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About This Role
Company Description About Syngenta
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
At Syngenta, our goal is to build the most collaborative and trustworthy team in agriculture, providing top-quality seeds and innovative crop protection solutions that improve farmers' success. To support this mission, Syngenta’s Analytics & Data Sciences Team is seeking an Operations Research Engineer in Malta, IL. This role will drive operational efficiency and decision quality across Seeds Research & Development by designing, building, and scaling algorithmic decision-support solutions.
The role applies Operations Research and Mathematical Optimization to complex end-to-end business processes—translating business objectives and constraints into robust models, integrating them into Syngenta’s digital ecosystem, and enabling measurable improvements in service, cost, speed, and resource utilization.
Accountabilities:
- Design, build, and deploy mathematical optimization models using Linear Programming (LP), Mixed-Integer Programming (MIP), Nonlinear Programming (NLP), Constraint Programming (CP), and hybrid approaches as appropriate.
- Translate business objectives, constraints, and operating policies into robust mathematical formulations with clear assumptions and acceptance criteria.
- Integrate optimization solutions into Syngenta’s digital ecosystem.
- Identify, collect, cleanse, and structure data from multiple sources; define input/output specifications, data quality requirements, and data lineage for optimization models.
- Define and align business KPIs and metrics that drive optimization objectives and enable value tracking.
- Validate models through testing, back-testing, and scenario analysis; run simulations and what-if analyses to assess robustness and quantify trade-offs.
- Lead and manage projects delivering optimization solutions for strategic decisions and day-to-day operations, such as scope, timeline, risks, and stakeholder alignment.
- Collaborate with cross-functional teams and line-of-business users to review current operational systems and processes, identify pain points, and prioritize optimization use cases.
- Communicate clearly on bottlenecks, insights, and recommendations to enable informed decisions by management and operational teams.
- Work in an agile way and adhere to engineering best practices.
- Contribute to capability building by developing technical training materials and curricula to support adoption and awareness of Operations Research solutions.
- PhD in Operations Research, Applied Mathematics, Industrial Engineering, Data Science, Management Science, Computer Science, or a related field.
- 5+ years of applied experience designing, building, and deploying Operations Research / Mathematical Optimization solutions in an industrial context.
- Strong understanding of Operations Research techniques with emphasis on mathematical programming.
- Proficiency in Python for optimization, data processing, and automation; ability to write production-quality, maintainable code.
- Hands-on experience with optimization solvers and modeling tools (e.g., Gurobi, SCIP, FICO Xpress, COPT) and building end-to-end optimization applications.
- Strong problem-framing and structured problem-solving skills: ability to identify complex business problems, assess fit for OR techniques, and deliver scalable decision-support solutions.
- Proven track record delivering implemented business process improvement solutions with measurable impact.
- Ability to identify, access, and translate relevant data into actionable, evidence-based decisions; ensure data quality, consistency, and traceability across sources.
Qualifications
- Experience in Manufacturing, Supply Chain, or Logistics environments.
- Knowledge of combinatorial optimization techniques.
- Familiarity with modern development practices and tooling (Git/GitHub, code reviews, testing).
- Experience using AI-assisted coding tools (e.g., GitHub Copilot).
- Experience building lightweight decision-support interfaces and visualizations (e.g., Streamlit or similar) to support user adoption.
- Ability to operate effectively in a matrix organization by building strong partnerships and influencing stakeholders at multiple levels without direct authority.
- Ability to generate strategic insights from complex analyses and communicate them clearly to drive measurable improvements in operational efficiency and effectiveness.
- Strong analytical mindset with the ability to communicate complex concepts clearly to technical and non-technical audiences.
Additional Information
Salary for this position ranges between $106,000 - $145000 annually.
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.
WL: 5B
#LI-REMOTE
Salary Context
This $106K-$145K range is below the median for Research Engineer roles in our dataset (median: $163K across 56 roles with salary data).
View full Research Engineer salary data →Role Details
About This Role
Research Engineers bridge the gap between research and production. They implement papers, build experiment infrastructure, optimize training pipelines, and make research prototypes production-ready. They're the engineers who make research work at scale.
The role sits at a unique intersection. You need to understand the math well enough to implement novel architectures correctly, and you need the engineering chops to make them run efficiently on distributed systems. When a research scientist has a breakthrough idea, you're the person who turns it from a notebook prototype into a training pipeline that runs on 256 GPUs.
Across the 9,253 AI roles we're tracking, Research Engineer positions make up 3% of the market. At Syngenta, this role fits into their broader AI and engineering organization.
Research Engineer roles are growing as AI labs recognize that research velocity depends on engineering quality. The role is less competitive than Research Scientist (no PhD required), but the bar for engineering skill is very high. These roles are concentrated at major labs and well-funded startups.
What the Work Looks Like
A typical week involves: implementing a new attention mechanism from a recent paper, profiling and optimizing a training pipeline that's bottlenecked on data loading, building evaluation infrastructure for a new benchmark, debugging distributed training issues across a GPU cluster, and pair-programming with a research scientist on their latest experiment. The work is deeply technical.
Research Engineer roles are growing as AI labs recognize that research velocity depends on engineering quality. The role is less competitive than Research Scientist (no PhD required), but the bar for engineering skill is very high. These roles are concentrated at major labs and well-funded startups.
Skills Required
Strong software engineering fundamentals plus ML knowledge. Python, C++, and CUDA experience are common requirements. You'll need to read papers and turn ideas into working code. Distributed systems experience (especially distributed training) is highly valued. Performance optimization skills separate great candidates from good ones.
Experience with large-scale training infrastructure (FSDP, DeepSpeed, Megatron), GPU programming (CUDA, Triton), and the internals of ML frameworks (PyTorch internals, custom autograd functions) is what makes candidates stand out. The best research engineers can debug issues that span the full stack from GPU memory management to numerical precision to algorithmic correctness.
Strong postings mention the team's recent research, the infrastructure scale, and the specific technical challenges. They often list the research areas you'd support. Look for roles that emphasize both implementation quality and research understanding.
Compensation Benchmarks
Research Engineer roles pay a median of $252,000 based on 457 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($125K) sits 50% below the category median. Disclosed range: $106K to $145K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and AI Engineering Manager ($251,622). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,382; Director: $272,300; VP: $235,830.
Syngenta AI Hiring
Syngenta has 2 open AI roles right now. They're hiring across AI/ML Engineer, Research Engineer. Positions span Bracknell, England, UK, Malta, IL, US. Compensation range: $145K - $145K.
Location Context
Across all AI roles, 6% (530 positions) offer remote work, while 8,653 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,487 roles, $210,000 median).
Career Path
Common paths into Research Engineer roles include Software Engineer, ML Engineer, Research Intern.
From here, career progression typically leads toward Senior Research Engineer, Research Scientist, ML Architect.
This is one of the best entry points into AI research without a PhD. Build a strong engineering portfolio with ML projects, contribute to open-source ML frameworks, and demonstrate that you can implement complex ideas correctly and efficiently. The transition to Research Scientist is possible with published first-author work, which some research engineer roles support.
What to Expect in Interviews
Technical screens test both engineering skill and research understanding. Expect coding rounds with performance-critical implementations (GPU optimization, efficient data loading). Be prepared to discuss papers relevant to the team's research area and explain how you'd implement key ideas. System design questions focus on training infrastructure: distributed training, experiment tracking, and compute resource management.
When evaluating opportunities: Strong postings mention the team's recent research, the infrastructure scale, and the specific technical challenges. They often list the research areas you'd support. Look for roles that emphasize both implementation quality and research understanding.
AI Hiring Overview
The AI job market has 9,253 open positions tracked in our dataset. By seniority: 107 entry-level, 4,462 mid-level, 3,410 senior, and 1,274 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (530 positions). The remaining 8,653 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Research Engineer roles are growing as AI labs recognize that research velocity depends on engineering quality. The role is less competitive than Research Scientist (no PhD required), but the bar for engineering skill is very high. These roles are concentrated at major labs and well-funded startups.
The AI Job Market Today
The AI job market spans 9,253 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,989), AI Software Engineer (462), AI Product Manager (457). 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 (107) are outnumbered by mid-level (4,462) and senior (3,410) 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 1,274 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (530 positions), with 8,653 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 $213,000. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,638 postings), Aws (893 postings), Azure (718 postings), Rag (666 postings), Gcp (532 postings), Pytorch (434 postings), Claude (408 postings), Kubernetes (402 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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