ai ml scientist operations research optimization simulation

A.P. Moller - Maersk

<div>
 
 AI/ML Scientist (Operations research/Optimization/Simulation)
 
 
 
 
 A.P. Moller - Maersk
 
 
 
 A.P. Moller &ndash; Maersk is the global leader in container shipping services. The business operates in 130 countries and employs 80,000 staff. An integrated container logistics company, Maersk aims to connect and simplify its customers&rsquo; supply chains.
 
 
 Today, we have more than 180 nationalities represented in our workforce across 131 Countries and this mean, we have elevated level of responsibility to continue to build inclusive workforce that is truly representative of our customers and their customers and our vendor partners too.
 
 
 The team - who are we:
 
 
 We are an ambitious team with the shared passion to use data, data science (DS), machine learning (ML), advanced simulation, optimization and engineering excellence to make a difference for our customers.
 
 
 We are a team, not a collection of individuals. We value our diverse backgrounds, our different personalities and strengths &amp; weaknesses. We value trust and passionate debates. We challenge each other and hold each other accountable. We uphold a caring feedback culture to help each other grow, professionally and personally.
 
 
 We are now seeking a new team member who is excited about developing advanced simulation models,optimization algorithms and AI/ML solutions that create operational insights for container shipping terminals
 
 
 worldwide, helping them optimize container handling, yard operations, vessel operations, and drive efficiency
 
 
 and business value.
 
 
 We Offer - This Is What You Get
 
 
 You will be part of the APM Terminals team within Global data and analytics (GDA), responsible for developing and delivering advanced simulation models, optimizations and AI/ML models for container shipping
 
 
 terminals. As an AI/ML Scientist, you will have a leading role in designing, building, maintaining, and
 
 
 iterating on products that directly impact terminal operations.
 
 
 This position offers a unique opportunity to develop and apply your deep knowledge of simulation, optimization, data science methods, software engineering, and modern AI development tools to create
 
 
 operational and strategic insights that are transforming container terminal operations globally.
 
 
 This is an exciting time to join a growing and dynamic team that solves some of the toughest problems in
 
 
 terminal operations and builds the future of container shipping. We offer a unique opportunity to impact global trade via world-leading container terminals. We focus on our people and the right candidate will have broad
 
 
 possibilities to further develop competencies in an environment characterized by change and continuous Progress.
 
 
 Key Responsibilities
 
 
 <ul>
 <li>
 Design, implement and deliver advanced simulation models and optimization solutions for terminal operations including container handling equipment efficiency, yard positioning strategies, vessel loading/unloading sequencing, and truck routing.
 </li>
 <li>
 Develop and maintain efficient applied engineering solutions that solve real-world operational challenges at container terminals globally.
 </li>
 <li>
 Build both operational tools for day-to-day terminal operations and strategic models for long-term planning and decision-making.
 </li>
 <li>
 Work with relevant stakeholders to understand terminal operational dynamics and business processes, incorporating their needs into products to enhance value delivery.
 </li>
 <li>
 Collaborate and communicate model rationale, results and insights with product teams, leadership and business stakeholders to roll out solutions to production environments.
 </li>
 <li>
 Analyse data, measure delivered value, and continuously evaluate and improve models to increase effectiveness and operational impact.
 
 
 </li>
 </ul>
 To do this job, we imagine you have:
 
 
 <ul>
 <li>
 5+ years of industry experience in building and delivering simulation models, optimization solutions, or data science products.
 </li>
 <li>
 PhD or M.Sc. in Operations Research, Industrial Engineering, Machine Learning, Statistics, Applied Mathematics, Computer Science, or other field related to algorithms and data (or equivalent experience).
 </li>
 <li>
 Strong experience in simulation modeling and optimization, particularly in operational environments with complex dynamics.
 </li>
 <li>
 Research or industry experience in one or more: Discrete event simulation, Combinatorial optimization problems (e.g., scheduling, vehicle routing, bin packing, sequencing), Continuous optimization problems (e.g., linear programming, mixed-integer programming, convex programming), AI/ML methods for operational problems, Prescriptive analytics (e.g., stochastic optimization, reinforcement learning).
 </li>
 <li>
 Experience with AI development tools and code assistants (GitHub Copilot, Cursor, Claude Code, or similar) for enhanced productivity.
 </li>
 <li>
 Excellent record of delivery using Python and technologies like Git, JIRA, etc.
 </li>
 <li>
 Ability to understand complex operational systems and translate business requirements into effective technical solutions.
 </li>
 <li>
 Strong communication skills with ability to work independently with stakeholders while liaising and reviewing with the team.
 </li>
 <li>
 Experience working with cloud environments.
 
 
 </li>
 </ul>
 Nice to have:
 
 
 <ul>
 <li>
 Experience in container terminal operations, port logistics, or similar operational environments with complex resource allocation and scheduling dynamics.
 </li>
 <li>
 Familiarity with container handling equipment, yard operations, or vessel operationsExperience in material handling, manufacturing operations, or other domains involving physical asset optimization and sequencing problems.
 </li>
 <li>
 Experience developing and interacting with generative AI models.
 
 
 </li>
 </ul>
 Maersk is committed to a diverse and inclusive workplace, and we embrace different styles of thinking. Maersk is an equal opportunities employer and welcomes applicants without regard to race, colour, gender, sex, age, religion, creed, national origin, ancestry, citizenship, marital status, sexual orientation, physical or mental disability, medical condition, pregnancy or parental leave, veteran status, gender identity, genetic information, or any other characteristic protected by applicable law. We will consider qualified applicants with criminal histories in a manner consistent with all legal requirements.
 
 
 We are happy to support your need for any adjustments during the application and hiring process. If you need special assistance or an accommodation to use our website, apply for a position, or to perform a job, please contact us by emailing accommodationrequests@maersk.com.
</div>

AI/ML Scientist(Operations research/Optimization/Simulation)

Scouto AI

<div>
 Position Summary
 
 
 As a Computational Materials Discovery Scientist, you will work at the intersection of materials science, computational chemistry, condensed matter physics, quantum computing, and AI/ML. You will contribute to first-principles simulations, molecular and mesoscale modeling, materials informatics pipelines, and hybrid quantum&ndash;classical algorithms for accelerated discovery of materials, catalysts, semiconductors, polymers, and functional systems.
 
 
 This role is ideal for candidates who want to solve real scientific and industrial problems using DFT, MD, multiscale modeling, machine learning, and emerging quantum computing techniques, fully integrated with QpiAI&rsquo;s AI and quantum computing platforms.
 
 
 
 Requirements
 
 
 
 Core Technical Skills
 
 
 <ul>
 <li>
 Computational &amp; Quantum Mechanical Methods
 
 
 </li>
 </ul>
 Electronic Structure &amp; Quantum Methods
 
 
 <ul>
 <li>
 Density Functional Theory (DFT)
 </li>
 <li>
 Ab initio Molecular Dynamics (AIMD)
 </li>
 <li>
 Time-dependent DFT (TDDFT) for excited states
 </li>
 <li>
 DFT+U for strongly correlated systems
 </li>
 <li>
 Hybrid functionals
 </li>
 <li>
 Hartree&ndash;Fock and post-HF methods (MP2, CCSD(T))
 
 
 </li>
 </ul>
 Molecular &amp; Statistical Simulations
 
 
 <ul>
 <li>
 Classical Molecular Dynamics (MD)
 </li>
 <li>
 Force-field development and validation
 </li>
 <li>
 Monte Carlo (MC) simulations
 </li>
 <li>
 Kinetic Monte Carlo (kMC) for surface reactions
 </li>
 <li>
 Machine Learning Force Fields ( Fairchem, Universal Forcefield, MACE)
 
 
 </li>
 </ul>
 Multiscale &amp; Mesoscale Modeling
 
 
 <ul>
 <li>
 Hierarchical nano &rarr; micro &rarr; meso &rarr; macro modeling
 </li>
 <li>
 QM&ndash;MM and QM&ndash;continuum coupling
 </li>
 <li>
 Microkinetic modeling and reaction networks
 </li>
 <li>
 Phase-field modeling
 </li>
 <li>
 Free energy methods (umbrella sampling, metadynamics)
 </li>
 <li>
 Materials Simulation &amp; Electronic Structure Modeling
 </li>
 <li>
 Bulk and surface DFT calculations
 </li>
 <li>
 k-point convergence, basis set testing
 </li>
 <li>
 Band structure, DOS, PDOS
 </li>
 <li>
 Phonons and lattice dynamics
 </li>
 <li>
 Elastic constants, thermal &amp; mechanical properties
 </li>
 <li>
 Defect formation energies
 </li>
 <li>
 Phase stability &amp; convex hulls
 </li>
 <li>
 Slab models and adsorption energies
 </li>
 <li>
 Reaction pathways using NEB / CI-NEB
 
 
 </li>
 </ul>
 Benchmarking &amp; Validation
 
 
 <li>
 Cross-code benchmarking across:
 </li>
 <ul>
 <li>
 DFT engines: VASP, Quantum ESPRESSO, CP2K, GPAW, CASTEP, Gaussian, ORCA
 </li>
 <li>
 MD engines: LAMMPS, GROMACS, NAMD
 </li>
 <li>
 ML potential
 </li>
 <li>
 Materials Informatics &amp; AI/ML
 </li>
 <li>
 Build ML models for materials &amp; catalyst property prediction:
 </li>
 <ul>
 <li>
 Fairchem, Universal Forcefield, CGCNN, MEGNet, SchNet, e3nn, NequIP
 </li>
 <li>
 Transformer &amp; foundation models for materials
 </li>
 </ul>
 <li>
 Curate datasets from:
 </li>
 <ul>
 <li>
 Materials Project, OQMD, NOMAD, JARVIS
 </li>
 </ul>
 <li>
 Develop ML surrogates for:
 </li>
 <ul>
 <li>
 Energy &amp; force prediction
 </li>
 <li>
 Bandgap estimation
 </li>
 <li>
 Thermal &amp; mechanical properties
 </li>
 <li>
 Catalyst screening &amp; ranking
 </li>
 </ul>
 <li>
 Integrate ML pipelines with:
 </li>
 <ul>
 <li>
 DFT / MD workflows
 </li>
 <li>
 Quantum simulation pipelines
 </li>
 </ul>
 <li>
 Simulation Workflow Engineering
 </li>
 <li>
 Build reproducible, automated workflows in Python for:
 </li>
 <ul>
 <li>
 High-throughput materials screening
 </li>
 <li>
 DFT&ndash;MD&ndash;CG-Mesoscale simulation pipelines
 </li>
 <li>
 Data extraction &amp; post-processing
 </li>
 </ul>
 <li>
 Develop modular tools for:
 </li>
 <ul>
 <li>
 Structure parsing (CIF, POSCAR, XYZ, PDB)
 </li>
 <li>
 Geometry builders &amp; surface generators
 </li>
 <li>
 Parameters generations
 </li>
 <li>
 Visualization (band structure, DOS, phonons, trajectories)
 </li>
 </ul>
 <li>
 Deploy workflows on:
 </li>
 <ul>
 <li>
 HPC clusters
 </li>
 <li>
 Cloud platforms (AWS, GCP)
 </li>
 <li>
 Containerized environments (Docker)
 </li>
 </ul>
 <li>
 Research, Collaboration &amp; Documentation
 </li>
 <li>
 Conduct literature reviews in:
 </li>
 <ul>
 <li>
 Computational materials
 </li>
 <li>
 Catalysis
 </li>
 <li>
 Semiconductors
 </li>
 <li>
 Alloy and Ceramics
 </li>
 <li>
 Polymers
 </li>
 <li>
 Quantum algorithms
 </li>
 </ul>
 <li>
 Design, execute, and analyze numerical experiments
 </li>
 <li>
 Prepare:
 </li>
 <ul>
 <li>
 Technical reports
 </li>
 <li>
 Internal whitepapers
 </li>
 <li>
 Presentations and datasets
 </li>
 </ul>
 <li>
 Collaborate closely with:
 </li>
 <ul>
 <li>
 Quantum hardware teams
 </li>
 <li>
 Algorithm developers
 </li>
 <li>
 AI/ML engineers
 
 </li>
 </ul>
 </ul>
 Specialization Tracks
 
 
 <ul>
 <li>
 DFT &amp; Electronic Structure Specialization
 </li>
 <li>
 Advanced XC functional selection &amp; benchmarking
 </li>
 <li>
 Strongly correlated systems (DFT+U, Hubbard models)
 </li>
 <li>
 Excited-state calculations (TDDFT, GW &ndash; exposure preferred)
 </li>
 <li>
 Defects, surfaces, and interfaces
 </li>
 <li>
 Electronic transport &amp; conductivity modeling
 </li>
 <li>
 Molecular Dynamics &amp; Classical Simulations
 </li>
 <li>
 Classical MD simulations (LAMMPS, GROMACS)
 </li>
 <li>
 Force-field parameterization &amp; validation
 </li>
 <li>
 Free energy calculations
 </li>
 <li>
 Reactive force fields (ReaxFF)
 </li>
 <li>
 ML-accelerated MD workflows
 </li>
 <li>
 Parameter generation for coarse-grained simulations
 </li>
 <li>
 Catalysis Specialization
 </li>
 <li>
 Heterogeneous, homogeneous &amp; electro-catalysis
 </li>
 <li>
 Reaction pathway identification
 </li>
 <li>
 Transition state searches (NEB, CI-NEB)
 </li>
 <li>
 Adsorption energies &amp; surface thermodynamics
 </li>
 <li>
 Microkinetic modeling
 </li>
 <li>
 Applications:
 </li>
 <ul>
 <li>
 OER, ORR, HER
 </li>
 <li>
 Photocatalysis
 </li>
 <li>
 Single-atom &amp; nanocluster catalysts
 </li>
 </ul>
 <li>
 Polymers &amp; Soft Matter Specialization
 </li>
 <li>
 DFT-based parameter extraction for polymers
 </li>
 <li>
 Multiscale polymer modeling (DFT, AA, CG)
 </li>
 <li>
 Dissipative Particle Dynamics (DPD)
 </li>
 <li>
 Monte Carlo Simulations
 </li>
 <li>
 Polymer blends, Polymer nanocomposites, surfactants, colloids
 </li>
 <li>
 Polymerization, degradation, crosslinking, morphology and aging studies
 </li>
 <li>
 Integration of DFT &rarr; MD &rarr; DPD&rarr;Phase field simulations pipelines
 </li>
 <li>
 Quantum Computing for Materials Simulation
 </li>
 <li>
 Map materials Hamiltonians to qubits:
 </li>
 <ul>
 <li>
 Jordan&ndash;Wigner, Bravyi&ndash;Kitaev, parity mappings
 </li>
 </ul>
 <li>
 Work on quantum algorithms including:
 </li>
 <ul>
 <li>
 VQE for correlated materials
 </li>
 <li>
 Subspace Quantum Diagonalization (SQD)
 </li>
 <li>
 qEOM for excited states
 </li>
 <li>
 Quantum Phase Estimation
 </li>
 <li>
 QITE / Quantum Monte Carlo
 </li>
 </ul>
 <li>
 Analyze:
 </li>
 <ul>
 <li>
 Qubit requirements
 </li>
 <li>
 Circuit depth
 </li>
 <li>
 Noise &amp; error budgets
 </li>
 </ul>
 <li>
 Design material-specific ans&auml;tze for NISQ devices and simulators
 
 </li>
 </ul>
 Software &amp; Programming Skills
 
 
 <ul>
 <li>
 DFT Codes: VASP, QE, CP2K, Gaussian, ORCA, CASTEP, ADF
 </li>
 <li>
 MD Codes: LAMMPS, GROMACS, NAMD, AMBER
 </li>
 <li>
 Visualization: VMD, VESTA, OVITO, Materials Studio, ASE
 </li>
 <li>
 Programming: Python (mandatory), Bash
 </li>
 <li>
 ML: PyTorch, TensorFlow, scikit-learn
 </li>
 <li>
 Infrastructure: HPC, MPI, Docker, Git, AWS / GCP
 
 
 </li>
 </ul>
 Soft Skills
 
 
 <ul>
 <li>
 Strong analytical and first-principles thinking
 </li>
 <li>
 Ability to design reproducible scientific workflows
 </li>
 <li>
 Clear scientific communication
 </li>
 <li>
 High ownership and curiosity-driven research mindset
 
 
 </li>
 </ul>
 
 Educational Qualifications
 
 
 
 <ul>
 <li>
 PhD (or pursuing PhD for intern role) in:
 </li>
 <ul>
 <li>
 Chemistry
 </li>
 <li>
 Materials Science
 </li>
 <li>
 Chemical Engineering
 </li>
 <li>
 Physics
 </li>
 <li>
 Computational Science or related STEM field
 </li>
 </ul>
 <li>
 Strong foundation in:
 </li>
 <ul>
 <li>
 Physical chemistry
 </li>
 <li>
 Quantum mechanics
 </li>
 <li>
 Statistical mechanics &amp; thermodynamics
 </li>
 </ul>
 <li>
 Specialization in computational chemistry / materials modeling strongly preferred
 
 </li>
 </ul>
 
 Preferred Qualifications
 
 
 
 <ul>
 <li>
 Publications or strong computational project portfolio
 </li>
 <li>
 Experience with HPC &amp; large-scale simulations
 </li>
 <li>
 Prior work in:
 </li>
 <ul>
 <li>
 Materials discovery
 </li>
 <li>
 Catalysis
 </li>
 <li>
 Semiconductor
 </li>
 <li>
 Polymer modeling
 </li>
 <li>
 ML-driven materials science
 </li>
 </ul>
 <li>
 Exposure to quantum algorithms or hybrid quantum&ndash;classical workflows
 
 </li>
 </ul>
 Skills: ml,dft,materials,simulations,modeling
</div>

Computational Materials Discovery Scientist

GreyOrange

<div>
 
 As an Operations Research Scientist at GreyOrange, you will play a pivotal role in designing, developing, and implementing advanced models and algorithms that enhance decision-making across various business functions.
 
 
 You will collaborate with cross-functional teams, including product development, engineering, to optimize processes, improve product performance, and solve complex problems using mathematical and computational methods.
 
 
 The ideal candidate has a deep understanding of optimization, statistical modeling, and machine learning, with a passion for solving real-world problems in a high-tech environment.
 
 
 
 Roles and responsibilities:
 
 
 <ul>
 <li>
 Develop and apply advanced operations research models, including optimization, simulation, and stochastic models, to solve complex business challenges.
 </li>
 <li>
 Work closely with engineering, data science, and product teams to integrate OR models into SaaS platforms, providing actionable insights to enhance product performance.
 </li>
 <li>
 Design and implement optimization algorithms (e.g., linear programming, mixed-integer programming, and nonlinear optimization) to drive business efficiency and effectiveness.
 </li>
 <li>
 Analyze large datasets and develop predictive models using statistical and machine learning techniques to improve decision-making processes.
 </li>
 <li>
 Develop mathematical models, simulations, or optimization algorithms to represent real-world systems or problems.
 </li>
 <li>
 Use techniques like linear programming, dynamic programming, queuing theory, or game theory to create models.
 </li>
 <li>
 Build simulation models to evaluate potential solutions and improve resource allocation, scheduling, and supply chain logistics.
 </li>
 <li>
 Collaborate with stakeholders to understand business requirements and translate them into mathematical models and algorithmic solutions.
 </li>
 <li>
 Collaborate with cross-functional teams such as engineering, product, and operations to implement solutions.
 </li>
 <li>
 Communicate findings, insights, and recommendations to non-technical stakeholders through reports, presentations, or data visualizations.
 </li>
 <li>
 Communicate complex mathematical concepts and results to non-technical stakeholders in a clear and understandable manner.
 </li>
 <li>
 Monitor the performance of implemented solutions and refine models over time based on feedback and new data.
 </li>
 <li>
 Stay updated with the latest advancements in operations research, machine learning, and optimization, and apply best practices to develop innovative solutions.
 </li>
 <li>
 Document models, algorithms, and analytical processes for future reference and team knowledge sharing.
 </li>
 <li>
 Generate reports that summarize the outcomes of analyses and model performance.
 </li>
 </ul>
 
 
 Requirements/Qualifications:
 
 
 <ul>
 <li>
 Master&rsquo;s or Ph.D. in Operations Research, Applied Mathematics, Industrial Engineering, Computer Science, or a related field.
 </li>
 <li>
 Strong expertise in mathematical modeling, optimization techniques (linear programming, dynamic programming), and statistical analysis.
 </li>
 <li>
 Proficiency in programming languages such as Python, R, C++, and experience with optimization tools (Gurobi, CPLEX, MATLAB).
 </li>
 <li>
 Familiarity with data analysis, machine learning techniques, and simulation.
 </li>
 <li>
 Excellent problem-solving skills and the ability to translate complex problems into actionable insights.
 </li>
 <li>
 Strong collaboration skills and the ability to communicate complex concepts to non-technical stakeholders.
 </li>
 <li>
 Experience managing or contributing to research projects in a fast-paced environment.
 </li>
 </ul>
 
 
 About GreyOrange:
 
 
 <ul>
 <li>
 GreyOrange is a global leader in AI-driven robotic automation software and hardware, transforming distribution and fulfillment centers worldwide.
 </li>
 <li>
 Our solutions increase productivity, empower growth and scale, mitigate labor challenges, reduce risk and time to market, and create better experiences for customers and employees.
 </li>
 <li>
 Founded in 2012, GreyOrange is headquartered in Atlanta, Georgia, with offices and partners across the Americas, Europe and Asia.
 </li>
 </ul>
 
 
 Our Solutions:
 
 
 <ul>
 <li>
 The GreyMatter Multiagent Orchestration (MAO) platform provides vendor-agnostic fulfillment orchestration to continuously optimize performance in real time: the right order, with the right bot and agent, taking the right path and action.
 </li>
 <li>
 Currently operating more than 70 fulfillment sites across the globe (with deployments of 700+ robots at a single site), GreyMatter enables customers to decrease their fulfillment Cost Per Unit by 50%, reduce worker onboarding time by 90% and optimize peak season performance.
 </li>
 <li>
 In retail stores, our gStore end-to-end store execution and retail management solution supports omnichannel fulfillment, real-time replenishment, intelligent workforce tasking and more.
 </li>
 <li>
 Using real-time overhead RFID technology, the platform increases inventory accuracy up to 99%, doubles staff productivity, and enables an engaging, seamless in-store experience.
 </li>
 </ul>
 
 
 EEO:
 
 
 <ul>
 <li>
 GreyOrange provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.
 </li>
 <li>
 This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, and training.
 </li>
 </ul>
</div>

Operations Research Scientist

Qualitest

<div>
 
 Data Scientist &ndash; QA &amp; AI Enablement (Hub Model)
 
 
 Role Summary
 
 
 
 We are seeking one Data Scientist to join the Hub component of the client hub-and-spoke engagement model. The Data Scientist will play a key role in supporting the deployment, refinement, and optimization of Coco bots and AI-driven quality engineering initiatives.
 
 
 
 Key Responsibilities
 
 
 
 <ul>
 <li>
 Participate in daily stand-up meetings, provide status updates, highlight risks, and deliver daily and weekly status reports.
 </li>
 <li>
 Collaborate with business and technical stakeholders to gather representative and high-quality datasets for AI model development and testing.
 </li>
 <li>
 Perform statistical analysis, data visualization, data cleansing, and preparation of datasets for AI/ML testing and validation.
 </li>
 <li>
 Evaluate the effectiveness, reliability, and accuracy of new and existing data sources using appropriate data assessment techniques.
 </li>
 <li>
 Apply predictive modeling techniques to improve QA efficiency and optimize broader business outcomes.
 </li>
 <li>
 Coordinate with cross-functional teams to implement data models and monitor their operational outcomes.
 </li>
 <li>
 Develop and maintain processes, metrics, and tools to continuously monitor model performance, data quality, and accuracy over time.
 
 
 </li>
 </ul>
 
 Required Skills &amp; Experience (Must Haves)
 
 
 
 <ul>
 <li>
 AI / Machine Learning: Proficiency level 4/5
 </li>
 <li>
 Predictive Modeling: Proficiency level 4/5
 </li>
 <li>
 Data Modeling Implementation: Proficiency level 3/5
 </li>
 </ul>
</div>

Data Scientist- QA and AI Enablement

Glance

<div>
 
 
 What you will be doing
 
 
 
 We are looking for a Data Scientist who can operate at the intersection of
 
 classical machine learning
 
 ,
 
 large-scale recommendation systems
 
 , and
 
 modern agentic AI systems
 
 .
 
 
 You will design, build, and deploy intelligent systems that power Glance&rsquo;s personalized lock screen and live entertainment experiences. This role blends deep ML craftsmanship with forward-looking innovation in autonomous/agentic systems.
 
 
 
 Your responsibilities will include:
 
 
 
 
 Classical ML &amp; Recommendation Systems
 
 
 <ul>
 <li>
 Design and develop large-scale recommendation systems using advanced ML, statistical modelling, ranking algorithms, and deep learning.
 </li>
 <li>
 Build and operate machine learning models on diverse, high-volume data sources for personalization, prediction, and content understanding.
 </li>
 <li>
 Develop rapid experimentation workflows to validate hypotheses and measure real-world business impact.
 </li>
 <li>
 Own data preparation, model training, evaluation, and deployment pipelines in collaboration with engineering counterparts.
 </li>
 <li>
 Monitor ML model performance using statistical techniques; identify drifts, failure modes, and improvement opportunities.
 </li>
 </ul>
 
 
 Agentic Systems &amp; Next-Gen AI
 
 
 <ul>
 <li>
 Build and experiment with
 
 agentic AI systems
 
 that autonomously observe model performance, trigger experiments, tune hyperparameters, improve ranking policies, or orchestrate ML workflows with minimal human intervention.
 </li>
 <li>
 Apply
 
 LLMs, embeddings, retrieval-augmented architectures, and multimodal generative models
 
 for semantic understanding, content classification, and user preference modelling.
 </li>
 <li>
 Design intelligent agents that can automate repetitive decision-making tasks&mdash;e.g., candidate generation tuning, feature selection, or context-aware content curation.
 </li>
 <li>
 Explore reinforcement learning, contextual bandits, and self-improving systems to power next-generation personalization.
 </li>
 </ul>
 
 
 Cross-functional impact
 
 
 <ul>
 <li>
 Collaborate with Designers, UX Researchers, Product Managers, and Software Engineers to integrate ML and GenAI-driven features into Glance&rsquo;s consumer experiences.
 </li>
 <li>
 Contribute to Glance&rsquo;s ML/AI thought leadership&mdash;blogs, case studies, internal tech talks, and industry conferences.
 </li>
 <li>
 Thrive in a multi-functional, highly collaborative team environment with engineering, product, business, and creative teams.
 </li>
 <li>
 Plus: Interface with stakeholders across Product, Business, Data, and Infrastructure to align ML initiatives with strategic priorities.
 </li>
 </ul>
 
 
 
 
 We are seeking candidates with deep expertise in ML, recommendation systems, and a strong appetite for building agentic AI systems.
 
 
 You should have experience with:
 
 <ul>
 <li>
 Large-scale ML and recommendation systems (collaborative filtering, ranking models, content-based approaches, embeddings).
 </li>
 <li>
 Classical ML and deep learning techniques across NLP, sequence modelling, RL, clustering, and time series.
 </li>
 <li>
 Experience in deploying ML workflows/models in production system
 </li>
 <li>
 Big data processing (Spark, distributed data systems) and cloud computing.
 </li>
 <li>
 Designing end-to-end ML solutions&mdash;from prototype to production.
 </li>
 <li>
 Plus: Building or experimenting with LLMs, generative models, and agentic AI workflows (e.g., autonomous evaluators, self-improving pipelines, automated experiment agents).
 </li>
 </ul>
 
 
 
 
 
 Qualifications
 
 
 <ul>
 <li>
 Bachelor&rsquo;s/master&rsquo;s in computer science, Statistics, Mathematics, Electrical Engineering, Operations Research, Economics, Analytics, or related fields. PhD is a plus.
 </li>
 <li>
 6+ years of industry experience in ML/Data Science, ideally in large-scale recommendation systems or personalization.
 </li>
 <li>
 Experience with LLMs, retrieval systems, generative models, or agentic/autonomous ML systems is highly desirable.
 </li>
 <li>
 Expertise with algorithms in NLP, Reinforcement Learning, Time Series, and Deep Learning, applied on real-world datasets.
 </li>
 <li>
 Proficient in Python and comfortable with statistical tools (R, NumPy, SciPy, PyTorch/TensorFlow, etc.).
 </li>
 <li>
 Strong experience with the big data ecosystem (Spark, Hadoop) and cloud platforms (Azure, AWS, GCP/Vertex AI).
 </li>
 <li>
 Comfortable working in cross-functional teams.
 </li>
 <li>
 Familiarity with privacy-preserving ML and identity-less ecosystems (especially on iOS and Android).
 </li>
 <li>
 Excellent communication skills with the ability to simplify complex technical concepts.
 </li>
 </ul>
 
 
 
 We value curiosity, problem-solving ability, and a strong bias toward experimentation and production impact.
 
 
 Our team includes engineers, physicists, economists, mathematicians, and social scientists&mdash;a great data scientist can come from anywhere.
 
</div>

Machine Learning Engineer III - Recommendation Systems

Microsoft

<div>
 
 Overview
 
 
 
 Overview
 
 
 With the rapid acceleration of AI and the need to deliver trustworthy, high-performing models, Microsoft&rsquo;s Customer Experience (CXP) Data Science team is driving innovation at scale! Our mission is to advance AI capabilities through rigorous evaluations, fine-tuning, and large-scale experimentation to create intelligent, personalized experiences for customers worldwide. The CXP Data Science team is seeking passionate AI practitioners and data scientists to join agile, cross-functional teams working on cutting-edge model evaluation frameworks, optimization pipelines, and experimentation platforms that power Microsoft&rsquo;s AI ecosystem.
 
 
 We are looking for an
 
 Applied AI Scientist II
 
 to join our team! As a member of the Customer Experience Data Science organization at Microsoft, you will help us accelerate the company&rsquo;s own AI transformation. You will have the opportunity to partner directly with the engineering and product management groups responsible for managing the Dynamics 365 applications that power Microsoft&rsquo;s sales, marketing, and support platforms. You will apply advanced analytics, statistical modeling, machine learning and GenAI tools to uncover insights, drive action and deliver innovative solutions for complex business challenges.
 
 
 
 This Role Offers The Opportunity To
 
 
 
 <ul>
 <li>
 Collaborate across a diverse team of data scientists, engineers, and product managers
 </li>
 <li>
 Deepen your expertise in the evolving AI and ML landscape
 </li>
 <li>
 Drive meaningful impact for thousands of Microsoft sales, marketing and support platform users
 
 
 </li>
 </ul>
 Microsoft&rsquo;s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.
 
 
 
 Responsibilities
 
 
 
 Responsibilities
 
 
 <ul>
 <li>
 Work with key stakeholders to understand the underlying business needs and formulate the needs into discrete, manageable problems with well-defined measurable objectives and outcomes.
 </li>
 <li>
 Transform formulated problems into implementation plans by defining success metrics, applying/creating the appropriate methods, algorithms, and tools, as well as delivering statistically valid and reliable results.
 </li>
 <li>
 Write robust, reusable, and extensible code to support analysis and modeling.
 </li>
 <li>
 Develop new ML and GenAI based models using advanced statistical, ML and LLM techniques.
 </li>
 <li>
 Lead the evaluation of various GenAI based solutions, diagnosing issues and identifying root causes to support potential fine-tuning or reinforcement learning based fixes.
 </li>
 <li>
 Use AI-powered tools in your daily work to accelerate coding, analysis, and other tasks.
 
 
 </li>
 </ul>
 
 Qualifications
 
 
 
 Qualifications
 
 
 
 Required/Minimum Qualifications (RQs/MQs)
 
 
 
 <ul>
 <li>
 Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 4+ years related experience (e.g., statistics, predictive analytics, research)
 </li>
 <ul>
 <li>
 OR Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 1+ year(s) related experience (e.g., statistics, predictive analytics, research)
 </li>
 <li>
 OR equivalent experience.
 </li>
 </ul>
 <li>
 Expertise in Python , PySpark, and SQL, LLMs
 </li>
 <li>
 Exposure to ML development platforms such as Azure Machine Learning, Azure AI Foundry + Azure OpenAI
 
 </li>
 </ul>
 
 Additional Or Preferred Qualifications (PQs)
 
 
 
 <ul>
 <li>
 Bachelor&rsquo;s degree in Machine Learning, Data science, Mathematics, Statistics, Computer Science, or related field AND 5+ years related experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results)
 </li>
 <ul>
 <li>
 OR master&rsquo;s degree in data science, Mathematics, Statistics, Computer Science, or related field AND 4+ year(s) related experience
 </li>
 <li>
 OR Doctorate in Data Science, Mathematics, Statistics, Computer Science, or related field AND 3+ year(s) related experience
 </li>
 </ul>
 <li>
 Experience in Spark and ability to write/maintain/understand declarative Spark code and to cleanly write and maintain SQL. Knowledge of finer operational aspects of Spark such as setting optimal cluster size, executor memory, number of executors etc. and experience in Azure Synapse/Databricks (or similar).
 </li>
 <li>
 Expertise in ML development platforms such as Azure Machine Learning, Azure AI Foundry + Azure OpenAI
 </li>
 <li>
 1+ year(s) experience creating publications (e.g., patents, peer-reviewed academic papers).
 
 </li>
 </ul>
 This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled.
 
 
 Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance with religious accommodations and/or a reasonable accommodation due to a disability during the application process, read more about
 
 requesting accommodations.
 
</div>

Data & Applied Scientist II

Amadeus

<div>
 
 Job Title
 
 
 
 Data Scientist 3
 
 
 
 About The Role
 
 
 
 You will join a multidisciplinary team of software engineers, data engineers, data scientists, product specialists, and designers working on next-generation
 
 AI-powered solutions
 
 for airport and airline operations. As part of the Data Spark
 
 Agile Release Train
 
 , you will collaborate closely with colleagues in Nice and London.
 
 
 Your key responsibility will be to
 
 design, automate, and optimize machine learning workflows
 
 that power:
 
 
 <ul>
 <li>
 Real-time analytics and inferencing
 </li>
 <li>
 Predictive modeling
 </li>
 <li>
 ML foundations for generative AI capabilities
 
 
 </li>
 </ul>
 
 Key Responsibilities
 
 
 
 <ul>
 <li>
 Lead the design, implementation, and maintenance of scalable, cloud-native ML pipelines (primarily on Microsoft Azure within a multi-cloud strategy) for batch and streaming workloads.
 </li>
 <li>
 Automate the end-to-end lifecycle of ML models: data preparation, experimentation, tuning, deployment, monitoring, and continuous improvement with a focus on reproducibility and governance.
 </li>
 <li>
 Collaborate with software engineers to ensure robust integration of ML models into production systems, including API contracts, SLAs, fallbacks, and graceful degradation strategies.
 
 
 </li>
 </ul>
 
 About The Ideal Candidate
 
 
 
 <ul>
 <li>
 Proven experience building and maintaining production-grade ML pipelines in cloud environments across real-world projects.
 </li>
 <li>
 Strong hands-on experience with ML frameworks (e.g., scikit-learn, TensorFlow, PyTorch) and solid understanding of model development patterns (supervised learning, time-series forecasting, model evaluation).
 </li>
 <li>
 Practical MLOps expertise with tools like Azure Machine Learning, MLflow, or similar platforms for experiment tracking, pipeline orchestration, and model deployment.
 </li>
 <li>
 Strong command of Python for production-grade deliveries, including testing, code reviews, documentation, packaging, and version control.
 </li>
 <li>
 Experience working in Agile teams (Scrum, SAFe).
 
 
 </li>
 </ul>
 
 
 
 Diversity &amp; Inclusion
 
 
 
 
 
 Amadeus aspires to be a leader in Diversity, Equity and Inclusion in the tech industry, enabling every employee to reach their full potential by fostering a culture of belonging and fair treatment, attracting the best talent from all backgrounds, and as a role model for an inclusive employee experience.
 
 
 Amadeus is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to gender, race, ethnicity, sexual orientation, age, beliefs, disability or any other characteristics protected by law.
</div>

Data Scientist 3

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