Machine Learning Scientist

Corteva, Inc.; Des Moines (IA); Remote; USD 113,000 - 142,000; Full time

Job summary

An innovative company is seeking a Machine Learning Scientist to enhance AI and predictive modeling capabilities. This role focuses on designing and deploying advanced machine learning models, including large language models and probabilistic systems. You will work on real-world challenges, collaborating with experts to create scalable solutions that drive impactful decisions. With a strong emphasis on experimentation and clean coding practices, this position offers numerous development opportunities, competitive benefits, and a chance to contribute to meaningful projects in a dynamic environment.

Benefits

Health benefits from day one

Four weeks of paid time off

Parental leave of 16 weeks

Retirement savings plan

Tuition reimbursement program

Qualifications

• Expertise in modern ML techniques and production-ready systems.
• Experience in developing ML models for optimization and forecasting.

Responsibilities

• Research and implement state-of-the-art ML models.
• Design and evaluate workflows using LLMs.

Skills

Machine Learning

Deep Learning

Python

Probabilistic Modeling

Reinforcement Learning

Education

MS or PhD in Computer Science

Statistics

Tools

PyTorch

TensorFlow

scikit-learn

Databricks

Spark

Job description

Machine Learning Scientist page is loaded

Machine Learning Scientist

Apply locations Remote (Iowa) Des Moines, Iowa, United States time type Full time posted on Posted Yesterday job requisition id 240368W

T he Systems Optimization and Decision Analytics (SODA) Team is seeking a curious, innovative, and results-driven Machine Learning Scientist to help advance our AI and predictive modeling capabilities. We focus on building scalable, intelligent systems that power optimization, planning, forecasting, and human-in-the-loop decision-making for global operations. This role will center around designing and deploying cutting-edge ML models—including probabilistic models, large language models (LLMs), and time-series or agent-based systems. The ideal candidate brings deep expertise in modern ML techniques and a passion for turning theoretical innovation into production-ready systems that drive real-world im pact.

What You’ll Do:

• Research, prototype, and implement state-of-the-art ML models across a range of tasks: forecasting, optimization, planning, recommendation, and human-AI teaming.

Develop models using advanced methods such as:

• Large Language Models (LLMs), foundation model fine-tuning, and prompt engineering.
• Probabilistic modeling, Bayesian inference, and uncertainty-aware decision systems.
• Reinforcement learning (RL), multi-agent systems, and decision intelligence architectures.
• Generative modeling (e.g., diffusion models, VAEs, normalizing flows).
• Time-series and forecasting models (e.g., Temporal Fusion Transformers, DeepAR , N-BEATS).
• Graph neural networks (GNNs), especially for spatio -temporal and structured prediction tasks.
• Causal inference, self-supervised learning, and contrastive representation learning.

Design and evaluate retrieval-augmented generation (RAG) and agentic workflows using LLMs.

Scale experimentation and model training pipelines using Databricks , MLflow , and Spark .

Partner with domain experts to frame complex, real-world challenges into solvable ML problems.

Produce clean, reproducible code with strong documentation and CI/CD integration.

What Skills You Need:

• MS or PhD in Computer Science, ML, Statistics, or a related field.
• Experience developing and deploying modern ML systems in production settings.
• Solid foundation in deep learning and probabilistic machine learning.
• Hands-on experience with transformer-based architectures, LLMs, and adaptation methods (e.g., fine-tuning, LoRA , RAG).
• Strong Python skills with experience in PyTorch , TensorFlow, scikit-learn, and Hugging Face.
• Familiarity with Databricks, Spark, and distributed computing frameworks.
• Understanding of model evaluation, uncertainty quantification, and scientific experiment design.

Nice To Have:

• Experience in applied reinforcement learning, causal inference, or simulation-to-real (sim2real) modeling.
• Exposure to self-supervised and contrastive learning techniques.
• Familiarity with federated learning or privacy-preserving ML approaches.
• Ability to integrate ML systems into user-facing applications and decision platforms.
• Background in supply chain, agriculture, or other complex operational domains.

About You:

• Passion for research and experimentation with a practical mindset for deployment.
• Excellent written and verbal communication skills for diverse audiences.
• Comfortable working independently and as part of a distributed, collaborative team.
• Able to prioritize, manage ambiguity, and deliver impact in a fast-paced setting.

#LI-BB1

Benefits – How We’ll Support You:

• Numerous development opportunities offered to build your skills
• Be part of a company with a higher purpose and contribute to making the world a better place
• Health benefits for you and your family on your first day of employment
• Four weeks of paid time off and two weeks of well-being pay per year, plus paid holidays
• Excellent parental leave which includes a minimum of 16 weeks for mother and father
• Future planning with our competitive retirement savings plan and tuition reimbursement program

• Learn more about our total rewards package here - Corteva Benefits
• Check out life at Corteva! www.linkedin.com/company/corteva/life

Are you a good match? Apply today! We seek applicants from all backgrounds to ensure we get the best, most creative talent on our team.

The salary range for this position is $113,470.00 to $141,840.00.

This reflects a reasonable estimate of the targeted base salary for this role. This role is also eligible for an annual bonus. Based on factors such as geographic location and candidate qualifications, actual base pay is determined when an employment offer is made.

Corteva Agriscience is an equal opportunity employer. We are committed to embracing our differences to enrich lives, advance innovation, and boost company performance. Qualified applicants will be considered without regard to race, color, religion, creed, sex, sexual orientation, gender identity, marital status, national origin, age, military or veteran status, pregnancy related conditions (including pregnancy, childbirth, or related medical conditions), disability or any other protected status in accordance with federal, state, or local laws.

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