ai research engineer

Hiring Talent

A fast paced startup Company is hiring an AI Research Engineer.Job Type: Full-time, onsiteShift Timings: 3 pm to 12 amLocation: Shahrah-e-Faisal, KarachiIndustry: AI / FintechResponsibilities&middot; Research and evaluate techniques for improving LLM reasoning, accuracy, and efficiency &mdash; including prompting strategies, fine-tuning, distillation, and model selection&middot; Design and run rigorous experiments with proper baselines, ablations, and statistical analysis to measure the impact of each technique&middot; Build evaluation frameworks and benchmarks to systematically measure model quality across Yara's key tasks&middot; Improve retrieval-augmented generation pipelines &mdash; embedding models, chunking strategies, re-ranking, query decomposition, and context window optimization&middot; Explore fine-tuning approaches (LoRA, RLHF, DPO) to adapt foundation models for domain-specific performance&middot; Study and reduce failure modes &mdash; hallucination, instruction drift, reasoning errors, and inconsistent structured output generation&middot; Stay current with the research landscape (papers, open-source releases, model updates) and identify techniques with high applicability to Yara's use cases&middot; Prototype emerging approaches (chain-of-thought, self-consistency, tool-use planning, multi-agent coordination) and measure real-world impact&middot; Translate research findings into production-ready improvements and document results for the engineering teamRequirements:&middot; 4-5 years in ML/AI research or applied AI engineering&middot; Strong understanding of LLM architectures, training dynamics, and inference optimization&middot; Hands-on experience with fine-tuning (LoRA, RLHF, DPO) and systematic model evaluation&middot; Proficient in Python and ML frameworks (PyTorch,HuggingFace Transformers)&middot; Experience designing experiments with proper controls, ablations, and reproducibility&middot; Familiarity with RAG systems, embedding models, and retrieval pipelines&middot; Strong written communication &mdash; ability to produce clear internal research reports and technical documentationInterested candidates may apply.Application Question(s):<ul><li>Are you willing to work onsite from 3 pm to 12 am ?</li></ul>Education:<ul><li>Bachelor's (Preferred)</li></ul>Experience:<ul><li>ML/AI research: 4 years (Preferred)</li><li>ML frameworks: 4 years (Preferred)</li><li>fine tuning and systematic model evaluation: 4 years (Preferred)</li><li>RAG system: 2 years (Preferred)</li></ul>Work Location: In person

AI Research Engineer

AI Research Engineer, FAIR Chemistry

Tether Operations Limited

<div>Join Tether and Shape the Future of Digital Finance
At Tether, we&rsquo;re not just building products, we&rsquo;re pioneering a global financial revolution. Our cutting-edge solutions empower businesses&mdash;from exchanges and wallets to payment processors and ATMs&mdash;to seamlessly integrate reserve-backed tokens across blockchains. By harnessing the power of blockchain technology, Tether enables you to store, send, and receive digital tokens instantly, securely, and globally, all at a fraction of the cost. Transparency is the bedrock of everything we do, ensuring trust in every transaction.
Innovate with Tether
Tether Finance: Our innovative product suite features the world&rsquo;s most trusted stablecoin, USDT, relied upon by hundreds of millions worldwide, alongside pioneering digital asset tokenization services.
But that&rsquo;s just the beginning:
Tether Power: Driving sustainable growth, our energy solutions optimize excess power for Bitcoin mining using eco-friendly practices in state-of-the-art, geo-diverse facilities.
Tether Data: Fueling breakthroughs in AI and peer-to-peer technology, we reduce infrastructure costs and enhance global communications with cutting-edge solutions like KEET, our flagship app that redefines secure and private data sharing.
Tether Education: Democratizing access to top-tier digital learning, we empower individuals to thrive in the digital and gig economies, driving global growth and opportunity.
Tether Evolution: At the intersection of technology and human potential, we are pushing the boundaries of what is possible, crafting a future where innovation and human capabilities merge in powerful, unprecedented ways.
Why Join Us?
Our team is a global talent powerhouse, working remotely from every corner of the world. If you&rsquo;re passionate about making a mark in the fintech space, this is your opportunity to collaborate with some of the brightest minds, pushing boundaries and setting new standards. We&rsquo;ve grown fast, stayed lean, and secured our place as a leader in the industry.
If you have excellent English communication skills and are ready to contribute to the most innovative platform on the planet, Tether is the place for you.
Are you ready to be part of the future?
About the job
As a member of the AI model team, you will drive innovation in reinforcement learning approaches for advanced models. Your work will optimize decision-making and adaptive behavior to deliver enhanced intelligence, improved performance, and domain-specific capabilities for real-world challenges. You will work across a broad spectrum of systems, including resource-efficient models designed for limited hardware environments and complex multi-modal architectures that integrate data such as text, images, and audio.
We expect you to have deep expertise in designing reinforcement learning systems and a strong background in advanced model architectures. You will adopt a hands-on, research-driven approach to developing, testing, and implementing novel reinforcement learning algorithms and training frameworks. Your responsibilities include curating specialized simulation environments and training datasets, strengthening baseline policy performance, and identifying as well as resolving bottlenecks in the reinforcement learning process. The ultimate goal is to unlock superior, domain-adapted AI performance and push the limits of what these models can achieve in dynamic, real-world environments.
Responsibilities<ul><li>
Develop and implement state-of-the-art reinforcement learning algorithms designed to optimize decision-making processes in both simulated and real-world settings. Establish clear performance targets such as reward maximization and policy stability.</li><li>
Build, run, and monitor controlled reinforcement learning experiments. Track key performance indicators while documenting iterative results and comparing outcomes against established benchmarks.</li><li>
Identify and curate high-quality simulation environments and training datasets that are tailored to specific domain challenges. Set measurable criteria to ensure that the selection and preparation of these resources significantly enhance the learning process and overall model performance.</li><li>
Systematically debug and optimize the reinforcement learning pipeline by analyzing both computational efficiency and learning performance metrics. Address issues such as reward signal noise, exploration strategy, and policy divergence to improve convergence and stability.</li><li>
Collaborate with cross-functional teams to integrate reinforcement learning agents into production systems. Define clear success metrics such as real-world performance improvements and robustness under varied conditions and ensure continuous monitoring and iterative refinements for sustained domain adaptation.</li></ul><ul><li>
A degree in Computer Science or related field. Ideally PhD in NLP, Machine Learning, or a related field, complemented by a solid track record in AI R&amp;D (with good publications in A* conferences).</li><li>
Proven experience with large-scale reinforcement learning experiments, including online RL techniques such as Group Relative Policy Optimization (GRPO), is essential. Your contributions should have led to measurable improvements in domain-specific decision-making and overall policy performance.</li><li>
Deep understanding of reinforcement learning algorithms is required, including state-of-the-art online RL methods and other gradient-based optimization approaches like policy gradients, actor-critic, and GRPO. Your expertise should emphasize enhancing policy stability, exploration, and sample efficiency in complex, dynamic environments.</li><li>
Strong expertise in PyTorch and relevant reinforcement learning frameworks is a must. Practical experience in developing RL pipelines, from simulation and online training to post-training evaluation and deploying RL-based solutions in production environments is expected.</li><li>
Demonstrated ability to apply empirical research to overcome reinforcement learning challenges such as sample inefficiency, exploration-exploitation tradeoffs, and training instability. You should be proficient in designing robust evaluation frameworks and iterating on algorithmic innovations to continuously push the boundaries of RL agent performance.</li></ul>
Important information for candidates 
Recruitment scams have become increasingly common. To protect yourself, please keep the following in mind when applying for roles:<ul><li>
Apply only through our official channels. We do not use third-party platforms or agencies for recruitment unless clearly stated. All open roles are listed on our official careers page: https://tether.recruitee.com/</li><li>
Verify the recruiter&rsquo;s identity. All our recruiters have verified LinkedIn profiles. If you&rsquo;re unsure, you can confirm their identity by checking their profile or contacting us through our website.</li><li>
Be cautious of unusual communication methods. We do not conduct interviews over WhatsApp, Telegram, or SMS. All communication is done through official company emails and platforms.</li><li>
Double-check email addresses. All communication from us will come from emails ending in @tether.to or @tether.io</li><li>
We will never request payment or financial details. If someone asks for personal financial information or payment at any point during the hiring process, it is a scam. Please report it immediately.</li></ul>
When in doubt, feel free to reach out through our official website.</div>

AI Research Engineer - Reinforcement Learning (100% remote Worldwide)

<div>Join Tether and Shape the Future of Digital Finance
At Tether, we&rsquo;re not just building products, we&rsquo;re pioneering a global financial revolution. Our cutting-edge solutions empower businesses&mdash;from exchanges and wallets to payment processors and ATMs&mdash;to seamlessly integrate reserve-backed tokens across blockchains. By harnessing the power of blockchain technology, Tether enables you to store, send, and receive digital tokens instantly, securely, and globally, all at a fraction of the cost. Transparency is the bedrock of everything we do, ensuring trust in every transaction.
Innovate with Tether
Tether Finance: Our innovative product suite features the world&rsquo;s most trusted stablecoin, USDT, relied upon by hundreds of millions worldwide, alongside pioneering digital asset tokenization services.
But that&rsquo;s just the beginning:
Tether Power: Driving sustainable growth, our energy solutions optimize excess power for Bitcoin mining using eco-friendly practices in state-of-the-art, geo-diverse facilities.
Tether Data: Fueling breakthroughs in AI and peer-to-peer technology, we reduce infrastructure costs and enhance global communications with cutting-edge solutions like KEET, our flagship app that redefines secure and private data sharing.
Tether Education: Democratizing access to top-tier digital learning, we empower individuals to thrive in the digital and gig economies, driving global growth and opportunity.
Tether Evolution: At the intersection of technology and human potential, we are pushing the boundaries of what is possible, crafting a future where innovation and human capabilities merge in powerful, unprecedented ways.
Why Join Us?
Our team is a global talent powerhouse, working remotely from every corner of the world. If you&rsquo;re passionate about making a mark in the fintech space, this is your opportunity to collaborate with some of the brightest minds, pushing boundaries and setting new standards. We&rsquo;ve grown fast, stayed lean, and secured our place as a leader in the industry.
If you have excellent English communication skills and are ready to contribute to the most innovative platform on the planet, Tether is the place for you.
Are you ready to be part of the future?
About the job
We're seeking experienced AI infrastructure Engineers to design and implement robust, scalable pipelines for massive data workloads. Join Tether&rsquo;s applied research team, where you&rsquo;ll contribute to high-impact projects that run across thousands of GPUs and drive cutting-edge video generation foundation development.
Responsibilities<ul><li>
Build and scale high-throughput data infrastructure optimized for video and multimodal content processing across large GPU clusters (e.g., H100/H200).</li><li>
Design core preprocessing algorithms for video, audio, text, and image modalities, enabling efficient extraction, synchronization, and normalization of temporal data.</li><li>
Build automated acquisition pipelines for sourcing large-scale video datasets, handling diverse formats, frame rates, annotations, and embedded audio.</li><li>
Architect robust systems for scalable evaluation and annotation, including prompt-based scoring, perceptual metrics, caption generation, and retrieval-based diagnostics.</li><li>
Collaborate with model researchers to co-design video model architectures (e.g. DiTs, VAEs, spatio-temporal transformers) and training schedules across pretraining and fine-tuning stages.</li><li>
Optimize distributed data loading and pipeline throughput for training at scale, ensuring robustness across model variants and modality combinations.</li><li>
Manage infrastructure to support experiment tracking, model versioning, and cross-team deployment workflows, integrating with production and research platforms.</li><li>
Support backend engineering across research, product, and creative teams to ensure seamless integration of data and model workflows from prototyping to inference.</li></ul><ul><li>
Proficient in Python with strong programming skills across backend, infrastructure, and data tooling domains.</li><li>
Strong software engineering experience, including 2+ years working with petabyte-scale data pipelines and systems across thousands of GPUs.</li><li>
Proven ability to architect and maintain large-scale distributed systems for data processing and delivery.</li><li>
Deep expertise in orchestration frameworks such as Kubernetes and SLURM with hands-on experience deploying and managing high-throughput workloads.</li></ul>
Preferred Qualifications<ul><li>
Practical experience on building pipelines and infrastructure with visual and multimodal datasets, including image/video pipelines.</li><li>
Experience in building video foundation infrastructure pipelines and workflows with collaboration of LLM and/or video foundation research and engineering teams is a strong advantage.</li></ul>
Important information for candidates 
Recruitment scams have become increasingly common. To protect yourself, please keep the following in mind when applying for roles:<ul><li>
Apply only through our official channels. We do not use third-party platforms or agencies for recruitment unless clearly stated. All open roles are listed on our official careers page: https://tether.recruitee.com/</li><li>
Verify the recruiter&rsquo;s identity. All our recruiters have verified LinkedIn profiles. If you&rsquo;re unsure, you can confirm their identity by checking their profile or contacting us through our website.</li><li>
Be cautious of unusual communication methods. We do not conduct interviews over WhatsApp, Telegram, or SMS. All communication is done through official company emails and platforms.</li><li>
Double-check email addresses. All communication from us will come from emails ending in @tether.to or @tether.io</li><li>
We will never request payment or financial details. If someone asks for personal financial information or payment at any point during the hiring process, it is a scam. Please report it immediately.</li></ul>
When in doubt, feel free to reach out through our official website.</div>

Senior AI Research Engineer - Data & Infrastructure (100% remote Worldwide)

<div>Meta is seeking a Research Engineer to join the Fundamental AI Research (FAIR) team, a research organization focused on advancing the state-of-the-art in AI and personal superintelligence. Based in Meta Superintelligence Labs (MSL) within the FAIR, you'll directly contribute to Meta&rsquo;s frontier model, while having the chance to collaborate with researchers and engineers across MSL. 
 
 
<h3 class="jobSectionHeader">Research Engineer, FAIR AI &amp; Society Responsibilities:
 
</h3><ul><li>Perform research to advance the science and technology of intelligent machines</li><li>
Develop and scale post-training pipelines for LLMs, including implementation of novel alignment algorithms</li><li>
Build engineering infrastructure to evaluate frontier models with regard to alignment and societal impact</li><li>
Develop and scale novel applications of pro-social artificial intelligence</li><li>
Work towards long-term research goals, while identifying immediate milestones</li><li>
Influence progress of relevant research communities by producing publications</li><li>
Open source high quality code and produce reproducible research</li></ul> 
 
 
<h3 class="jobSectionHeader">Minimum Qualifications: 
</h3><ul><li>
Currently has, or is in the process of obtaining a Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience. Degree must be completed prior to joining Meta</li><li>
Research background in machine learning, artificial intelligence, computational statistics, applied mathematics, or related areas</li><li>
Research publications reflecting experience in theoretical or empirical research</li><li>
Experience in developing and debugging in Python or similar programming languages</li><li>
Experience in analyzing and collecting data from various sources, including Hive, Spark, SQL etc</li><li>
Must obtain work authorization in the country of employment at the time of hire, and maintain ongoing work authorization during employment</li></ul> 
 
 
<h3 class="jobSectionHeader">Preferred Qualifications: 
</h3><ul><li>
Experience with large-scale model training, fine-tuning, and post-training (e.g., RLHF, DPO)</li><li>
Engineering expertise in distributed systems and deep learning frameworks (e.g., PyTorch)</li><li>
Research and engineering experience demonstrated via grants, fellowships, patents, internships, work experience, and/or coding competitions</li><li>
First-authored publications at peer-reviewed conferences, such as ICML, NeurIPS, ACL, EMNLP, ICLR, FacCT, CCS, IEEE S&amp;P, and other similar venues</li><li>
Experience solving complex problems and comparing alternative solutions, tradeoffs, and different points of view to determine a path forward</li><li>
Experience collaborating in a team environment on research projects</li><li>
Experience communicating research for public audiences of peers</li><li>
Recognized expert in identified research areas such as AI alignment and post-training of Large Language Models (LLMs)</li><li>
Keen interest in producing new science to understand intelligence and technology towards ensuring the positive societal impact of artificial intelligence</li></ul> 
 
 
<h3 class="jobSectionHeader">About Meta: 
</h3>
Meta builds technologies that help people connect, find communities, and grow businesses. When Facebook launched in 2004, it changed the way people connect. Apps like Messenger, Instagram and WhatsApp further empowered billions around the world. Now, Meta is moving beyond 2D screens toward immersive experiences like augmented and virtual reality to help build the next evolution in social technology. People who choose to build their careers by building with us at Meta help shape a future that will take us beyond what digital connection makes possible today&mdash;beyond the constraints of screens, the limits of distance, and even the rules of physics. 
 
 
<div>Meta is proud to be an Equal Employment Opportunity and Affirmative Action employer. We do not discriminate based upon race, religion, color, national origin, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, transgender status, sexual stereotypes, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics. We also consider qualified applicants with criminal histories, consistent with applicable federal, state and local law. Meta participates in the E-Verify program in certain locations, as required by law. Please note that Meta may leverage artificial intelligence and machine learning technologies in connection with applications for employment.
 
 
Meta is committed to providing reasonable accommodations for candidates with disabilities in our recruiting process. If you need any assistance or accommodations due to a disability, please let us know at accommodations-ext@meta.com. 
 
$122,000/year to $181,000/year + bonus + equity + benefits 
 
Individual compensation is determined by skills, qualifications, experience, and location. Compensation details listed in this posting reflect the base hourly rate, monthly rate, or annual salary only, and do not include bonus, equity or sales incentives, if applicable. In addition to base compensation, Meta offers benefits. Learn more about benefits at Meta.</div></div>

Research Engineer, FAIR AI & Society

Value Engineering

Reporting to AI Lead / Solution Architect, this is a senior technical leadership role responsible for: Designing and deploying state-of-the-art LLMs and multi-modal AI models. Implementing agentic AI systems with RAG, self-RAG, and memory-optimized architectures. Leading cloud-based GPU LLM training pipelines, including data labeling, preprocessing, model training, and post-processing. Extending AI models into front-end applications and back-end pipelines. Managing staging, development, and production environments with SSH deployment and editing. Driving complex problem-solving, critical thinking, and innovative AI solutionsKey Responsibilities1. LLM Cloud Training &amp; Pipelines (Mandatory)Design and deploy LLM training pipelines on cloud GPU infrastructure. Manage data labeling, preprocessing, and post-processing for training datasets. Optimize model training, memory usage, and inference efficiency. Implement RAG-based pipelines, agentic RAG, and self-RAG for knowledge augmentation.2. Deep Learning &amp; Model DevelopmentResearch, design, and implement Transformers, Vision Transformers, Diffusion Models. Optimize SAM models for tabular and image data, including preprocessing and post-processing. Implement multi-modal AI pipelines (text, image, video). Develop agentic AI models with adaptive memory and reasoning.3. Agentic AI &amp; OrchestrationDesign agent frameworks with orchestration, inter-agent communication, and protocol management. Integrate LangChain, LangGraph, or custom agent pipelines. Ensure AI agents are robust, scalable, and production-ready.4. Machine Learning for Real-World DataImplement ML models for tabular, image, and multi-modal data. Preprocess, normalize, and feature-engineer datasets for SAM and other model types. Translate research models into production pipelines with RAG augmentation.5. Front-End &amp; Back-End AI IntegrationExtend AI models into front-end applications (interactive AI features). Integrate AI models into back-end services for low-latency inference. Ensure seamless integration between AI models and application architecture.6. Production AI Infrastructure &amp; DeploymentDeploy AI models on MCP / GPU servers, distributed clusters, or cloud infrastructure. Manage staging, dev, and production environments, including SSH deployment, editing, and version control. Implement monitoring and observability using Prometheus, Grafana, and custom dashboards. Maintain CI/CD for AI models, training pipelines, and data workflows, including Git/GitHub workflow management.7. Research to Production TranslationBring experimental AI models into production-grade systems. Validate model performance, reliability, and safety. Implement reproducibility, versioning, and automated retraining pipelines.8. Mentorship &amp; LeadershipMentor junior AI engineers and researchers. Establish best practices for AI experimentation, deployment, and RAG/agentic architectures. Contribute to AI roadmap and enterprise AI strategy.Required Experience5+ years AI / Deep Learning / Research &amp; Production experience. Mandatory: Hands-on experience in LLM training pipelines, including cloud GPU infrastructure, data labeling, preprocessing, training, and post-processing. Deep knowledge of Neural Networks, Transformers, LLMs, Vision Transformers, Diffusion Models, Agentic AI, RAG/self-RAG pipelines. Hands-on experience with SAM models, tabular data ML, and preprocessing pipelines. Experience managing staging, dev, and production environments with SSH deployment, editing, and Git/GitHub workflow. Strong Computer Vision and Image Processing skills. Experience with agent orchestration, LangChain, LangGraph, and protocol optimization. Proven problem-solving, critical thinking, and innovative AI solution delivery. Experience integrating AI into front-end and back-end systems.Preferred ExperienceExperience with generative AI, diffusion models, multi-modal agents. Optimizing LLM memory, inference, and fine-tuning at scale. Experience in production RAG pipelines and knowledge-augmented AI systems.Application Question(s):<ul><li>Are you available to work from 2:00 PM &ndash; 11:00 PM shift Monday to Friday with alternate Saturday?</li></ul><ul><li>Are you comfortable working onsite from Bahria Phase 4, Rawalpindi?</li></ul><ul><li>How many years of experience do you have as AI Engineer?</li></ul>Work Location: In person

Lead AI Engineer

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AI Research Engineer

AI Research Engineer, FAIR Chemistry

AI Research Engineer - Reinforcement Learning (100% remote Worldwide)

Senior AI Research Engineer - Data & Infrastructure (100% remote Worldwide)

Research Engineer, FAIR AI & Society

Lead AI Engineer

Research Engineer, FAIR AI & Society