ABOUT THE ROLE

You would be working as part of our Applied Research team, focused on turning pre-trained LLMs into well-aligned and highly capable AI systems for coding and software development. This is a hands-on role where you'll work across a variety of efforts, including: Building data pipelines for coding use cases, researching and implementing fine-tuning algorithms, training reward models, and more. You will have access to thousands of GPUs in this team.

YOUR MISSION

To turn pre-trained LLMs into well-aligned and highly capable AI systems.

RESPONSIBILITIES

• Research and experiment on ways to specialize foundational models to coding use cases
• Build and maintain data and training pipelines
• Keep up with latest research, and be familiar with state of the art in LLMs, alignment, synthetic data generation, code generation
• Design, analyze, and iterate on training/fine-tuning/data generation experiments
• Write high-quality, pragmatic code
• Work as part of a team: plan future steps, discuss, and communicate clearly with your peers

SKILLS & EXPERIENCE

• Experience with Large Language Models (LLM)
• Deep knowledge of Transformers
• Strong deep learning fundamentals
• Good taste in data
• Fine-tuning experience with LLMs
• Extensively used and probed LLMs, familiarity of their capabilities and limitations
• Knowledge of distributed training
• Strong machine learning and engineering background
• Research experience
• Experience in proposing and evaluating novel research ideas
• Familiar with, or contributed to the state of the art in multiple of the following topics: Fine-tuning and alignment of LLMs, synthetic data generation, continual learning, RLHF, code generation
• Is comfortable in a rapidly iterating environment
• Is reasonably opinionated
• Programming experience: Linux, Strong algorithmic skills, Python with PyTorch or Jax. 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 one of our Founding Engineers

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

AI Scientist

The Role

This AI Scientist position will drive the development and optimization of Aizen's generative AI-based peptide drug discovery platform, DaX™. You will be responsible for incorporating state-of-the-art neural network architectures and high-performance computational biology software to improve the accuracy and throughput of our drug discovery efforts. Your work will be critical in translating experimental data and scientific insights into scalable, robust models.

Our Ideal Candidate

You are passionate about the company’s mission and a self-starter with an inextinguishable fire to compete and succeed. You thrive in an environment that requires crisp judgment, pragmatic decision-making, rapid course-corrections, and comfort with market ambiguity. You discharge your duties within a culture of mutual team respect, high performance, humility, and humor.

Key Responsibilities

• Incorporate state-of-the-art neural network architectures and training methods to improve accuracy and throughput of DaX™, Aizen's generative AI-based peptide drug discovery platform.
• Develop, test, deploy, and maintain high-performance computational biology software according to the needs and feedback of experimentalists at Aizen.
• Orchestrate new and existing Aizen software tools into scalable, highly-available, and easy-to-use cloud pipelines.
• Work closely with experimental scientists at Aizen to manage storage and access of Aizen's experimental data.

Candidate Skills and Experience

• Ph.D. and/or postdoctoral studies in Computer Science, Computational Biology, Bioinformatics, or a related field.
• Deep, demonstrated expertise in advanced Generative Models (e.g., Flow Matching, Diffusion Models) for de novo design in discrete and continuous spaces.
• Experience integrating and leveraging data from physics-based simulations (e.g., Molecular Dynamics) into machine learning models.
• Experience collecting, sanitizing, and training on biological property datasets, with a preference for prior experience with peptides.
• Proficiency with Python, shell scripting, and a high-performance compiled language.
• Entrepreneurial spirit, self-starter with proper balance of scientific creativity and disciplined execution.
• Preferred: Experience designing and maintaining high-availability cloud architectures for hosting high-performance biological analysis software.
• Preferred: Experience in chemical featurization, representation, and model application for peptide chemistry, non-canonical amino acids (NCAAs), and complex peptide macrocycles.
• Preferred: Experience in protein/peptide folding dynamics, protein structural analysis, and resultant data integration to improve computation/design.

About Aizen

Aizen is an AI-driven biotechnology company pioneering Mirror Peptides, a novel class of biologic medicines. Mirror Peptides are synthetic, fully D-amino acid peptides that represent a vast, unexplored therapeutic chemical space. Backed by life science venture capital and based in the biotech hub of San Diego, CA.

Location & Compensation

• Reporting: Principal AI Scientist
• Location: This position offers fully remote work with monthly/quarterly trips to company facilities in California.
• Compensation: Competitive base salary, stock options, and a benefits package including medical coverage.

Contact

To apply, please contact us at jobs@aizentx.com.

An equal opportunity employer V1

AI Platform Engineer

Location: Boston (US) / Barcelona (Spain)

Position Overview

As an AI Platform Engineer, you are the bridge between AI research and production software. You will:

• Build and maintain AI infrastructure: model serving, vector databases, embedding pipelines
• Enable AI developers to deploy their work reproducibly and safely
• Design APIs for AI inference, prompt management, and evaluation
• Implement MLOps pipelines: versioning, monitoring, logging, experimentation tracking
• Optimize performance: latency, cost, throughput, reliability
• Collaborate with backend engineers to integrate AI capabilities into the product

Key Responsibilities

AI Infrastructure

• Deploy and serve LLMs (OpenAI, Anthropic, HuggingFace, fine-tuned models)
• Optimize inference latency and costs
• Implement caching, rate limiting, and retry strategies

MLOps & Pipelines

• Version models, prompts, datasets, and evaluation results
• Implement experiment tracking (Weights & Biases)
• Build CI/CD pipelines for model deployment
• Monitor model performance and drift
• Set up logging and observability for AI services

API Development

• Design and implement APIs (FastAPI)
• Create endpoints for prompt testing, model selection, and evaluation
• Integrate AI services with backend application
• Ensure API reliability, security, and performance

Collaboration & Enablement

• Work with AI Developers to productionize their experiments regarding improving user workflows
• Define workflows: notebook/test repository → PR → staging → production
• Document AI infrastructure and best practices
• Review code and mentor AI developers on software practices

Required Skills & Experience

Must-Have

• 7+ years of software engineering experience (Python preferred)
• Experience with LLMs and AI/ML in production: OpenAI API, HuggingFace, LangChain, or similar
• Understanding of vector databases (Pinecone, Chroma, Weaviate, FAISS)
• Cloud infrastructure experience: GCP (Vertex AI preferred) or AWS (SageMaker)
• API development: FastAPI, REST, async programming
• CI/CD and DevOps: Docker, Terraform, GitHub Actions
• Monitoring and observability
• Problem-solving mindset: comfortable debugging complex distributed systems
• Operating experience with AI deployment in enterprise environment

Nice-to-Have

• Experience fine-tuning or training models
• Familiarity with LangChain, Pydantic AI or similar frameworks
• Knowledge of prompt engineering and evaluation techniques
• Experience with real-time inference and streaming responses
• Background in data engineering or ML engineering
• Understanding of RAG architectures
• Contributions to open-source AI/ML projects

Tech Stack

Current Stack:

• Languages: Python (primary), Bash
• AI/ML: OpenAI API, Anthropic, HuggingFace, LangChain, Pydantic AI
• Vector DBs: Pinecone, Chroma, Weaviate, or FAISS
• Backend: FastAPI, SQLAlchemy, Pydantic
• Cloud: GCP (Vertex AI, Cloud Run), Terraform
• CI/CD: GitHub Actions
• Experiment Tracking: MLflow, Weights & Biases, or custom
• Containers: Docker, Kubernetes (optional)

What we offer:

Competitive compensation

Stock Options Plan: Empowering you to share in our success and growth.
Cutting-Edge Tools: Access to state-of-the-art tools and collaborative opportunities with leading experts in artificial intelligence, physics, hardware and electronic design automation.
Work-Life Balance: Flexible work arrangements in one of our offices with potential options for remote work.
Professional Growth: Opportunities to attend industry conferences, present research findings, and engage with the global AI research community.
Impact-Driven Culture: Join a passionate team focused on solving some of the most challenging problems at the intersection of AI and hardware.

As a Machine Learning Researcher at IMC, your work will directly impact our global trading strategies. You will leverage your superior analytical, mathematical, and computing skills to improve existing models and develop new ones. We will empower you to discover your unique niche and excel, taking on responsibility and ownership from the start. Machine Learning Researchers work closely with Traders and Developers in an environment where problem solving, innovation and teamwork are recognized and rewarded.

Toronto or remote

Mission: We are seeking a highly skilled Machine Learning Engineer to join our advanced model development team. This role focuses on pre-training, continued training, and post-training of models, with a particular emphasis on draft model optimization for speculative decoding and quantization-aware training (QAT). The ideal candidate has deep experience with training methodologies, open-weight models, and performance-tuning for inference.

Responsibilities & opportunities in this role: Lead pre-training and post-training efforts for draft models tailored to speculative decoding architectures. Conduct continued training and post-training of open-weight models for non-draft (standard) inference scenarios. Implement and optimize quantization-aware training pipelines to enable low-precision inference with minimal accuracy loss. Collaborate with model architecture, inference, and systems teams to evaluate model readiness across training and deployment stages. Develop tooling and evaluation metrics for training effectiveness, draft model fidelity, and speculative hit-rate optimization. Contribute to experimental designs for novel training regimes and speculative decoding strategies.

Ideal candidates have/are: 5+ years of experience in machine learning, with a strong focus on model training. Proven experience with transformer-based architectures (e.g., LLaMA, Mistral, Gemma). Deep understanding of speculative decoding and draft model usage. Hands-on experience with quantization-aware training, including PyTorch QAT workflows or similar frameworks. Familiarity with open-weight foundation models and continued/pre-training techniques. Proficient in Python and ML frameworks such as PyTorch, JAX, or TensorFlow.

Preferred Qualifications: Experience optimizing models for fast inference and sampling in production environments. Exposure to distributed training, low-level kernel optimizations, and inference-time system constraints. Publications or contributions to open-source ML projects.

Attributes of a Groqster: Humility - Egos are checked at the door Collaborative & Team Savvy - We make up the smartest person in the room, together Growth & Giver Mindset - Learn it all versus know it all, we share knowledge generously Curious & Innovative - Take a creative approach to projects, problems, and design Passion, Grit, & Boldness - no limit thinking, fueling informed risk taking

Austin, TX

About the Team

Avride builds autonomous solutions from the ground up, using machine learning as the core of our navigation pipeline. We are evolving our stack to support the next generation of self-driving, leveraging efficient CNNs, Transformers, and MLLMs to solve complex perception and planning challenges. Our goal is to apply the right approach to the right problem, laying the groundwork for unified, data-driven approaches.

About the Role

We are seeking a Machine Learning Engineer to build the infrastructure and ML foundations for advanced autonomous behaviors. You won't just optimize isolated models; you will architect scalable training workflows and high-fidelity components.

This is a strategic position: You will contribute to the critical infrastructure that paves the way for future end-to-end capabilities. You will translate relevant research ideas into production-ready improvements when they prove beneficial, helping prepare our stack for a transition toward unified, learned behaviors.

What You'll Do

• Strengthen Core Modules: Design and refine models for perception, prediction, or planning, enhancing reliability to support future holistic learning approaches.
• Architect Data Foundations: Build scalable pipelines for multimodal datasets, ensuring they support both current needs and future large-scale E2E experiments.
• Advance Training Infra: Develop distributed training workflows capable of handling massive model architectures for next-gen foundation models.
• Bridge Research & Production: Analyze research in relevant fields, identifying specific opportunities to introduce these techniques into our production stack.
• System Integration: Collaborate with engineering teams to ensure individual ML improvements translate into better system-level performance.

What You'll Need

• Strong ML Fundamentals: Mastery of processing and fusing self-driving modalities (multiview camera, sparse LiDAR, vector maps).
• Architectural Expertise: Deep knowledge of modern architectures like Transformers and Attention Mechanisms.
• Applied Experience: 5+ years of combined experience in industry or applied research settings, with a strong grasp of the full lifecycle from data to deployment.
• Technical Proficiency: Python, PyTorch/JAX/TensorFlow, and distributed computing (PySpark, Ray).
• Systems Mindset: Ability to visualize how modular systems evolve into end-to-end learners and the practical challenges of deploying them.
• Research Capability: Ability to distill complex papers into practical engineering roadmaps.

Nice to Have

• Advanced degree in CS, ML, Robotics, or related field.
• Familiarity with World Models, Occupancy Networks, or Joint Perception-Planning.
• Experience with inference optimization (Triton, TensorRT) and embedded hardware.

New York, New York

We are looking for a foundational member of the Cloud infrastructure team at WRITER. This role will involve contributing to the development and implementation of our Site reliability engineering (SRE) program. The ideal candidate will ensure the reliability, scalability, performance, and security of WRITER’s critical systems, taking a proactive approach to guarantee that our high-ROI products reach our customers seamlessly.

Your responsibilities:

• Lead the design, implementation, and maintenance of WRITER, Inc.’s cloud infrastructure to ensure high availability and performance
• Design and implement scalable cloud automation to support seamless deployment for our largest enterprise customers
• Automate infrastructure provisioning and management using Terraform & Python
• Collaborate with development teams to optimize cloud resources and enhance system reliability
• Develop and maintain monitoring and alerting systems to proactively identify and resolve issues affecting the reliability of our writing solutions
• Conduct post-mortem analyses of system failures to identify root causes and implement preventive measures
• Optimize and scale our cloud infrastructure to support growing user demand and ensure cost efficiency
• Ensure the security and compliance of our systems, adhering to industry standards and regulations
• Provide mentorship and technical guidance to junior engineers, fostering a culture of reliability and continuous improvement
• Stay current with emerging technologies and industry trends to continuously improve our site reliability practices

Is this you?

• Proven expertise in Site Reliability Engineering with a minimum of 7 years of hands-on experience
• Deep understanding of system architecture and infrastructure design to ensure high availability and performance
• Bachelor’s degree in Computer Science, Engineering, or a related technical field
• Strong proficiency in programming languages such as Python, Java, Go for automation and monitoring
• Experience with cloud platforms like AWS, Azure, or GCP, and their respective services for scalable and resilient systems
• Expertise in containerization technologies (e.g., Docker, Kubernetes) and orchestration tools
• Knowledge of monitoring and logging tools (e.g., Prometheus, Grafana, ELK Stack) to maintain system health and performance
• Ability to lead and mentor junior engineers in best practices for reliability and system optimization
• Excellent communication skills to collaborate effectively with cross-functional teams and stakeholder
• Proactive approach to identifying and mitigating potential system failures and performance bottlenecks

Location Beijing CHINA

Description

1. Program and Vision： BAAI launches its "Rising Star" Researcher Program, designed to recruit exceptional young scholars who have demonstrated outstanding research potential in AI and related fields. We provide a world-class research platform and robust development support, enabling you to launch your academic career from a high starting point, collaborate with leading scientists, and rapidly grow into a future leader in your field.

2. Qualifications：

3. A record of notable early-career research achievements in AI, Computer Science, Mathematics, or related interdisciplinary fields, demonstrating significant potential.
4. A soon-to-graduate outstanding Ph.D. candidate, a postdoctoral fellow, or an early-career scholar with a pure passion for scientific inquiry and innovation.

5. Strong independent research capabilities and a collaborative spirit.

6. We Offer：

7. A market-competitive salary and benefits package with a clear path for career advancement.
8. Ample start-up research funding and shared access to top-tier computing resources.
9. A clear career development path, with support to grow into an independent researcher.
10. Access to subsidized talent apartments and support for Beijing residency registration.

11. A comprehensive supplementary health insurance plan.

12. How to Apply： Please send your full CV, representative publications, and reference letters or contact information for references to: [recruiting@baai.ac.cn] Use the email subject line: "Researcher Application - [Name] - [Specific Research Focus]"

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

Bala Cynwyd (Philadelphia Area), Pennsylvania United States & New York, New York United States

Overview

Our Machine Learning PhD Internship is a 10-week immersive experience designed for PhD candidates who are passionate about solving high-impact problems at the intersection of data, algorithms, and markets.

As a Machine Learning Intern at Susquehanna, you’ll work on high-impact projects that closely reflect the challenges and workflows of our full-time research team. You’ll apply your technical expertise in machine learning and data science to real-world financial problems, while developing a deep understanding of how machine learning integrates into Susquehanna’s research and trading systems. You will leverage vast and diverse datasets and apply cutting-edge machine learning at scale to drive data-informed decisions in predictive modeling to strategic execution.

What You Can Expect

 • Conduct research and develop ML models to identify patterns in noisy, non-stationary data

 • Work side-by-side with our Machine Learning team on real, impactful problems in quantitative trading and finance, bridging the gap between cutting-edge ML research and practical implementation

 • Collaborate with researchers, developers, and traders to improve existing models and explore new algorithmic approaches

 • Design and run experiments using the latest ML tools and frameworks

 • One-on-one mentorship from experienced researchers and technologists

 • Participate in a comprehensive education program with deep dives into Susquehanna’s ML, quant, and trading practices

 • Apply rigorous scientific methods to extract signals from complex datasets and shape our understanding of market behavior

 • Explore various aspects of machine learning in quantitative finance from alpha generation and signal processing to model deployment and risk-aware decision making

What we're looking for

 • Currently pursuing a PhD in Computer Science, Machine Learning, Statistics, Physics, Applied Mathematics, or a closely related field

 • Proven experience applying machine learning techniques in a professional or academic setting

 • Strong publication record in top-tier conferences such as NeurIPS, ICML, or ICLR

 • Hands-on experience with machine learning frameworks, including PyTorch and TensorFlow

 • Deep interest in solving complex problems and a drive to innovate in a fast-paced, competitive environment

Why Join Us?

 • Work with a world-class team of researchers and technologists

 • Access to unparalleled financial data and computing resources

 • Opportunity to make a direct impact on trading performance

 • Collaborative, intellectually stimulating environment with global reach