimum of 2 years of related experience; or an advanced degree without experience

• Experience building ML models in Python; solid software engineering and algorithms fundamentals

• Experience developing backend services in Java/Kotlin for ML-driven systems and features

• Experience writing clean, testable, and maintainable production code

• Experience working with structured and unstructured data, including SQL for large-scale data querying, and building scalable data pipelines and features from data

• Experience integrating ML models into search systems using engines such as OpenSearch or similar, with familiarity in container orchestration for deployment with senior guidance

• Excellent understanding of model evaluation techniques, feature engineering, experiment design, and familiarity with LLM systems (RAG, embeddings, output evaluation)

Salary Range Transparency

Tier 1 - United States of America 118,000 - 176,000 USD per year

Tier 2 - United States of America 130,000 - 196,000 USD per year

Tier 3 - United States of America 143,000 - 215,000 USD per year

Tier 4 - N/A

Tier 5 - United States of America 163,000 - 245,000 USD per year

Salary Range Disclaimer

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 tiers by location on our career site (https://www.indeed.com/careers/paytiers), and recruiters can confirm how location is considered for a specific role.

Benefits - Health, Work/Life Harmony, & Wellbeing

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Indeed is committed to ensuring fairness and transparency throughout our hiring process. We use artificial intelligence (AI) tools to assist in the screening, assessment, and selection of applicants for this position by analyzing information provided in resumes and applications. Our use of AI does not replace human decision-making.

Unless otherwise notified, Indeed does not use AI constituting an AEDT or an ADMT as those tools are defined in applicable laws.

The deadline to apply to this position is 6/12/2026. Job postings may be extended at the hiring team's discretion based on applicant volume.

Reference ID: 46644

Reference ID: 46644