The role

We seek an experienced Senior ML Solutions Architect to support customers leveraging Nebius Token Factory's serverless inference platform for open-source LLMs across multiple modalities. In this role, you will be collaborating with clients to design and implement customized LLM-based solution and architect scalable AI applications using our served models, and working together with our backend team to improve our platform to match the clients' needs.

You’re welcome to work remotely from Singapore.

Your responsibilities will include: 

• Design and implement LLM-based solutions using Nebius Token Factory’s inference services to drive business value and support customer goals
• Build production-ready applications leveraging our serverless LLM APIs, including multimodal models (text, vision, audio) and domain-specific models
• Provide technical expertise in prompt engineering, RAG architectures, model selection, and inference optimization
• Collaborate with product and engineering teams to surface customer feedback and shape the platform roadmap
• Guide customers in scaling from POC to production with a focus on performance, reliability, and cost efficiency

We expect you to have: 

• 5+ years of experience in ML/AI systems, with at least 2 years focused on LLMs and generative AI
• Deep knowledge of the LLM ecosystem, including model architectures and fine-tuning approaches
• Hands-on experience with:
  • Prompt engineering and LLM pipeline development, including evaluation
  • Agentic frameworks such as Langchain, Langsmith, smolagents, or equivalent
  • Vector databases and RAG implementation patterns
  • Deploying LLM-powered applications using APIs from OpenAI, Anthropic, or open-source models
• Strong Python programming skills
• Excellent communication skills, with the ability to clearly explain technical concepts to diverse audiences

It would be an added bonus if you have: 

• Experience with inference frameworks and libraries (e.g., vLLM, SGLang, TensorRT-LLM, Transformers)
• Familiarity with inference optimization techniques such as quantization, batching, caching, and routing
• Work with multimodal AI models (e.g., vision-language, speech)
• Proficiency with DevOps tools (Docker, Kubernetes)
• Contributions to open-source ML/AI projects

Preferred technical stack:

• Programming Languages – Python
• ML Frameworks and Libraries – vLLM, SGLang, TensorRT-LLM, Transformers, OpenAI/Anthropic SDKs
• Frameworks for Agentic Pipelines – Langchain / Langsmith / smolagents / equivalent
• API and Web Frameworks – FastAPI, Flask
• MLOps and DevOps tools – Kubernetes (K8s), Docker, Git
• Cloud Platforms – AWS (SageMaker, Bedrock), GCP (Vertex AI), Azure (Azure ML)

HPC & Cloud Infrastructure Engineer

Important Information

Location: Singapore

12 months contract

Job Summary

We’re hiring an HPC & Cloud Infrastructure Engineer to design, deploy, and optimize high-performance computing environments across on-prem and cloud. You’ll manage HPC clusters, interconnects, job schedulers, and enable AI/ML workloads at scale while driving automation and cost efficiency

Job Description

Architect, deploy, and manage HPC clusters with job schedulers, parallel file systems, and cluster management tools

Design, configure, and troubleshoot Infiniband high-throughput, low-latency interconnects for HPC/distributed workloads

Own PBS Professional scheduling: deployment, queue optimization, custom job submission scripts, workload management

Administer RHEL-based systems: performance tuning, package management, security hardening, patching via Red Hat Satellite and Ansible

Build and maintain cloud HPC environments on AWS, Azure, and GCP – provisioning, hybrid setups, migrations, and cost optimization

Implement Infrastructure as Code using Terraform/Ansible and integrate with CI/CD pipelines for reproducible infrastructure

Enable GPU & AI/ML workloads: containers, TensorFlow, PyTorch, scikit-learn, Keras, MXNet; support MLOps pipelines for training and deployment

Optimize parallel applications using MPI and OpenMP; debug and scale distributed/shared memory workloads

Drive monitoring, logging, and alerting for cluster health, job efficiency, and resource utilization

Required Skills and Experience

High-Performance Computing

Hands on experience in managing HPC clusters with job scheduler, cluster management parallel programming libraries, and parallel filesystems.

Knowledge of resource scheduling and job optimization for efficient workload management

Infiniband (Networking)

Hands-on experience with high-throughput, low-latency interconnect technologies like Infiniband.

Ability to design, configure, and troubleshoot interconnects in HPC or distributed environments.

Operating Systems and Environments 

Administration and configuration of RHEL-based systems.

Performance tuning, package management, and security hardening.

Knowledge of Red Hat Satellite and Ansible for automation.

Job Scheduling with PBS Professional 

Experience in deploying and managing PBS Professional for scheduling and workload management in HPC environments.

Customizing job submission scripts and optimizing job queues.

Parallel Programming Libraries

MPI (Message Passing Interface) and OpenMP (Open Multi-Processing):

Proficiency in writing, debugging, and optimizing parallelized code.

Experience with scaling applications across HPC systems.

Understanding of distributed memory (MPI) and shared memory (OpenMP)

paradigms.

Cloud Platforms

AWS, Azure, Google Cloud:

Expertise in provisioning, configuring, and managing services on all three platforms.

Cross-platform migration and hybrid cloud solutions knowledge.

Proficiency in managing high-performance computing (HPC) clusters on the cloud.

Deep understanding of cost optimization, security, and cloud native development tools (e.g., Kubernetes, Terraform).

Infrastructure as Code (IaC) 

Ability to design, deploy, and maintain infrastructure using automation and configuration management tools.

CI/CD pipeline integration for IaC workflows.

GPU & AI Libraries and Tools 

Hands-on experience with container technologies.

Hands-on experience with TensorFlow, PyTorch, scikit-learn, Keras, or MXNet.

Familiarity with AI/ML pipelines, model training, and optimization.

Knowledge of MLOps tools for deploying and monitoring models

About Encora

Encora is a global company that offers Software and Digital Engineering solutions. Our practices include Cloud Services, Product Engineering & Application Modernization, Data & Analytics, Digital Experience & Design Services, DevSecOps, Cybersecurity, Quality Engineering, AI & LLM Engineering, among others.

At Encora, we hire professionals based solely on their skills and do not discriminate based on age, disability, religion, gender, sexual orientation, socioeconomic status, or nationality.

Responsibilities

We are looking for an exceptional AI Researcher to join our growing AI team. In this role, you will design, build, deploy, and improve ML/LLM-powered services and features that power intelligent automation and AI-driven product experiences across the Workato platform. You will work closely with our Engineering, Product, and Design teams to define and track product metrics and evaluation strategies, design customer-facing experiments and dive deep to provide actionable insights.This role is ideal for someone who combines strong ML/LLM intuition, software engineering skills and a practical mindset for shipping reliable, scalable AI systems.

• Build and improve AI services using LLMs and custom machine learning models for production use cases.

• Design, develop, and operate ML/LLM systems end-to-end, from prototyping to deployment and monitoring.

• Write high-quality Python code that is testable, maintainable, and efficient.

• Improve validation, observability, and performance monitoring for ML services (quality, latency, reliability, cost).

• Partner cross-functionally with product managers, platform engineers, and other stakeholders to ship AI-powered product capabilities.

• Evaluate and improve existing implementations by identifying bottlenecks, bugs, and opportunities for optimization.

• Design controlled experiments to test the features for our AI-based products and perform deep analysis from the results to find actionable insights

• Contribute to technical design and code reviews, helping raise engineering quality across the team.

• Experiment and iterate on model behavior, prompting, retrieval, tool use, or orchestration strategies to improve user outcomes.

Requirements

Qualifications / Experience / Technical Skills

• Experience with tool-use agents or workflow-aware AI systems.

• Experience building AI products in enterprise SaaS environments.

• Experience with A/B testing and statistical significance techniques.

• Experience with LLMOps/MLOps tooling and practices (monitoring, evaluation pipelines, model rollout, CI/CD).

• Experience working with modern data warehouses such as Amazon Redshift Snowflake.

Job Req ID: 2724

Mission Summary:

We are seeking an experienced and visionary Principle Level Tech Lead Manager to build and lead our new Machine Learning (ML) Acceleration team. This pivotal role will drive the strategy, development, and execution of initiatives aimed at significantly accelerating ML model training. The ultimate goal is to drastically reduce the development cycle for new ML models and enable rapid hot-patching for issues within our deployed autonomous vehicle services.

You will be a hands-on leader, blending deep technical expertise in ML systems and performance optimization with strong leadership and people management skills. You will recruit, mentor, and grow a high-performing team of engineers, fostering a culture of innovation, collaboration, and continuous improvement.

What you'll be doing:

• Team Leadership & Management:
• Build, lead, and manage a high-performing team of ML and infra engineers focused on acceleration.
• Provide technical guidance, mentorship, and career development opportunities to team members.
• Foster a collaborative and inclusive team environment.
• Define team goals, priorities, and roadmap in alignment with company objectives.
  
  
• Technical Strategy & Execution:
• Define the technical vision and strategy for ML acceleration across the organization.
• Identify and evaluate cutting-edge technologies and methodologies for speeding up ML training, including but not limited to data pipeline optimization, large scale distributed training, data loader optimization, hardware acceleration, and model optimization techniques.
• Design, develop, and implement scalable and efficient ML acceleration solutions.
  
  
• Cross-functional Collaboration:
• Collaborate closely with ML research, ML Training platform, and product teams to understand their needs and integrate acceleration solutions seamlessly.
• Communicate complex technical concepts and strategies to both technical and non-technical stakeholders.
• Act as a technical expert and advocate for ML acceleration initiatives across the company.
  
  
• Impact & Measurement:
• Regularly measure and report on the impact of acceleration efforts.
• Continuously seek opportunities for further optimization and innovation.
  
  

What we're looking for:

• Experience:
• Bachelor’s degree in Computer Science, a related technical field, or equivalent practical experience
• 8+ years of experience in software engineering, with at least 5+ years focused on Machine Learning systems and model performance optimization.
• 3+ years of experience in a technical lead or management role, with a proven track record of building and leading high-performing teams.
• Extensive experience with large-scale ML model training and deployment, ideally in a production environment.
• Strong understanding of distributed systems and cloud computing platforms (e.g., AWS, GCP, Azure).
  
  
• Technical Skills:
• Deep expertise in ML frameworks such as PyTorch or JAX.
• Proficiency in performance profiling and optimization techniques for Deep Neural Networks.
• Strong programming skills in Python; C++ experience is a plus.
• Knowledge of MLOps principles and practices.
  
  
• Leadership Skills:
• Exceptional leadership, communication, and interpersonal skills.
• Ability to attract, hire, and retain top engineering talent.
• Proven ability to drive complex technical projects from conception to completion.
• Strong problem-solving skills and a proactive, results-oriented mindset.
• Ability to thrive in a fast-paced, dynamic environment.

About WPP Media  

WPP is the trusted growth partner for the world’s leading brands. With exceptional talent, trusted data and intelligence, and world-class partnerships – all united by our pioneering agentic marketing platform, WPP Open – we help clients navigate change, capture opportunity, and deliver transformational growth.   

WPP Media is WPP's AI-driven media operating unit, bringing together media, data, and partnerships to deliver creative personalisation at scale. Connected through WPP Open and powered by Open Intelligence, clients see exactly where, how, and why their media investment is working.  

For more information, visit wppmedia.com. 

 Role Summary and Impact 

The Media Futures Group AI Squad is a small, agile, and autonomous innovation team dedicated to charting the course for AI transformation across APAC for one of WPP’s most important and strategic global technology clients. 
 
By seamlessly integrating AI across media, creative, and production, this cross-functional squad delivers cutting-edge innovation and pioneering work to deliver more effective and efficient business outcomes. 
 
The team’s mission is to solve specific business challenges by delivering demonstrable proof of AI-enabled marketing improvements, providing strategic guideposts for the broader organization. 
 
Utilizing best-in-class tools like WPP Open and Gemini, you will innovate and deliver award-winning creative ideas while building, testing, and scaling solutions that enhance effectiveness, efficiency, and execution for our clients. 

 The AI Solutions Engineer serves as the master builder executing the construction. This highly technical role provides the dedicated, hands-on development support required to forcefully move theoretical architectural visions out of isolated testing environments and into highly secure, Google-approved, enterprise-grade production infrastructures. The AI Solutions Engineer bridges the critical, often perilous gap between a fragile, locally hosted prototype and a highly scalable, robust organizational tool capable of withstanding massive global traffic. 

Responsibilities 

• API & UI Development: Construct complex backend Application Programming Interface (API) connections and develop the secure user interfaces absolutely necessary to bridge the proprietary WPP Open platform with localized campaign tools and highly guarded client data lakes. 

• Live Deployment & Debugging: Execute rapid debugging, performance analysis, and troubleshooting of both machine learning models and underlying cloud infrastructure in real-time to prevent catastrophic performance degradation or service outages during live campaign sprints. 

• Security & Compliance: Strictly document the deployed technical architecture upon the conclusion of active campaigns. Solidify security protocols and data pipelines to ensure absolute compliance with enterprise risk standards and global data privacy regulations. 

• Codebase Handover: Facilitate the seamless handover of the finalized codebase to centralized, operations-driven technology teams, ensuring pristine code quality, proper containerization, and long-term deployment readiness. 

Skills and Experience 

• A minimum of 5 years of applied experience in software engineering, highly technical support, or complex solutions engineering. 

• At least 2 years focused exclusively on deploying machine learning (ML) or natural language processing (NLP) applications in live commercial environments. 

• Exceptional coding and scripting capabilities, primarily utilizing Python, Java, C++, or Go. 

• Deep, academic understanding of data structures, complex algorithms, and object-oriented software design principles. 

• Proven, hands-on experience troubleshooting, debugging, and reproducing obscure issues within advanced machine learning frameworks, specifically TensorFlow, Keras, and PyTorch. 

• Extensive operational experience navigating public cloud services, specifically Google Cloud Platform (GCP) components such as Kubernetes, Compute Engine, and Apache Beam. 

• Thorough understanding of computer networking, peering with private clouds, managing highly distributed systems, and Git version control. 

Key Competencies 

• Vertex MLOps & Cloud Infrastructure Deployment: Capable of establishing robust, automated machine learning pipelines using Vertex AI Pipelines and Google Cloud Platform components like Kubernetes and Compute Engine. Must ensure that generative AI tools are properly containerized to scale securely and withstand massive global traffic during live campaigns. 

• Vertex AI Mastery: Deep, hands-on proficiency with Google Cloud's Vertex AI platform as the exclusive, mandated environment for model deployment. Capable of navigating the Vertex Model Garden, configuring foundational models (like Gemini), and executing parameter-efficient fine-tuning for specific campaign needs 

• Secure RAG & Grounding Pipeline Engineering: Expert ability to build complex Retrieval-Augmented Generation (RAG) architectures within Vertex AI. Must demonstrate high technical acumen in securely connecting generative models to highly guarded, proprietary enterprise data lakes and Customer Data Platforms without exposing PII or violating data governance. 

• Rapid Problem-Solving: Ability to diagnose and repair code-level pipeline failures or UI breakdowns under the extreme pressure of a live campaign sprint. 

• Extreme Psychological Resilience: Thriving in highly volatile, fast-paced deployment environments where prototype code must be hardened instantly. 

Life at WPP Media & Benefits  

Our passion for shaping the next era of media is powered by our commitment to Be Extraordinary, investing in our employees to inspire transformational creativity. We also Lead Optimistically, firmly believing in and Championing Growth and Development for every individual. This commitment allows WPP Media employees to leverage the extensive global WPP Media & WPP networks to pursue their passions, build vital professional connections, and learn at the cutting edge of marketing and advertising.  

We Create an Open environment built on trust and respect, where everyone feels they belong and has opportunities to progress. This inclusive culture is fostered through a variety of employee resource groups and frequent in-office events showcasing team wins, sharing thought leadership, and celebrating holidays and milestone events. Our comprehensive benefits package reflects this commitment, including competitive medical, vision, and dental insurance, significant paid time off, preferential partner discounts, and employee mental health awareness days.    

WPP Media is an equal opportunity employer and considers applicants for all positions without discrimination or regard to characteristics. We believe the best work happens when we're together, fostering creativity, collaboration, and connection in this open and supportive environment. That's why we’ve adopted a hybrid approach, with teams in the office around four days a week. If you require accommodations or flexibility, please discuss this with the hiring team during the interview process.  

Please note that while our philosophy is the same across WPP, benefits may vary by office/country. 

#LI-Regional

We’re not hiring someone to run models. We’re hiring someone who builds systems that think.

At SimplifyNext, our AI Engineers are core to how we deliver transformation — designing and deploying intelligent systems that genuinely change how organisations operate. You won’t be a supporting act to another team. You’ll be the one building the agents, pipelines, and infrastructure that make our AI products real.

We work across public sector and enterprise, at the intersection of AI, automation, and product-led transformation. If you’re energised by hard engineering problems, care about production outcomes - not just research benchmarks - and want your work to reach real users at scale, read on.

What You'll Do

1. Build Agentic AI Systems

• Design and build sophisticated AI agents capable of independent operation, complex decision-making, and self-correction across domain-specific contexts.
• Develop orchestration workflows using frameworks such as LangChain, LangGraph, and/or the Microsoft Bot Framework to create robust conversational and task-oriented agents.
• Implement Retrieval-Augmented Generation (RAG) systems that connect agents to live, accurate knowledge bases — reducing hallucinations and improving output quality.
• Build Memory, Reasoning, and Planning (MRP) capabilities so agents can maintain context, reason across information, and execute multi-step plans.
• Design Agent-to-Agent (A2A) communication protocols that allow multiple autonomous agents to collaborate, delegate tasks, and exchange information securely.
• Work with LLM serving solutions (Ollama, vLLM) to ensure efficient, scalable inference in production environments.

2. Deploy and Operate at Scale

• Deploy and manage AI systems on major cloud platforms — AWS, Azure, or GCP — ensuring high availability, security, and scalability.
• Containerise applications with Docker and orchestrate deployments with Kubernetes, including end-to-end on-premise cluster setup and management.
• Build and maintain CI/CD pipelines using tools like Argo (Argo Workflows, Argo CD) for reliable, automated delivery of AI services.
• Expose AI capabilities through well-documented, performant APIs that integrate seamlessly with client-facing systems and applications.

3. Train and Fine-tune Models

• Train, fine-tune, and optimise custom AI models using TensorFlow and/or PyTorch, particularly where off-the-shelf models don’t meet specific project requirements.
• Run structured experiments — hyperparameter tuning, ablations, model evaluation — to hit performance targets with confidence.
• Stay current with the latest developments in large language models and agentic AI, and bring new techniques into our systems proactively.
• Work across diverse data modalities: text, image, time-series, and graph-structured data.

Who You Are

Must-Have

• Production mindset — you’ve shipped AI systems to real users, not just demos.
• Strong fundamentals in agentic AI — you understand how to design agents that are reliable, not just impressive.
• Hands-on with at least one major cloud platform (AWS, Azure, or GCP) for deploying and managing AI workloads.
• Solid Python engineering skills and comfort working in a Git-based, collaborative codebase.
• Experience with containerisation (Docker) and orchestration (Kubernetes) in real deployment contexts.
• Clear communicator — able to explain complex technical decisions to non-technical stakeholders.

Good to Have

• 3–7 years in AI/ML engineering or a closely related role (data engineering, MLOps, applied research).
• Hands-on experience with LangChain, LangGraph, Ollama, vLLM, or similar orchestration and serving tools.
• Demonstrable RAG system implementation and optimisation experience.
• Familiarity with Memory, Reasoning, and Planning (MRP) concepts and Agent-to-Agent (A2A) protocol design.
• Experience with MLOps tooling, particularly Argo Workflows and Argo CD.
• Proficiency in TensorFlow and/or PyTorch for model training and fine-tuning.
• Exposure to knowledge graphs, semantic web technologies, or prompt engineering strategies.

This role is not for you if…

• You’ve only worked with AI in sandboxed or prototype environments and haven’t dealt with production reliability challenges.
• You prefer to work from a detailed spec rather than figuring out the right approach as you go.
• You treat deployment as someone else’s problem
• You measure success by models trained, not by outcomes delivered.

Why SimplifyNext

We partner with governments and enterprises to shift from project delivery to product thinking. That means working on problems that genuinely matter — healthcare access, business licensing, workforce development — and being held accountable for outcomes, not just deliverables.

We are seeking a skilled MLOps & Agentic Platform Engineer. This role involves managing model registries, developing continuous training loops, and implementing A/B testing infrastructure. The ideal candidate will have a strong DevOps/MLOps background and be adept at deploying scalable microservices and building observability dashboards.

Responsibilities:

• Manage model registries, continuous training loops, and A/B testing infrastructure.
• Deploy agents as scalable microservices on Kubernetes.
• Build observability dashboards to track token usage, latency, and agent reasoning paths.

Qualifications:

• Strong DevOps/MLOps background (Kubernetes, Docker, Terraform).
• Experience with MLflow, Weights & Biases, or LangSmith.
• Knowledge of building scalable microservice architectures.

Job's mission

As a Senior Specialist in AI/ML within ASM’s Operations Intelligence function, you will play a pivotal role in reimagining how data, artificial intelligence, and intelligent automation transform global operations. You will design, build, and deploy scalable AI and machine learning solutions that optimize semiconductor supply chain, manufacturing, and logistics performance. By translating complex operational challenges into impactful AI-driven solutions, you will help demonstrate the power of agentic AI, large language models, and advanced analytics—driving measurable business outcomes and shaping the future of smart manufacturing at scale.

What you will be working on

• Design, develop, and deploy advanced prediction models, including Agentic AI systems and Large Language Models (LLMs), to drive automation, intelligent decision-making, and innovation across Global Operations.
• Collaborate with leaders, program managers, and cross-functional teams to integrate AI into existing systems.
• Stay abreast of the latest advancements in machine learning techniques, Agentic AI, and LLMs, and evaluate their applicability to Global Operations’ business challenges.
• Analyze business problem statements, translate business problem statements into data and AI requirements, collaborate with tech team to identify data sources, extract and transform data, perform data modelling, simulate outputs, test to produce data and knowledge for AI Solutions.
• Automate and integrate data and AI solution development and deployment; identify and prioritize opportunities, and develop proof-of-concepts for AI/ML applications to automate manual business processes.
• Work closely with data engineers, data analysts/scientists, senior management, and other stakeholders to support digital transformation.

What we are looking for

What sets you apart

About the role 

We are seeking an experienced Machine Learning Lead to helm our Machine Learning team. 

In this pivotal role, you will be the engineering architect behind Vulcan’s core AI capabilities. You will act as the nexus between Research, Platform, and Product. Your mission is to translate cutting-edge findings on GenAI threats into robust, production-ready machine learning models that power our GenAI Security Guardrails (Blue Team) and Automated Vulnerability Assessment (Red Team). 

Crucially, you will serve as the bridge between deep tech and business strategy, articulating technical constraints (like FLOPS and latency) to leadership and clients while guiding the engineering direction. 

Key Responsibilities

1. Model Development & Optimization (Training & Fine-tuning):

• Research to Production: Collaborate with the Security Research Team to operationalize new threat detection techniques. They identify the "what" (e.g., new prompt injection patterns); you determine the "how" (model architecture, training strategy). 
• Fine-tuning & Adaptation: Lead the fine-tuning of Language Models (e.g., using LoRA/PEFT) to optimize for our supported muti-lingual languages and specific security intents.
• Multimodal Readiness: Prepare the system for Multimodal (Text + Image/Audio) capabilities. Evaluate and implement models to detect visual prompt injections and non-textual threats as the product evolves. 

2. MLOps& Data Infrastructure: 

• Enhance & Scale MLOps: Take ownership of our existing ML pipelines. Focus on optimizing and scaling CI/CD/CT workflows to improve training efficiency and deployment velocity. 
• Data Governance: Implement and enforce rigorous Data Versioning strategies (e.g., DVC) to ensure complete reproducibility of model artifacts and datasets.
• Monitoring & Reliability: Maintain rigorous monitoring for model drift and performance, ensuring high reliability in a production security environment.

3. Cross-Functional Implementation & Leadership:

• Platform Collaboration: Work closely with the Platform Engineering Team to integrate ML models into the broader product architecture. Ensure seamless interaction between model inference services and the main platform logic. 
• Team Leadership: Lead and mentor Machine Learning Engineers, fostering a culture of engineering rigor, code quality, and operational excellence.
• Resource Management: Manage GPU resources and compute budgets effectively for both training and inference workloads. 

4. Technical Strategy & Stakeholder Management:

• Translating Tech to Business: Act as the technical voice of the ML team. You must effectively explain complex ML concepts (e.g., FLOPS, quantization trade-offs, model latency vs. accuracy) to executive leadership and clients. 
• Cost-Benefit Analysis: Justify compute resource investments. Articulate the trade-off between infrastructure costs (GPU hours) and performance gains to non-technical stakeholders. 

Qualifications

• Experience: 5+ years in Machine Learning Engineering, with specific experience in leading technical projects or mentoring engineers.
• Communication & Business Acumen: Exceptional ability to distill complex technical topics (e.g., compute complexity, infrastructure costs) into clear, business-relevant insights for decision-makers.
• MLOps Proficiency: Proven experience in optimizing ML pipelines and infrastructure. Familiarity with tools like MLflow, Kubeflow, Airflow, and Data Versioning tools (DVC, etc.).
• Engineering First: Proficient in Python, Docker, and Kubernetes. You treat ML models as software artifacts that need testing and version control.
• NLP & LLM Expertise: Experience with Transformer architectures, Embeddings, and LLM fine-tuning. Familiarity with frameworks like PyTorch, Hugging Face, and vLLM.
• Language Support: Experience processing or fine-tuning models for multi-lingual environments. 

Nice to Have

• Multimodal Expertise: Experience working with Multimodal models (Image-to-Text, Text-to-Image, VLMs like CLIP, LLaVA).
• Security Awareness: Understanding of GenAI security threats (e.g., Prompt Injection).
• High-Performance Computing: Experience optimizing inference speed (quantization, distillation, vLLM) for real-time applications.
• Vector Database: Experience with Vector DBs for RAG applications. 

Why Join Us? 

• Innovative Environment: Be part of a company at the forefront of technology to provide security in GenAI, with opportunities to work on groundbreaking projects. 
• Growth Opportunities: Take your career to new heights with our career development programs and growth-focused culture. 
• Dynamic Team: Join a multi-cultural and dynamic team of dedicated professionals who inspire and support each other. 
• Compensation: Competitive salary and benefits package, commensurate with experience and performance. 

Application Process 

• If you're ready to embark on this exciting journey and contribute to shaping the future of GenAI security, please submit your resume outlining your relevant experience and motivation for applying.
• If you prefer a direct connection or have specific questions about our vision, feel free to reach out to our Co-founder, Alvin Kwock, via LinkedIn. 

We are seeking a seasoned Technical Leader to build and scale the foundational compute infrastructure that powers our state-of-the-art AI models—from multimodal training data pipelines to high-throughput, low-latency video generation.

Responsibilities

You will be the core engineer responsible for building the robust, efficient, and scalable platform that enables our research and production teams to rapidly iterate on HeyGen's generative video models. Your contributions will directly impact model performance, developer productivity, and the final quality of every AI-generated video.

• Optimize GPU Utilization: Design and implement mechanisms to aggressively optimize GPU and cluster utilization across thousands of devices for inference, training, data processing and large-scale deployment of our state-of-art video generation models.

• Develop Large-Scale AI Job Framework: Build highly scalable, reliable frameworks for launching and managing massive, heterogeneous compute jobs, including multi-modal high-volume data ingestion/processing, distributed model training, and continuous evaluation/benchmarking.

• Enhance Observability: Develop world-class observability, tracing, and visualization tools for our compute cluster to ensure reliability, diagnose performance bottlenecks (e.g., memory, bandwidth, communication).

• Accelerate Pipelines: Collaborate closely with AI researchers and AI engineers to integrate innovative acceleration techniques (e.g., custom CUDA kernels, distributed training libraries) into production-ready, scalable training and inference pipelines.

• Infrastructure Management: Champion the adoption and optimization of modern cloud and container technologies (Kubernetes, Ray) for elastic, cost-efficient scaling of our distributed systems.

Minimum Requirements

We are looking for a highly motivated engineer with deep experience operating and optimizing AI infrastructure at scale.

• Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience.

• 5+ years of full-time industry experience in large-scale MLOps, AI infrastructure, or HPC systems.

• Experience with data frameworks and standards like Ray, Apache Spark, LanceDB

• Strong proficiency in Python and a high-performance language such as C++  for developing core infrastructure components.

• Deep understanding and hands-on experience with modern orchestration and distributed computing frameworks such as Kubernetes and Ray.

• Experience with core ML frameworks such as PyTorch, TensorFlow, or JAX.

Preferred Qualifications

• Master's or PhD in Computer Science or a related technical field.

• Demonstrated Tech Lead experience, driving projects from conceptual design through to production deployment across cross-functional teams.

• Prior experience building infrastructure specifically for Generative AI models (e.g., diffusion models, GANs, or large language models) where cost and latency are critical.

• Proven background in building and operating large-scale data infrastructure (e.g., Ray, Apache Spark) to manage petabytes of multi-modal data (video, audio, text).

• Expertise in GPU acceleration and deep familiarity with low-level compute programming, including CUDA, NCCL, or similar technologies for efficient inter-GPU communication.

What HeyGen Offers

• Competitive salary and benefits package.
• Dynamic and inclusive work environment.
• Opportunities for professional growth and advancement.
• Collaborative culture that values innovation and creativity.
• Access to the latest technologies and tools.

HeyGen is an Equal Opportunity Employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.

We are seeking a seasoned Software Engineer to build and scale the foundational compute infrastructure that powers our state-of-the-art AI models—from multimodal training data pipelines to high-throughput, low-latency video generation.

Responsibilities

You will be the core engineer responsible for building the robust, efficient, and scalable platform that enables our research and production teams to rapidly iterate on HeyGen's generative video models. Your contributions will directly impact model performance, developer productivity, and the final quality of every AI-generated video.

• Optimize GPU Utilization: Design and implement mechanisms to aggressively optimize GPU and cluster utilization across thousands of devices for inference, training, data processing and large-scale deployment of our state-of-art video generation models.

• Develop Large-Scale AI Job Framework: Build highly scalable, reliable frameworks for launching and managing massive, heterogeneous compute jobs, including multi-modal high-volume data ingestion/processing, distributed model training, and continuous evaluation/benchmarking.

• Enhance Observability: Develop world-class observability, tracing, and visualization tools for our compute cluster to ensure reliability, diagnose performance bottlenecks (e.g., memory, bandwidth, communication).

• Accelerate Pipelines: Collaborate closely with AI researchers and AI engineers to integrate innovative acceleration techniques (e.g., custom CUDA kernels, distributed training libraries) into production-ready, scalable training and inference pipelines.

• Infrastructure Management: Champion the adoption and optimization of modern cloud and container technologies (Kubernetes, Ray) for elastic, cost-efficient scaling of our distributed systems.

Minimum Requirements

We are looking for a highly motivated engineer with deep experience operating and optimizing AI infrastructure at scale.

• Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience.

• 5+ years of full-time industry experience in large-scale MLOps, AI infrastructure, or HPC systems.

• Experience with data frameworks and standards like Ray, Apache Spark, LanceDB

• Strong proficiency in Python and a high-performance language such as C++  for developing core infrastructure components.

• Deep understanding and hands-on experience with modern orchestration and distributed computing frameworks such as Kubernetes and Ray.

• Experience with core ML frameworks such as PyTorch, TensorFlow, or JAX.

Preferred Qualifications

• Master's or PhD in Computer Science or a related technical field.

• Demonstrated Tech Lead experience, driving projects from conceptual design through to production deployment across cross-functional teams.

• Prior experience building infrastructure specifically for Generative AI models (e.g., diffusion models, GANs, or large language models) where cost and latency are critical.

• Proven background in building and operating large-scale data infrastructure (e.g., Ray, Apache Spark) to manage petabytes of multi-modal data (video, audio, text).

• Expertise in GPU acceleration and deep familiarity with low-level compute programming, including CUDA, NCCL, or similar technologies for efficient inter-GPU communication.

What HeyGen Offers

• Competitive salary and benefits package.
• Dynamic and inclusive work environment.
• Opportunities for professional growth and advancement.
• Collaborative culture that values innovation and creativity.
• Access to the latest technologies and tools.

HeyGen is an Equal Opportunity Employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.

Role Overview

We are seeking highly motivated and curious individuals to join our Machine Learning team at Kronos Research. In this role, you will bridge the gap between advanced deep learning and financial markets, designing robust models for medium and high-frequency systematic trading strategies. You will manage the full ML lifecycle, from researching novel architectures to deploying scalable, low-latency models that directly drive trading revenue.

Key Responsibilities

• Feature Engineering: Analyze complex time-series data, orderbook dynamics and trade data to engineer high-signal features
• Deep Learning Architecture: Design and train deep-learning based models (MLP, LSTM, RNN, Transformers, RL agents, etc) tailored for financial trading environments
• Backtesting & Evaluation: Conduct comprehensive backtesting and simulation across various asset classes and exchanges; analyze trade execution and PnL attribution
• Model Deployment: Collaborate with engineering teams to optimize and deploy models into production
• MLOps & Automation: Build and maintain automated pipelines for data ingestion, model retraining, and continuous performance monitoring to streamline the research-to-production workflow

Qualifications

• Strong academic or professional foundation in machine learning, quantitative research, and/or other related STEM fields; open to both experienced candidates and highly-motivated fresh graduates
• Deep understanding of neural network architectures and their application to time-series forecasting
• Proficiency in Python and modern ML frameworks (PyTorch/TensorFlow/Jax); C++ preferred
• Solid command of probability theory, linear algebra and applied statistics
• Strong communication skills and able to articulate technical concepts with clarity
• High level of drive, curiosity and a passion for continuously learning in a fast-paced environment