San Jose, CA, USA

We are seeking a creative and technically skilled Prompt Engineer to enhance large language model (LLM) performance across business-critical workflows. This position centers on designing, testing, and integrating strategies that drive intelligent agents and enterprise use cases. You will work closely with AI engineers, product teams, and domain experts to guarantee scalable, safe, and high-accuracy AI applications.

What you'll Do - Prompt Strategy & Design: Develop templates and multi-step chains tailored to specific business functions (e.g., sales enablement, support, knowledge management). Develop few-shot, zero-shot, and hybrid patterns for enhanced reasoning and context retention. Maintain libraries for reuse and version control.

• Function Calling & Tool Use: Implement LLM function calling to trigger APIs, databases, or internal tools. Build tool-use pipelines within agent workflows for complex task automation.

• Conversation Flow & Persona Design: Define and build agent personas, roles, and behaviors for domain-specific applications. Manage multi-turn conversations, memory handling, and contextual continuity.

• Enterprise-grade Optimization: Tailor prompts for performance in enterprise environments, prioritizing accuracy, privacy, fairness, and compliance. Collaborate with legal and security teams to mitigate hallucination, bias, and misuse risks.

• Testing & Evaluation: Tailor prompts for performance in enterprise environments, prioritizing accuracy, privacy, fairness, and compliance. Collaborate with legal and security teams to mitigate hallucination, bias, and misuse risks.

• Deployment & Integration: Partner with AI Agent Engineers to integrate prompts into agent workflows and orchestration pipelines. Maintain documentation and workflows for deployment in production environments.

What you need to succeed - 3+ years of experience in NLP, AI/ML product development, or application scripting - Strong grasp of LLM capabilities and limitations (e.g., OpenAI, Claude, Mistral, Cohere) - Experience crafting prompts and evaluation methods for enterprise tasks - Familiarity with frameworks like LangChain, Semantic Kernel, or AutoGen - Strong Python and API integration skills - Excellent written communication and structured thinking

Preferred Qualifications - Experience with LLM function calling, custom tool integration, and agent workflows - Background in UX writing, human-computer interaction, or instructional design - Understanding of enterprise compliance (e.g., SOC 2, GDPR) in AI systems - Bachelor's or equivalent experience in Computer Science, Computational Linguistics, Cognitive Science, or a related field

Redwood City, CA

Biohub is leading the new era of AI-powered biology to cure or prevent disease through its 501c3 medical research organization, with the support of the Chan Zuckerberg Initiative.

The Team Biohub supports the science and technology that will make it possible to help scientists cure, prevent, or manage all diseases by the end of this century. While this may seem like an audacious goal, in the last 100 years, biomedical science has made tremendous strides in understanding biological systems, advancing human health, and treating disease.

Achieving our mission will only be possible if scientists are able to better understand human biology. To that end, we have identified four grand challenges that will unlock the mysteries of the cell and how cells interact within systems — paving the way for new discoveries that will change medicine in the decades that follow:

Building an AI-based virtual cell model to predict and understand cellular behavior Developing state-of-the-art imaging systems to observe living cells in action Instrumenting tissues to better understand inflammation, a key driver of many diseases Engineering and harnessing the immune system for early detection, prevention, and treatment of disease As a Senior Data Scientist, you'll lead the creation of groundbreaking datasets that power our AI/ML efforts within and across our scientific grand challenges. Working at the intersection of data science, biology, and AI, your work will focus on creating large, AI-ready datasets, spanning single-cell sequencing, immune receptor profiling, and mass spectrometry peptidomics data. You will define data needs, format standards, analysis approaches and quality metrics and build pipelines to ingest, transform, and validate data products that form the foundation of our experiments.

Our Data Ecosystem:

These efforts will form a part of, and interoperate with our larger larger data ecosystem. We are generating unprecedented scientific datasets that drive biological innovation:

Billions of standardized cells of single-cell transcriptomic data, with a focus on measuring genetic and environmental perturbations 10s of thousands of donor-matched DNA & RNA samples 10s PBs-scale static and dynamic imaging datasets 100s TBs-scale mass spectrometry datasets Diverse, large multi-modal biological datasets that enable biological bridges across measurement types and facilitate multi-modal model training to define how cells act. When analysis of a dataset is complete, you will help publish it through public resources like CELLxGENE Discover, the CryoET Portal, and the Virtual Cell Platform, used by tens of thousands of scientists monthly to advance understanding of genetic variants, disease risk, drug toxicities, and therapeutic discovery.

You'll collaborate with cross-functional teams to lead dataset definition, ingestion, transformation, and delivery for AI modeling and experimental analysis. Success means delivering high-quality, usable datasets that directly address modeling challenges and accelerate scientific progress. Join us in building the data foundation that will transform our understanding of human biology and move us along the path to curing, preventing, and managing all disease.

We are Bagel Labs - a distributed machine learning research lab working toward open-source superintelligence.

Role Overview

You will design and optimize distributed diffusion model training and serving systems.
Your mission is to build scalable, fault-tolerant infrastructure that serves open-source diffusion models across multiple nodes and regions with efficient adaptation support.

Key Responsibilities

• Design and implement distributed diffusion inference systems for image, video, and multimodal generation.
• Architect high-availability clusters with failover, load balancing, and dynamic batching for variable resolutions.
• Build monitoring and observability systems for denoising steps, memory usage, generation latency, and CLIP score tracking.
• Integrate with open-source frameworks such as Diffusers, ComfyUI, and Invoke AI.
• Implement and optimize rectified flow, consistency distillation, and progressive distillation.
• Design distributed systems for ControlNet, IP-Adapter, and multimodal conditioning at scale.
• Build infrastructure for LoRA/LyCORIS adaptation with hot-swapping and memory-efficient merging.
• Optimize VAE decoding pipelines and implement tiled/windowed generation for ultra-high-resolution outputs.
• Document architectural decisions, review code, and publish technical deep-dives on blog.bagel.com.

Who You Might Be

You understand distributed systems and diffusion architectures deeply.
You’re excited about the evolution from DDPM to flow matching to consistency models, and you enjoy building infrastructure that handles complex, variable compute workloads.

Required Skills

1. 5+ years in distributed systems or production ML serving.
2. Hands-on experience with Diffusers, ComfyUI, or similar frameworks in production.
3. Deep understanding of diffusion architectures (U-Net, DiT, rectified flows, consistency models).
4. Experience with distributed GPU orchestration for high-memory workloads.
5. Proven record of optimizing generation latency (CFG, DDIM/DPM solvers, distillation).
6. Familiarity with attention optimization (Flash Attention, xFormers, memory-efficient attention).
7. Strong grasp of adaptation techniques (LoRA, LyCORIS, textual inversion, DreamBooth).
8. Skilled in variable-resolution generation and dynamic batching strategies.

Bonus Skills

• Contributions to open-source diffusion frameworks or research.
• Experience with video diffusion models and temporal consistency optimization.
• Knowledge of quantization techniques (INT8, mixed precision) for diffusion models.
• Experience with SDXL, Stable Cascade, Würstchen, or latent consistency models.
• Distributed training using EDM, v-prediction, or zero-terminal SNR.
• Familiarity with CLIP guidance, perceptual loss, and aesthetic scoring.
• Experience with real-time diffusion inference (consistency or adversarial distillation).
• Published work or talks on diffusion inference optimization.

What We Offer

• Top-of-market compensation
• A deeply technical culture where bold ideas are built, not just discussed
• Remote flexibility within North American time zones
• Ownership of work shaping decentralized AI
• Paid travel to leading ML conferences worldwide

Apply now - help us build the infrastructure for open-source superintelligence.

Rochester, Minnesota, USA

Mayo Clinic seeks a highly motivated individual to advance the development, validation, and real-world implementation of generative AI systems for clinical decision support in Gastroenterology and Hepatology. This role bridges research and translation into clinical workflows, focusing on building trustworthy AI systems that augment human presence and put the needs of the patient first. Research Fellows will work within a multidisciplinary team of data scientists, physicians, and engineers to design novel generative agentic architectures, develop useful benchmarks, and work together with clinical teams to decrease time to diagnosis and time to treatment. Contact shung.dennis@mayo.edu if interested.

London

Flow Traders is looking for a Senior Research Engineer to join our Hong Kong office. This is a unique opportunity to join a leading proprietary trading firm with an entrepreneurial and innovative culture at the heart of its business. We value quick-witted, creative minds and challenge them to make full use of their capacities.

As a Senior Research Engineer, you will be responsible for helping to lead the development of our trading model research framework and using it to conduct research to develop models for trading in production. You'll expand the framework to become global standard way of training, consuming, combining, and transforming any data source in a data-driven systematic way. You will then partner with Quantitative Researchers to build the trading models themselves.

What You Will Do

• Help to lead the development and global rollout of our research framework for defining and training models through various optimization procedures (supervised learning, backtesting etc.), as well as its integration with our platform for deploying and running those models in production
• Partner with Quantitative Researchers to conduct research: test hypotheses and tune/develop data-driven systematic trading strategies and alpha signals

What You Need to Succeed

• Advanced degree (Master's or PhD) in Machine Learning, Statistics, Physics, Computer Science or similar
• 8+ years of hands-on experience MLOps, Research Engineering, or ML Research
• A strong background in mathematics and statistics
• Strong proficiency in programming languages such as Python, with experience in libraries like numpy, pytorch, polars, pandas, and ray.
• Demonstrated experience in designing and implementing end-to-end machine learning pipelines, including data preprocessing, model training, deployment, and monitoring
• Understanding of and experience with modern software development practices and tools (e.g. Agile, version control, automated testing, CI/CD, observability)
• Understanding of cloud platforms (e. g., AWS, Azure, GCP) and containerization technologies (e. g., Docker, Kubernetes)

About AIMATX

AIMATX is a Berkeley-based startup revolutionizing materials science by creating next-generation materials and molecules that power the future economy. Our AI-driven platform explores vast chemical spaces, predicts new materials and their properties, and accelerates discovery through intelligent, targeted experimentation. By reducing years of R&D to weeks, we are shaping the future of materials innovation;come join us!

AIMATX is built and guided by a world-class team at the intersection of science, AI and engineering. Our leadership includes Omar Yaghi (2025 Nobel Prize), Fernando Perez inventor of Jupyter/IPython, alongside former CEOs of public companies and leading researchers in generative AI and autonomous experimentation. This ecosystem brings unmatched scientific depth, computational expertise, and entrepreneurial excellence to accelerate the future of discovery.

Role Overview

We are seeking a highly skilled Computational Chemist / Materials Scientist to join our innovative team. You will apply your expertise in chemical and materials science R&D to develop sustainable, high-performance materials tailored to specific use cases. As part of our technical team, you will:

• Develop and apply AI-computational tools to predict novel material structures and properties.
• Design and implement machine learning algorithms to analyze large datasets and predict material behavior.
• Build AI-based methods for synthesis prediction of candidate materials.
• Collaborate with engineering teams to translate computational predictions into high-throughput experimental workflows.
• Incorporate experimental feedback into predictive models to improve accuracy within a closed-loop, self-improving platform.
• Analyze and visualize theoretical and experimental data, presenting insights to stakeholders and guiding research and product strategy.
• Work with data science experts to quantify and calibrate uncertainties across the predictive pipeline.
• Stay current with scientific advances and integrate relevant ideas into ongoing projects.
• Implement computational methods in a rigorously tested codebase deployed using modern software engineering best practices.

Required Qualifications

• PhD in Machine Learning, Computational Chemistry, Chemistry, Materials Science, Physics or a related field.
• Experience applying machine learning to scientific or structured data
• Proficiency with Python, GitHub workflows, testing, documentation, and continuous integration.
• Demonstrated leadership and project ownership in computational or ML-driven research.

Preferred Qualifications

• Experience developing modeling approaches, including physics-based atomistic modeling.
• Experience in polymer chemistry, ceramics, nanomaterials, or related areas.
• Publication record in peer-reviewed journals and presentations at scientific conferences.

Soft Skills & Cultural Fit

• Excellent written and verbal communication skills.
• Collaborative mindset and ability to work effectively in a multidisciplinary team.
• Strong organization, attention to detail, and a results-driven attitude.
• Proactive and self-motivated, with the ability to take initiative.
• Commitment to scientific rigor, innovation, and continuous learning.

Benefits & Perks

We offer a competitive salary with bonus potential and meaningful early equity. Compensation reflects experience, expertise and expected impact.

Additional benefits may include: - Flexible work arrangements and remote options. - Medical, dental, and vision coverage. - 401(k) with company matching. - Generous PTO and parental leave.

Equal Opportunity Statement

AIMATX is committed to fostering a diverse and inclusive workplace. All qualified applicants will receive consideration without regard to race, color, religion, gender, sexual orientation, gender identity, national origin, disability, or vet

Send your CV to theo.jaf@aimatx.ai

San Francisco

We are seeking a talented software engineer with generative AI experience who is deeply proficient with python and typescript to join our dynamic and growing team at Writer. As a key member of our engineering team, you will play a crucial role in building the genAI software. Your primary focus will be on developing a state-of-the-art platform that harnesses generative AI technologies and you will deliver seamless and scalable solutions. You will work closely with cross-functional teams to design, implement, and maintain features that enhance the user experience, drive product growth, establish best practices, and integrate cutting-edge AI capabilities.

Your responsibilities

• Design and develop robust and scalable generative AI services using Python and open source frameworks such as Writer Agent Builder, LangChain, and n8n.
• Implement responsive and user-friendly frontend interfaces, leveraging technologies like React, TypeScript, and modern web frameworks.
• Work with cloud platforms such as AWS, GCP, or Azure to deploy and scale applications.
• Develop and integrate high-performance, low-latency APIs for AI-driven features.
• Ensure code quality through testing, peer reviews, and continuous integration.
• Collaborate with the team to build, and maintain generative AI agents.
• Participate in architectural design discussions and promote engineering best practices.
• Continuously improve the application’s performance, scalability, and maintainability.

Is This You?

• 5+ years of experience in software engineering at expert level with Python
• Experience building web applications using FastAPI and Asyncio
• Experience building with generative AI applications in production environments.
• Expertise with microservices architecture and RESTful APIs.
• Solid understanding of database technologies such as PostgreSQL and vector databases as Elastic, Pinecone, Weaviate, or similar.
• Familiarity with cloud platforms (AWS, GCP, etc.) and containerized environments (Docker, Kubernetes).
• Familiarity with MCP, devtools, AI agents, or contributed to open source
• You are committed to writing clean, maintainable, and scalable code, following best practices in software development.
• You enjoy solving complex problems and continuously improving the performance and scalability of systems.
• You thrive in collaborative environments, working closely with cross-functional teams to build impactful features.
• Proven ability to help teams adopt technical best practices.

The role We are seeking a highly skilled and customer-focused professional to join our team as a Cloud Solutions Architect specializing in Cloud infrastructure and MLOps. As a Cloud Solutions Architect, you will play a pivotal role in designing and implementing cutting-edge solutions for our clients, leveraging cloud technologies for ML/AI teams and becoming a trusted technical advisor for building their pipelines.

You’re welcome to work remotely from the US or Canada.

Your responsibilities will include: - Act as a trusted advisor to our clients, providing technical expertise and guidance throughout the engagement. Conduct PoC, workshops, presentations, and training sessions to educate clients on GPU cloud technologies and best practices. - Collaborate with clients to understand their business requirements and develop solution architecture that align with their needs: design and document Infrastructure as code solutions, documentation and technical how-tos in collaboration with support engineers and technical writers. - Help customers to optimize pipeline performance and scalability to ensure efficient utilization of cloud resources and services powered by Nebius AI. - Act as a single point of expertise of customer scenarios for product, technical support, marketing teams. - Assist to Marketing department efforts during events (Hackathons, conferences, workshops, webinars, etc.)

We expect you to have: - 5 - 10 + years of experience as a cloud solutions architect, system/network engineer, developer or a similar technical role with a focus on cloud computing - Strong hands-on experience with IaC and configuration management tools (preferably Terraform/Ansible), Kubernetes, skills of writing code in Python - Solid understanding of GPU computing practices for ML training and inference workloads, GPU software stack components, including drivers, libraries (e.g. CUDA, OpenCL) - Excellent communication skills - Customer-centric mindset

It will be an added bonus if you have: - Hands-on experience with HPC/ML orchestration frameworks (e.g. Slurm, Kubeflow) - Hands-on experience with deep learning frameworks (e.g. TensorFlow, PyTorch) - Solid understanding of cloud ML tools landscape from industry leaders (NVIDIA, AWS, Azure, Google)

Location United States

Description At Oracle Cloud Infrastructure (OCI), we are building the future of cloud computing—designed for enterprises, engineered for performance, and optimized for AI at scale. We are a fast-paced, mission-driven team within one of the world’s largest cloud platforms. The Multimodal AI team in OCI Applied Science is working on developing cutting-edge AI solutions using Oracle's industry leading GPU-based AI clusters to disrupt industry verticals and push the state-of-the-art in Multimodal and Video GenAI research. You will work with a team of world-class scientists in exploring new frontiers of Generative AI and collaborate with cross-functional teams including software engineers and product managers to deploy these globally for real-world enterprise use-cases at the largest scale.

Responsibilities: - Contribute to the development and optimization of distributed multi-node training infrastructure - Stay Updated: Maintain a deep understanding of industry trends and advancements in video generatio, multimodal understanding, pretraining workflows and paradigms. -Model Development: Design, develop, and train state-of-the-art image and vide generation models that meet the highest quality standards. - Collaborate with cross-functional teams to support scalable and secure deployment pipelines. - Assist in diagnosing and resolving production issues, improving observability and reliability. - Write maintainable, well-tested code and contribute to documentation and design discussions

Minimum Qualifications - BS in Computer Science or related technical field. - 6+ years of experience in backend software development with cloud infrastructure. - Strong proficiency in at least one language such as Go, Java, Python, or C++. - Experience building and maintaining distributed services in a production environment. - Familiarity with Kubernetes, container orchestration, and CI/CD practices. - Solid understanding of computer science fundamentals such as algorithms, operating systems, and networking.

Preferred Qualifications - MS in Computer Science. - Experience in large-scale multi-node distributed training of LLMs and multimodal models. - Knowledge of cloud-native observability tools and scalable service design. - Interest in compiler or systems-level software design is a plus.

Why Join Us - Build mission-critical AI infrastructure with real-world impact. - Work closely with a collaborative and experienced global team. - Expand your knowledge in AI, cloud computing, and distributed systems. - Contribute to one of Oracle’s most innovative and fast-growing initiatives.

Disclaimer:

Certain US customer or client-facing roles may be required to comply with applicable requirements, such as immunization and occupational health mandates.

Range and benefit information provided in this posting are specific to the stated locations only

US: Hiring Range in USD from: $96,800 to $223,400 per annum. May be eligible for bonus and equity.

Oracle maintains broad salary ranges for its roles in order to account for variations in knowledge, skills, experience, market conditions and locations, as well as reflect Oracle’s differing products, industries and lines of business. Candidates are typically placed into the range based on the preceding factors as well as internal peer equity.

Oracle US offers a comprehensive benefits package which includes the following: 1. Medical, dental, and vision insurance, including expert medical opinion 2. Short term disability and long term disability 3. Life insurance and AD&D 4. Supplemental life insurance (Employee/Spouse/Child) 5. Health care and dependent care Flexible Spending Accounts 6. Pre-tax commuter and parking benefits 7. 401(k) Savings and Investment Plan with company match 8. Paid time off: Flexible Vacation is provided to all eligible employees assigned to a salaried (non-overtime eligible) position. Accrued Vacation is provided to all other employees

Noumenal Labs | Remote-friendly | Full-time

Noumenal's Thermodynamic Computing Lab is building the foundations of physical AI at the intersection of robotics and novel hardware, As a Research Engineer, you will help to define, design, and deploy the hybrid computing stack powering a paradigm shift in which stochastic thermodynamic dynamics become the substrate of intelligence itself. The goal: robots that learn from tens of demonstrations instead of thousands and run an order of magnitude longer on the same battery.

What You’ll Do

~ Architect hybrid software–hardware systems that implement probabilistic frameworks using energy-based algorithms on thermodynamic chips. ~ Build sampling-based inference systems (e.g., MCMC, Gibbs sampling, variational inference) optimized for thermodynamic computing substrates. ~ Co-design algorithms jointly with hardware teams to map computation efficiently onto novel physical architectures. ~ Deploy, evaluate, and iterate on these systems in real robotic environments. ~ Collaborate closely with physicists, AI researchers, hardware engineers, and product teams to drive real-time adaptive computation. ~ Contribute to publications, patents, and open-source frameworks advancing the field of physical AI and intelligent thermodynamic systems.

Required Skills

~ Strong coding ability in Python and at least one ML framework (PyTorch, JAX, or TensorFlow). ~ Experience with probabilistic inference (MCMC, variational inference, or energy-based models). ~ Solid understanding of machine learning fundamentals — especially deep learning, Bayesian, and Maximum Entropy Inverse RL. ~ Enthusiasm about both non-traditional hardware (e.g., neuromorphic, analog, quantum, thermodynamic) and how algorithms map to computation beyond GPUs. ~ Interest in developing within the active inference framework. ~ A systems mindset focused on performance, energy efficiency, and robustness.

Ideal Background

~ Experience with diffusion/score-based models, or generative world models. ~ Interest in control as inference. ~ Robotics experience (simulators or physical robots).

What We Offer

~ Early access to thermodynamic computing hardware. ~ Collaboration with leading researchers in active inference, generative modeling, and novel computing. ~ Real robotic platforms for prototyping and deployment. ~ Remote-friendly culture with periodic on-site collaboration. ~ Strong support for research, publication, and open-source contributions. ~ Salary $100,000 to 150,000 USD + equity.