New York

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)

The Chan Zuckerberg Biohub Network (https://www.czbiohub.org/) is a group of nonprofit research institutes that bring together scientists, engineers, and physicians with the goal of pursuing grand scientific challenges on 10- to 15-year time horizons. The CZ Biohub Network focuses on understanding underlying mechanisms of disease and developing new technologies that will lead to actionable diagnostics and effective therapies.

We pursue large scientific challenges that cannot be pursued in conventional environments We enable individual investigators to pursue their riskiest and most innovative ideas The technologies developed at the CZ Biohub Network facilitate research by scientists and clinicians at our home institutions and beyond Diversity of thought, ideas, and perspectives are at the heart of CZ Biohub Network and enable disruptive innovation and scholarly excellence. We are committed to cultivating an organization where all colleagues feel inspired and know their work makes an important contribution.

The Biohub Network is seeking an accomplished computational biologist and machine learning/AI specialist to join our interdisciplinary team. This role requires experience in research settings, a background in biology, and a proven ability to design, evaluate, and publish innovative computational methodologies that leverage machine learning, statistics, language modelling, and AI to advance biological research and discovery. Research projects to accelerate the rate of scientific discovery will be assigned by the President of the New York location, and in collaboration with research teams across the organization.

The ideal candidate will have a strong track record of accomplishments and a dedication to collaborative work within a highly interdisciplinary environment.

This role is based out of the New York location.

What You'll Do - Contribute to a dynamic, innovative, and collaborative program that aligns with the mission of CZ Biohub NY. - Develop and evaluate cutting-edge computational / AI methodologies using data generated from across all research groups and incorporating relevant available datasets for to develop predictive models. - Collaborate within an interdisciplinary research environment to develop, test, and validate models. - Engage with colleagues throughout the Biohub to uphold our values of scholarly excellence, innovation, open communication, hands-on hacking, and partnership. - Communicate progress and results with colleagues inside and outside of your team. - Publish and disseminate impactful findings through preprints (medRxiv, bioRxiv) and/or software repositories (e.g., GitHub). - Work with the CZ Biohub team to patent and license technologies resulting from your research.

What You'll Bring - PhD in Computational Biology, AI / Machine learning, Applied Statistics or a MS plus relevant job experience. - Background in relevant areas of biomedical science, demonstrating a deep understanding of cellular biology, transcription and protein signal transduction. - 2-4 years of post-doctoral and/or industry experience demonstrating the ability to implement, evaluate, and create new computational methodologies that leverage machine learning, statistics, and AI for biological research and discovery. - Experience in building and evaluating machine learning and/or neural network models on biological data, with a deep understanding of feature selection, regularization, model introspection, and interpretability. - Proficiency in using and modifying probabilistic learning or deep learning models such as RNNs, GNNs, protein sequence models, or natural language processing models. - Proven track record of individual innovation, as well as a strong ability to work collaboratively. - Outstanding interpersonal and communication skills. - Demonstrated commitment to open science and alignment with the mission and values of CZ Biohub.

ABOUT POOLSIDE

In this decade, the world will create Artificial General Intelligence. There will only be a small number of companies who will achieve this. Their ability to stack advantages and pull ahead will define the winners. These companies will move faster than anyone else. They will attract the world's most capable talent. They will be on the forefront of applied research, engineering, infrastructure and deployment at scale. They will continue to scale their training to larger & more capable models. They will be given the right to raise large amounts of capital along their journey to enable this. They will create powerful economic engines. They will obsess over the success of their users and customers.

poolside exists to be this company - to build a world where AI will be the engine behind economically valuable work and scientific progress.

ABOUT OUR TEAM

We are a remote-first team that sits across Europe and North America and comes together once a month in-person for 3 days and for longer offsites twice a year.

Our R&D and production teams are a combination of more research and more engineering-oriented profiles, however, everyone deeply cares about the quality of the systems we build and has a strong underlying knowledge of software development. We believe that good engineering leads to faster development iterations, which allows us to compound our efforts.

ABOUT THE ROLE

You would be working on our pre-training team focused on building out our distributed training of Large Language Models and major architecture changes. This is a hands-on role where you'll be both programming and implementing LLM architectures (dense & sparse) and distributed training code all the way from data to tensor parallelism, while researching potential optimizations (from basic operations to communication) and new architectures & distributed training strategies. You will have access to thousands of GPUs in this team.

YOUR MISSION

To train the best foundational models for source code generation in the world in minimum time and with maximum hardware utilization.

RESPONSIBILITIES

• Follow the latest research on LLMs and source code generation. Propose and evaluate innovations, both in the quality and the efficiency of the training
• Do LLM-Ops: babysitting and analyzing the experiments, iterating
• Write high-quality Python, Cython, C/C++, Triton, CUDA code
• Work in the team: plan future steps, discuss, and always stay in touch

SKILLS & EXPERIENCE

• Experience with Large Language Models (LLM)
• Deep knowledge of Transformers is a must
• Knowledge/Experience with cutting-edge training tricks
• Knowledge/Experience of distributed training
• Trained LLMs from scratch
• Coded LLMs from scratch
• Knowledge of deep learning fundamentals
• Strong machine learning and engineering background
• Research experience
• Author of scientific papers on any of the topics: applied deep learning, LLMs, source code generation, etc. - is a nice to have
• Can freely discuss the latest papers and descend to fine details
• Is reasonably opinionated
• Programming experience: Linux, Strong algorithmic skills, Python with PyTorch or Jax, C/C++, CUDA, Triton
• Use modern tools and are always looking to improve
• Strong critical thinking and ability to question code quality policies when applicable
• Prior experience in non-ML programming, especially not in Python - is a nice to have

PROCESS

• Intro call with one of our Founding Engineers
• Technical Interview(s) with one of our Founding Engineers
• Team fit call with the People team
• Final interview with Eiso, our CTO & Co-Founder

BENEFITS

• Fully remote work & flexible hours
• 37 days/year of vacation & holidays
• Health insurance allowance for you and dependents
• Company-provided equipment
• Wellbeing, always-be-learning and home office allowances
• Frequent team get togethers
• Great diverse & inclusive people-first culture

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.

Who we are:

Peripheral is developing spatial intelligence, starting in live sports and entertainment. Our models generate interactive, photorealistic 3D reconstructions of sporting events, building the future of live media. We’re solving key research challenges in 3D computer vision, creating the foundations for the next generation of robotic perception and embodied intelligence.

We’re backed by Tier-1 investors and working with some of the biggest names in sports. Our team includes top robotics and machine learning researchers from the University of Toronto, advised by Dr. Steven Waslander and Dr. Igor Gilitshenski.

Our team is ambitious and looking to win. We’re seeking a machine learning engineer to push the latency and quality of our 3D reconstruction system.

What you’ll be doing:

• Development of our 3D reconstruction method to improve novel view reconstruction quality,

• Improving our models for prior generation, such as depth and surface estimation, keypoint matching, and segmentation.

What we’d want to see:

• Strong understanding of 3D computer vision,

• Past research experience in neural rendering (Gaussian Splatting, NERFs),

• Previous industry experience training and deploying ML models,

Ways to stand out from the crowd:

• Previous research experience with feedforward, temporal, multi-image models.

• Previous experience fine-tuning foundational models such as DINO, Map Anything, etc,

• Top publications at conferences like NeurIPS, ICLR, ICML, CVPR, WACV, CoRL, ICRA,

• Experience leading high-performance teams,

Why join us:

• Competitive equity as an early team member.

• $80-150K CAD + bonuses, flexible based on experience.

• Exclusive access to the world’s biggest sporting events and venues,

• Work on impactful projects, developing the future of 3D media and spatial intelligence.

To explore additional roles, please visit: www.peripheral.so

Location: Toronto, ON, Canada

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.

San Jose, CA, USA

We are looking for a hands-on, systems-oriented AI Agent Engineer to design, build, and maintain intelligent agents that drive automation and business impact across the enterprise. This role is responsible for the full lifecycle of agent development — from design to versioning, orchestration, and continuous learning.

You’ll contribute directly to scaling our AI strategy by engineering reusable components, optimizing agent workflows, and ensuring real-world performance in production environments.

What you'll Do

• Agent Development: Build and fine-tune specialized AI agents for targeted customer experience use cases such as discovery, support, and lead qualification. Implement prompt engineering strategies, memory handling, resource management and tool-calling integrations

• Multi-Agent Communication: Adopt agent-to-agent communication protocols and handoff mechanisms to enable cooperative task execution and delegation. Build orchestrated workflows across agents using frameworks like LangChain, AutoGen, or Semantic Kernel

• Templates & Reusability: Create reusable agent templates and modular components to accelerate deployment across business units. Build plug-and-play configurations for domain-specific requirements.

• Lifecycle Management & Monitoring: Track and improve conversation quality, task success rate, user satisfaction, and performance metrics. Automate monitoring of agent behavior using observability tools (e.g., Arize, LangSmith, custom dashboards)

• Continuous Improvement: Implement learning workflows, including human-in-the-loop feedback and automatic retraining. Refine prompts and model behavior through structured experimentation and feedback loops.

• Maintenance & Governance: Handle knowledge base updates, drift detection, performance degradation, and integration of new business logic. Ensure agents stay aligned with evolving enterprise data sources and compliance requirements

• Deployment: Manage agent versioning, testing pipelines (unit, regression, UX), and controlled rollout processes. Collaborate with DevOps, QA, and infrastructure teams to ensure scalable deployments

What you need to succeed - 3–5+ years of experience in AI/ML engineering, NLP systems, or backend development - Strong proficiency with LLM frameworks (e.g., OpenAI APIs, LangChain, RAG pipelines) - Experience building conversational agents or workflow bots in production environments - Familiarity with cloud platforms (AWS/GCP/Azure), REST APIs, Python, and containerization (Docker, K8s) - Comfort with prompt design, vector databases, and memory handling strategies

Preferred Qualifications - Experience with multi-agent frameworks or agent orchestration systems - Familiarity with observability tools, data labeling workflows, or synthetic data generation - Background in conversational design or dialogue management systems - Degree in Computer Science, Data Science, Engineering, or a related field

Toronto

Description - Bloomberg’s Engineering AI department comprises over 350 AI experts dedicated to building cutting edge, market-leading products. Leveraging advanced technologies including transformers, large language models, and dense vector databases, we are transforming search, discovery, and workflow solutions across the financial industry. As we expand our group, we are seeking highly skilled Machine Learning (ML) and Software Engineers who will contribute innovative solutions to AI-driven customer-facing products.

At Bloomberg, we foster transparency and efficiency in global financial markets. Our technology powers search and discoverability, bringing actionable insights from news, research, financial data, and analytics covering more than 35 million financial instruments. Since 2009, Bloomberg has been at the forefront of applying artificial intelligence to organize the vast volumes of structured and unstructured data that inform critical financial decisions, uncover market signals, and deliver clarity precisely when our clients need it most.

In Toronto, our Machine Learning Engineers are central to advancing Bloomberg’s efforts in financial query understanding and code generation. They bridge the gap between pioneering research and practical solutions, developing models to address complex financial queries and automate code writing. They engineer state-of-the-art code generation systems and apply LLM techniques like CoT, SFT or RLHF to drive iterative model refinement.

Join the AI Group as a Senior ML Research Engineer and you will have the opportunity to: -Collaborate with colleagues on production systems and write, test, and maintain production quality code -Design, train, experiment, and evaluate ML models, algorithms and solutions -Demonstrate technical leadership by owning cross-team projects -Stay current with the latest research in ML and incorporate new findings into our models and methodologies -Represent Bloomberg at scientific and industry conference and in open-source communities -Publish product and research findings in documentation, whitepapers or publications to leading academic venues

We are looking for Senior ML Research Engineers with the following experience: -Practical experience with solving Machine Learning problems and techniques -Ph.D. in ML, Statistics or a relevant field or MSc in CS, ML, Math, Statistics, Engineering, or related fields and 2+ years of relevant work experience -Experience with machine learning and deep learning frameworks -Proficiency in software engineering -An understanding of Computer Science fundamentals such as data structures and algorithms and a data oriented approach to problem-solving -Excellent communication skills and the ability to collaborate with engineering peers as well as non-engineering stakeholders. -A track record of authoring publications in top conferences and journals is a strong plus