About Handshake AI Handshake is building the career network for the AI economy. Our three-sided marketplace connects 18 million students and alumni, 1,500+ academic institutions across the U.S. and Europe, and 1 million employers to power how the next generation explores careers, builds skills, and gets hired. Handshake AI is a human data labeling business that leverages the scale of the largest early career network. We work directly with the world’s leading AI research labs to build a new generation of human data products. From PhDs in physics to undergrads fluent in LLMs, Handshake AI is the trusted partner for domain-specific data and evaluation at scale. This is a unique opportunity to join a fast-growing team shaping the future of AI through better data, better tools, and better systems—for experts, by experts.

Now’s a great time to join Handshake. Here’s why: Leading the AI Career Revolution: Be part of the team redefining work in the AI economy for millions worldwide. Proven Market Demand: Deep employer partnerships across Fortune 500s and the world’s leading AI research labs. World-Class Team: Leadership from Scale AI, Meta, xAI, Notion, Coinbase, and Palantir, just to name a few. Capitalized & Scaling: $3.5B valuation from top investors including Kleiner Perkins, True Ventures, Notable Capital, and more.

About the Role Handshake AI builds the data engines that power the next generation of large language models. Our research team works at the intersection of cutting-edge model post-training, rigorous evaluation, and data efficiency. Join us for a focused Summer 2026 internship where your work can ship directly into our production stack and become a publishable research contribution. To start between May and June 2026.

Projects You Could Tackle LLM Post-Training: Novel RLHF / GRPO pipelines, instruction-following refinements, reasoning-trace supervision. LLM Evaluation: New multilingual, long-horizon, or domain-specific benchmarks; automatic vs. human preference studies; robustness diagnostics. Data Efficiency: Active-learning loops, data value estimation, synthetic data generation, and low-resource fine-tuning strategies. Each intern owns a scoped research project, mentored by a senior scientist, with the explicit goal of an archive-ready manuscript or top-tier conference submission.

Desired Capabilities Current PhD student in CS, ML, NLP, or related field. Publication track record at top venues (NeurIPS, ICML, ACL, EMNLP, ICLR, etc.). Hands-on experience training and experimenting with LLMs (e.g., PyTorch, JAX, DeepSpeed, distributed training stacks). Strong empirical rigor and a passion for open-ended AI questions.

Extra Credit Prior work on RLHF, evaluation tooling, or data selection methods. Contributions to open-source LLM frameworks. Public speaking or teaching experience (we often host internal reading groups).

Senior Software Engineer (Backend)

Location: Boston (US) / Barcelona (Spain)

About us:

Axiomatic_AI is dedicated to accelerating R&D by developing the next generation of Automated Interpretable Reasoning, a verifiably truthful AI model built for reasoning in science and engineering, empowering engineers specifically in hardware design and Electronic Design Automation (EDA), with a mission to revolutionize the fields of hardware design and simulation in the photonics and semiconductor industry. We seek highly motivated professionals to help us bring these innovations to life, driving the evolution from development to commercial product.

Position Overview

As a Senior Software Engineer (Backend) at Axiomatic, you will:

• Design and build scalable backend services (FastAPI, FastMCP, Python)
• Own key features end-to-end: from API design to database schema to deployment
• Integrate AI capabilities (LLMs, Agents) into production systems
• Collaborate with frontend, AI, and infrastructure teams on architecture and delivery
• Ensure system reliability, performance, and security
• Mentor other engineers as the team grows

Key Responsibilities

Architecture & Design

• Contribute to system architecture and technical decisions
• Design for scalability, reliability, and security
• Propose and implement improvements to codebase and infrastructure
• Document technical designs and API contracts
• Ensure best practices are followed by the team

Backend Development

• Design and implement REST APIs using FastAPI
• Build scalable, maintainable, and testable services
• Design database schemas and optimize SQL queries (PostgreSQL)
• Integrate with external services (OpenAI, Anthropic)
• Optimize API performance, latency, and throughput

Quality & Testing

• Write comprehensive unit and integration tests
• Participate in code reviews (give and receive feedback)
• Debug and resolve production issues
• Maintain high code quality standards

Collaboration & Mentorship

• Work closely with Tech Lead on architecture and roadmap
• Partner with AI Platform Engineer on AI integrations
• Mentor mid-level engineers and share knowledge

Required Skills & Experience

Must-Have

• 7+ years of backend development experience
• Deep knowledge in Python, FastAPI
• Strong database skills: PostgreSQL, SQL, ORMs (SQLAlchemy)
• Experience designing REST APIs: best practices, versioning, documentation
• Cloud platform experience: GCP preferred (AWS, Azure acceptable)
• Testing mindset: unit tests, integration tests, TDD
• Version control & CI/CD: Git, GitHub Actions, Docker
• Strong problem-solving skills: debugging, performance optimization
• Fluent in English (Spanish is a plus)

Nice-to-Have

• Experience with FastMCP
• Familiarity with LangChain, Pydantic AI or similar frameworks
• Knowledge of async programming (asyncio, async/await)
• Familiarity with AI/ML APIs (OpenAI, HuggingFace, Vertex AI)
• Understanding of infrastructure as code (Terraform)
• Experience with microservices architecture

Tech Stack

Current Stack:

• Backend: Python, FastAPI, SQLAlchemy, Pydantic AI, Alembic
• Databases: PostgreSQL, Redis (caching)
• APIs: REST, WebSockets, SSE
• AI/ML: OpenAI API, Anthropic, Gemini
• Cloud: Google Cloud Platform (Cloud Run, Cloud SQL, GCS, VPCs, Bucket)
• Infrastructure: Terraform, Docker
• CI/CD: GitHub Actions, Terraform
• Testing: pytest, pytest-asyncio, pytest-cov

About Handshake AI Handshake is building the career network for the AI economy. Our three-sided marketplace connects 18 million students and alumni, 1,500+ academic institutions across the U.S. and Europe, and 1 million employers to power how the next generation explores careers, builds skills, and gets hired. Handshake AI is a human data labeling business that leverages the scale of the largest early career network. We work directly with the world’s leading AI research labs to build a new generation of human data products. From PhDs in physics to undergrads fluent in LLMs, Handshake AI is the trusted partner for domain-specific data and evaluation at scale. This is a unique opportunity to join a fast-growing team shaping the future of AI through better data, better tools, and better systems—for experts, by experts.

Now’s a great time to join Handshake. Here’s why: Leading the AI Career Revolution: Be part of the team redefining work in the AI economy for millions worldwide. Proven Market Demand: Deep employer partnerships across Fortune 500s and the world’s leading AI research labs. World-Class Team: Leadership from Scale AI, Meta, xAI, Notion, Coinbase, and Palantir, just to name a few. Capitalized & Scaling: $3.5B valuation from top investors including Kleiner Perkins, True Ventures, Notable Capital, and more.

About the Role As a Staff Research Scientist, you will play a pivotal role in shaping the future of large language model (LLM) alignment by leading research and development at the intersection of data quality and post-training techniques such as RLHF, preference optimization, and reward modeling. You will operate at the forefront of model alignment, with a focus on ensuring the integrity, reliability, and strategic use of supervision data that drives post-training performance. You’ll set research direction, influence cross-functional data standards, and lead the development of scalable systems that diagnose and improve the data foundations of frontier AI.

You will: Lead high-impact research on data quality frameworks for post-training LLMs — including techniques for preference consistency, label reliability, annotator calibration, and dataset auditing. Design and implement systems for identifying noisy, low-value, or adversarial data points in human feedback and synthetic comparison datasets. Drive strategy for aligning data collection, curation, and filtering with post-training objectives such as helpfulness, harmlessness, and faithfulness. Collaborate cross-functionally with engineers, alignment researchers, and product leaders to translate research into production-ready pipelines for RLHF and DPO. Mentor and influence junior researchers and engineers working on data-centric evaluation, reward modeling, and benchmark creation. Author foundational tools and metrics that connect supervision data characteristics to downstream LLM behavior and evaluation performance. Publish and present research that advances the field of data quality in LLM post-training, contributing to academic and industry best practices.

Desired Capabilities PhD or equivalent experience in machine learning, NLP, or data-centric AI, with a track record of leadership in LLM post-training or data quality research. 5 years of academic or industry experience post-doc Deep expertise in RLHF, preference data pipelines, reward modeling, or evaluation systems. Demonstrated experience designing and scaling data quality infrastructure — from labeling frameworks and validation metrics to automated filtering and dataset optimization. Strong engineering proficiency in Python, PyTorch, and ecosystem tools for large-scale training and evaluation. A proven ability to define, lead, and execute complex research initiatives with clear business and technical impact. Strong communication and collaboration skills, with experience driving strategy across research, engineering, and product teams.

Work Location:

Toronto, Ontario, Canada

Description

We are currently seeking talented individuals for a variety of positions, ranging from junior to senior levels, and will evaluate your application in its entirety.

Layer 6 is the AI research centre of excellence for TD Bank Group. We develop and deploy industry-leading machine learning systems that impact the lives of over 27 million customers, helping more people achieve their financial goals and needs. Our research broadly spans the field of machine learning with areas such as deep learning and generative AI, time series forecasting and responsible use of AI. We have access to massive financial datasets and actively collaborate with world renowned academic faculty.

We are always looking for people driven to be at the cutting edge of machine learning in research, engineering, and impactful applications.

As a Research Machine Learning Scientist, you will

• Join a world-class team of machine learning researchers with an extensive track record in both academia and industry.

• Research, develop, and apply new techniques in deep learning to advance our industry leading products.

• Work with large-scale, real-world datasets that range from banking transactions, to large document collections.

• Collaborate closely with our engineering team in a fast-paced startup environment and see your research deployed in production with very short turnaround.

Required Qualifications:

• PhD or Master’s degree in Computer Science, Statistics, Mathematics, Engineering or a related field

• Strong background in machine learning and deep learning

• 2+ years of research experience with publication record

• Proven track record of applying machine learning to solve real-world problems

Preferred Qualifications:

• Depth of experience in relevant ML research disciplines

• Hands on experience in software systems development

• Experience with one or more of Pytorch, Tensorflow, Jax, or comparable library

• Experience with Spark, SQL, or comparable database systems

• Experience using GPUs for accelerated deep learning training

• Familiarity with cloud computing systems like Azure or AWS

Shanghai

Key Responsibilities • Building the compute platform and machine learning libraries for large scale machine learning and simulation workloads • Focus on compute platform stability and efficiency on both CPU and GPU clusters, making the platform observable and scalable • Utilize cluster monitoring and profiling tools to identify bottlenecks and optimize both infrastructure and software system • Troubleshoot and resolve issues related to OS, storage, network, and GPUs

Challenges You Will Tackle: design, build and improve our compute platform for PB scale data model training and simulations with a wide range of machine learning models by leveraging our existing research infrastructure.

Requirements: • Solid experience in running production machine learning infrastructure at a large scale • Experience in designing, deploying, profiling and troubleshooting in Linux-based computing environments • Proficiency in containerization, parallel computing and distributed training algorithms • Experience with storage solutions for large scale, cluster-based data intensive workloads

Bonus qualification: • Experience of supporting machine learning researchers or data scientists for production workloads

WHAT YOU CAN EXPECT FROM US: In return for you joining our elite team, you will be offered a competitive salary package as well as access to a plethora of Optiver-perks. To hear more about what it is like to work here and our great culture, apply now and take the first step towards the best career move you will ever make!

DIVERSITY AND INCLUSION Optiver is committed to diversity and inclusion, and it is hardwired through every stage of our hiring process. We encourage applications from candidates from any and all backgrounds, and we welcome requests for reasonable adjustments during the process to ensure that you can best demonstrate your abilities.

PRIVACY DISCLAIMER

Optiver 重视个人信息的保护。请您在提供个人信息给我们之前，认真阅读Optiver China Privacy Notice, 了解我们如何收集及处理您的个人信息。 Personal information protection is of utmost importance to Optiver. Before you provide any personal information to us, we strongly urge you to read our Privacy Policy to acknowledge how we collect and process your personal information.

Pinely is a privately owned algorithmic trading firm specializing in high-frequency and mid-frequency trading. We’re based in Amsterdam, Cyprus, and Singapore, and we’re experiencing rapid growth. We are seeking a Staff Deep Learning Scientist to drive advanced AI research. This senior individual contributor role focuses on leading technical innovation and shaping research direction across the team. The ideal candidate has deep curiosity, hands-on expertise in neural networks, and prior experience at top AI labs, contributing directly to building and deploying models.

Responsibilities:

• Develop AI models powering every component of end-to-end trading strategies across global markets;
• Tackle the hardest real-world AI problem — predicting financial markets — by understanding deep networks in extremely noisy, diverse, and ever-changing environments;
• Shape research direction and elevate team capabilities through your insights;
• Lead all stages of research from ideation to deployment, ensuring full production integration.

Requirements:

• Senior/Staff/Principal Researcher at a top AI lab or faculty member at a leading institution (Stanford, Berkeley, MIT, CMU, ETH, Mila, UofT, Oxford, UCL, NYU, Princeton, etc.);
• Preferably experienced in competitive AI domains: LLMs, reasoning architectures, generative models (e.g., video), mechanistic interpretability;
• Motivated by deep research and meaningful impact on both the team and the field.

What we offer:

• Significant impact across the company’s entire trading portfolio;
• Competitive compensation with exceptional upside through profit-sharing;
• A research-driven environment where deep technical insight directly influences outcomes;
• Option to work part-time alongside an academic lab;
• A culture that supports initiative, exploration, and high performance;
• Flexible work location: Amsterdam office or fully remote, with optional business travel.

San Francisco, CA, US; Palo Alto, CA, US; Seattle, WA, US; Remote, US

About Pinterest:

Millions of people around the world come to our platform to find creative ideas, dream about new possibilities and plan for memories that will last a lifetime. At Pinterest, we’re on a mission to bring everyone the inspiration to create a life they love, and that starts with the people behind the product.

Discover a career where you ignite innovation for millions, transform passion into growth opportunities, celebrate each other’s unique experiences and embrace the flexibility to do your best work. Creating a career you love? It’s Possible.

With more than 600 million users around the world and 400 billion ideas saved, Pinterest Machine Learning engineers build personalized experiences to help Pinners create a life they love. With just over 4,000 global employees, our teams are small, mighty, and still growing. At Pinterest, you’ll experience hands-on access to an incredible vault of data and contribute large-scale recommendation systems in ways you won’t find anywhere else.

We are seeking talented Staff Machine Learning Engineers for multiple openings across our Core Engineering organization, including teams such as Search, Notifications, and Content & User Engineering. In these roles, you will drive the development of state-of-the-art applied machine learning systems that power core Pinterest experiences.

What you’ll do:

• Design features and build large-scale machine learning models to improve user ads action prediction with low latency
• Develop new techniques for inferring user interests from online and offline activity
• Mine text, visual, and user signals to better understand user intention
• Work with product and sales teams to design and implement new ad products

What we’re looking for:

• Degree in computer science, machine learning, statistics, or related field
• 6+ years of industry experience building production machine learning systems at scale, data mining, search, recommendations, and/or natural language processing
• 2+ years of experience leading projects/teams
• Strong mathematical skills with knowledge of statistical methods
• Cross-functional collaborator and strong communicator

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.

Remote US or Canada

Mission: Join the team that builds and operates Groq’s real-time, distributed inference system delivering large-scale inference for LLMs and next-gen AI applications at ultra-low latency. As a Low-Level Production Engineer, your mission is to ensure reliability, fault tolerance, and operational excellence in Groq’s LPU-powered infrastructure. You’ll work deep in the stack—bridging distributed runtime systems with the hardware—to keep Groq systems fast, stable, and production-ready at scale.

Responsibilities & opportunities in this role: Production Reliability: Operate and harden Groq’s distributed runtime across thousands of LPUs, ensuring uptime and resilience under dynamic global workloads. Low-Level Debugging: Diagnose and resolve hardware-software integration issues in live environments, from datacenter level events to single component failures. Observability & Diagnostics: Build tools and infrastructure to improve real-time system monitoring, fault detection, and SLO tracking. Automation & Scale: Automate deployment workflows, failover systems, and operational playbooks to reduce overhead and accelerate reliability improvements. Performance & Optimization: Profile and tune production systems for throughput, latency, and determinism—every cycle counts. Cross-Functional Collaboration: Partner with compiler, hardware, infra, and data center teams to deliver robust, fault-tolerant production systems.

Ideal candidates have/are: Proven experience in production engineering across the stack and operating large-scale distributed systems. Deep knowledge of computer architecture, operating systems, and hardware-software interfaces. Skilled in low-level systems programming (C/C++ or Rust), with scripting fluency (Python, Bash, or Go). Comfortable debugging complex issues close to the metal—kernels, firmware, or hardware-aware code paths. Strong background in automation, CI/CD, and building reliable systems that scale. Thrive across environments—from kernel internals to distributed runtimes to data center operations. Communicate clearly, make pragmatic decisions, and take ownership of long-term outcomes.

Nice to have: Experience operating high-performance, real-time systems at scale (ML inference, HPC, or similar). Familiarity with GPUs, FPGAs, or ASICs in production environments. Prior exposure to ML frameworks (e.g., PyTorch) or compiler tooling (e.g., MLIR). Track record of delivering complex production systems in high-impact environments.

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

New York / Chicago / Austin

You will develop, refine and implement algorithmic trading strategies that shape the future of electronic trading. Working alongside a research team of mathematicians, scientists and technologists, you will leverage vast data sets to construct complex models to predict market movements. With your expertise in statistics and exceptional analytical and research skills, you will develop innovative solutions that are foundational to Optiver’s trading strategies.

You will participate in Optiver’s Global Academy and be equipped with the knowledge needed to make an impact the moment you join your team. The comprehensive training covers trading theory and Optiver’s tech stack to hone your skills for your role. You will also be paired with a dedicated mentor who will empower you to take ownership of your work and make a difference.

As a Quantitative Researcher, you will have the opportunity to contribute to several key areas: • Using statistical models and machine learning to develop trading algorithms. • Leveraging big data technologies to analyze high-frequency trading strategies, market microstructure and financial instruments to identify trading opportunities. • Building stochastic models to determine the fair value of financial derivatives. • Combining quantitative analysis and high-performance implementation to ensure the efficiency and accuracy of pricing engines and libraries.

You’ll join a culture of collaboration and excellence, where you’ll be surrounded by curious thinkers and creative problem solvers. Motivated by a passion for continuous improvement, you’ll thrive in a supportive, high-performing environment alongside talented colleagues, working collectively to tackle the toughest problems in the financial markets. In addition, you’ll receive: • A performance-based bonus structure unmatched anywhere in the industry. We combine our profits across desks, teams and offices into a global profit pool. • The opportunity to work alongside best-in-class professionals from over 40 different countries. • Ownership over initiatives that directly solve business problems. • 401(k) match up to 50% and fully paid health insurance. • 25 paid vacation days alongside market holidays. • Extensive office perks, including breakfast, lunch and snacks, regular social events, clubs, sporting leagues and more.

Who you are: • PhD in Mathematics, Statistics, Computer Science, Physics, or a related STEM field, with outstanding academic achievements • Expected to graduate by mid-2026 and available to start full-time employment upon graduation • Solid foundation in mathematics, probability, and statistics • Excellent research, analytical, and modeling skills • Independent research experience • Proficiency in any programming language • Experience in machine learning, with practical applications in time-series analysis and pattern recognition • Strong interest in working in a fast-paced, collaborative environment • Fluent in English with strong written and verbal communication skills

At Optiver, our mission is to improve the market by injecting liquidity, providing accurate pricing, increasing transparency and stabilising the market no matter the conditions. With a focus on continuous improvement, we prioritise safeguarding the health and efficiency of the markets for all participants. As one of the largest market making institutions, we are a respected partner on 100+ exchanges across the globe. Our differences are our edge. Optiver does not discriminate on the basis of race, religion, color, sex, gender identity, sexual orientation, age, physical or mental disability, or other legally protected characteristics.

Optiver is supportive of US immigration sponsorship for this role.

*We accept one application per role per year. If you have previously applied to this position during this season and have been unsuccessful, you can reapply once the next recruitment season begins in 2026.