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About This Role
Company Description
At Syngenta Seeds Field Crops, we're shaping the future of agriculture and empowering farmers to meet the ever-growing demand for food and fuel. We’re a global Ag Tech powerhouse, headquartered in the United States, with passionate, local experts collaborating with farmers to deliver solutions that create market opportunities. We unite precision breeding, advanced biotechnology trait choice, and digital platforms for unmatched in-field performance. Our seeds help mitigate risks such as disease, insect, weed, and extreme weather pressures, all while promoting sustainable farming practices that protect and enhance our planet. Join our mission of revolutionizing food security and transforming agriculture.
Job Description
We are seeking an innovative and highly motivated Translational Genomics AI/ML Scientist based in Durham, NC, to develop next-generation computational capabilities that connect advanced genomics, machine learning, and biological interpretation to real-world crop research and breeding decisions. This role will transform large-scale genomic and phenotypic data into scientific insight, predictive models, and reusable digital tools that accelerate trait discovery, improve genomic prediction, and support innovation across Seeds R&D.
The successful candidate will work at the intersection of computational biology, AI/ML, bioinformatics, data science, and scientific product development. They will design methods for representing and learning from complex biological data, including genomic variation, gene annotations, expression data, pathways, phenotypes, and literature-derived evidence.
This position requires a strong scientific foundation and the ability to translate ambiguous research questions into scalable analytical approaches, validated models, and practical decision-support capabilities. The role will collaborate closely with geneticists, breeders, molecular biologists, data scientists, software engineers, and product teams to ensure that advanced computational methods are scientifically meaningful, technically robust, and adopted by research users.
The ideal candidate is a creative, hands-on scientist who combines deep expertise in genomics or computational biology with modern AI/ML skills and a passion for applying emerging technologies to high-impact biological problems. They can move fluidly between research innovation, method development, scalable implementation, stakeholder communication, and strategic scientific problem solving.
Accountabilities
- Lead the scientific design and delivery of AI/ML-enabled genomics capabilities that translate large-scale genomic, phenotypic, and biological datasets into actionable insights for trait discovery, genomic prediction, and breeding decisions.
- Develop, validate, and operationalize advanced computational methods, including graph machine learning, foundation models, knowledge graphs, and explainable AI approaches, ensuring they are scientifically rigorous, reproducible, and fit for research and product adoption.
- Build scalable bioinformatics and machine-learning workflows that integrate genomic variation, annotations, expression data, pathways, phenotypes, and literature-derived evidence across cloud and enterprise data platforms.
- Advance computational genomics approaches by creating scalable representations, feature-engineering strategies, and predictive models that capture biological complexity and support research decision-making.
- Partner with geneticists, breeders, molecular biologists, data scientists, software engineers, and product teams to translate complex biological questions into practical analytical solutions and reusable digital capabilities.
- Communicate methods, assumptions, results, limitations, and biological interpretation clearly to technical and non-technical stakeholders, enabling confident scientific decision-making.
- Drive innovation by identifying emerging AI/ML, genomics, and scientific-computing approaches that can accelerate discovery, improve predictive performance, and strengthen Syngenta’s digital genomics platforms.
- Ensure solutions follow good scientific, data governance, engineering, and reproducibility practices, including clear documentation, version control, model validation, and transparent handover to collaborators or product teams.
Qualifications Required Qualifications
- PhD in Bioinformatics, Computational Biology, Genomics, Genetics, Computer Science, Machine Learning, Statistics, Applied Mathematics, or a related discipline.
- Strong background in genomics, quantitative genetics, population genetics, or computational biology.
- Demonstrated expertise in machine learning and statistical modeling with proficiency in Python and scientific computing.
- Experience working with large-scale biological datasets.
- Strong publication or research track record in computational genomics, AI/ML, or a related field.
- Excellent communication and collaborative problem-solving skills.
Desired Qualifications
We’re especially interested in candidates who bring experience in one or more of the following areas:
- Genomics and graph-based genomic representations, including GNNs, GATs, graph embeddings, or network analytics.
- Foundation models, transformers, DNA language models, or biological large language models.
- Knowledge graphs, semantic technologies, biological ontologies, or AI-assisted scientific research workflows.
- Cloud computing and production AI environments, including AWS, SnowFlake, distributed computing, or MLOps.
- Plant genomics, breeding, crop science, or agricultural biotechnology.
Additional Information
- Authorized to work in the U.S. without sponsorship
What We Offer
- A culture that celebrates belonging and collaboration, promotes professional development and strives for a work-life balance that supports the team members. Offers flexible work options to support your work and personal needs.
- Full Benefit Package (Medical, Dental & Vision) that starts your first day.
- 401k plan with company match, Profit Sharing & Retirement Savings Contribution.
- Paid Vacation, Paid Holidays, Maternity and Paternity Leave, Education Assistance, Wellness Programs, Corporate Discounts, among other benefits.
Syngenta has been ranked as a top employer by Science Journal. Learn more about our team and our mission here: https://www.youtube.com/watch?v=OVCN_51GbNI
Syngenta is an Equal Opportunity Employer and does not discriminate in recruitment, hiring, training, promotion, or any other employment practices for reasons of race, color, religion, gender, national origin, age, sexual orientation, marital or veteran status, disability, or any other legally protected status.
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Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Syngenta, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Syngenta AI Hiring
Syngenta has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Durham, NC, US.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (193) are outnumbered by mid-level (4,500) and senior (3,455) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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