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

Toronto or Remote from US

Mission: As Senior Staff Compiler Engineer, you will be responsible for defining and developing compiler optimizations for our state-of-the-art compiler, targeting Groq's revolutionary LPU, the Language Processing Unit.

In this role you will drive the future of Groq's LPU compiler technology. You will be in charge of architecting new passes, developing innovative scheduling techniques, and developing new front-end language dialects to support the rapidly evolving ML space. You will also be required to benchmark and monitor key performance metrics to ensure that the compiler is producing efficient mappings of neural network graphs to the Groq LPU.

Ideal candidates have experience with LLVM and MLIR, and knowledge with functional programming languages an asset. Also, knowledge with ML frameworks such as TensorFlow and PyTorch, and portable graph models such as ONNX desired.

Responsibilities & opportunities in this role: Compiler Architecture & Optimization: Lead the design, development, and maintenance of Groq’s optimizing compiler, building new passes and techniques that push the performance envelope on the LPU. IR Expansion & ML Enablement: Extend Groq’s intermediate representation dialects to capture emerging ML constructs, portable graph models (e.g., ONNX), and evolving deep learning frameworks. Performance & Benchmarking: Benchmark compiler outputs, diagnose inefficiencies, and drive enhancements to maximize quality-of-results on LPU hardware. Cross-Disciplinary Collaboration: Partner with hardware architects and software leads to co-design compiler and system improvements that deliver measurable acceleration gains. Leadership & Mentorship: Mentor junior engineers, review contributions, and guide large-scale, multi-geo compiler projects to completion. Innovation & Impact: Publish novel compilation techniques and contribute thought leadership to top-tier ML, compiler, and computer architecture conferences.

Ideal candidates have/are: 8+ years of experience in the area of computer science/engineering or related 5+ years of direct experience with C/C++ and LLVM or compiler frameworks Knowledge of spatial architectures such as FPGA or CGRAs an asset Knowledge of functional programming an asset Experience with ML frameworks such as TensorFlow or PyTorch desired Knowledge of ML IR representations such as ONNX and Deep Learning

Additionally nice to have: Strong initiative and personal drive, able to self-motivate and drive projects to closure Keen attention to detail and high levels of conscientiousness Strong written and oral communication; ability to write clear and concise technical documentation Team first attitude, no egos Leadership skills and ability to motivate peers Optimistic Outlook, Coaching and mentoring ability

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

Bala Cynwyd (Philadelphia Area), Pennsylvania United States

Overview

We’re looking for a Machine Learning Systems Engineer to help build the data infrastructure that powers our AI research. In this role, you'll develop reliable, high-performance systems for handling large and complex datasets, with a focus on scalability and reproducibility. You’ll partner with researchers to support experimental workflows and help translate evolving needs into efficient, production-ready solutions. The work involves optimizing compute performance across distributed systems and building low-latency, high-throughput data services. This role is ideal for someone with strong engineering instincts, a deep understanding of data systems, and an interest in supporting innovative machine learning efforts.

What You’ll Do

Design and implement high-performance data pipelines for processing large-scale datasets with an emphasis on reliability and reproducibility Collaborate with researchers to translate their requirements into scalable, production-grade systems for AI experimentation Optimize resource utilization across our distributed computing infrastructure through profiling, benchmarking, and systems-level improvements Implement low-latency high-throughput sampling for models

What we're looking for

Experience building and maintaining data pipelines and ETL systems at scale Experience with large-scale ML infrastructure and familiarity with training and inference workflows Strong understanding of best practices in data management and processing Knowledge of systems level programming and performance optimization Proficiency in software engineering in python Understanding of AI/ML workloads, including data preprocessing, feature engineering, and model evaluation

Why Join Us?

Susquehanna is a global quantitative trading firm that combines deep research, cutting-edge technology, and a collaborative culture. We build most of our systems from the ground up, and innovation is at the core of everything we do. As a Machine Learning Systems Engineer, you’ll play a critical role in shaping the future of AI at Susquehanna — enabling research at scale, accelerating experimentation, and helping unlock new opportunities across the firm.

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.

Work Location: Toronto, Ontario, Canada

Job Description

We are currently seeking talented individuals for a variety of positions, ranging from mid 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.

Day-to-day as a Technical Product Owner:

• Translate broad business problems into sharp data science use cases, and craft use cases into product visions

• Own machine learning products from vision to backlog; prioritizing features and defining minimum viable releases; maximizing the value your products generate, and the ROI of your pod

• Guide Agile pods on continuous improvement, ensuring that the next sprint is delivered better than the previous

• Work closely with stakeholders to identify, refine and (occasionally) reject opportunities to build machine learning products; collaborate with support functions such as risk, technology, model risk management and incorporate interfacing features

• Facilitate the professional & technical development of your colleagues through mentorship and feedback

• Anticipate resource needs as solutions move through the model lifecycle, scaling pods up and down as models are built, perform, degrade, and need to be rebuilt

• Championing model development standards, industry best-practices and rigorous testing protocols to ensure model excellence

• Self-direct, with the ability to identify meaningful work in down times and effectively prioritize in busy times

• Drive value through product, feature & release prioritization, maximizing ROI & modelling velocity

• Be an exceptional collaborator in a high-interaction environment

Job Requirements

• Minimum five years of experience delivering major data science projects in large, complex organizations

• Strong communication, business acumen and stakeholder management competencies

• Strong technical skills: machine learning, data engineering, MLOps, cloud solution architecture, software development practices

• Strong coding proficiency: python, R, SQL and / or Scala, cloud architecture

• Certified Scrum Product Owner and / or Certified Scrum Master or equivalent experience

• Familiarity with cloud solution architecture, Azure a plus

• Master’s degree in data science, artificial intelligence, computer science or equivalent experience

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

Boston/NYC/LA/SF

About Suno

At Suno, we are building a future where anyone can make music. You can make a song for any moment with just a few short words. Award-winning artists use Suno, but our core user base consists of everyday people making music — often for the first time.

We are a team of musicians and AI experts, including alumni from Spotify, TikTok, Meta and Kensho. We like to ship code, make music and drink coffee. Our company culture celebrates music and experimenting with sound — from lunchroom conversations to the studio in our office.

Over the last two years, nearly 100 million people have made music on Suno – many for the first time in their lives, discovering a passion they never knew they had. And this isn’t just a story about new creators: top producers and songwriters have integrated Suno into their daily workflows, and new artists emerging on Suno are being recognized by the industry’s most important charts. Suno has become a platform where imagination meets reality, at every level of the creative journey.

Recently Suno announced its 250M Series C at a 2.45B post-money valuation.

About the Role

We’re looking for research scientists and engineers to build foundation models for music and audio. Our research focuses not only on generative tasks, but also understanding such as source separation, captioning, lyrics transcription, midi transcription, and alignment.We have roles open for pre-training, post-training, multimodal architectures, data, distributed training, and inference optimization. We are a small research team with a very large cluster, serving millions of users daily.

Healthcare for you and your dependents, with vision and dental

401k with match

Generous commuter benefit

Flexible PTO

USA – Austin, San Jose

Overview

Arm technology is becoming the platform of choice for compute and AI. The Arm System Engineering team’s mission is to architect, design, and develop scalable compute platforms. Our capabilities span hardware and software, interconnects, system management, storage, physical infrastructure, and performance engineering. We lead customer collaborations, technology evaluation, end-to-end architecture, network strategy, and rigorous performance analysis. The team is developing advanced technologies to deliver innovative, high-performance solutions to power high-performance AI/ML applications. Job Overview:

The Performance Engineering Team plays a central role in enabling and optimizing performance across Arm’s compute systems. The team’s charter is to model, measure, and optimize performance at scale, ensuring Arm-based solutions achieve world-class efficiency and throughput for diverse workloads—from AI training and inference to scientific and data-intensive computing Responsibilities:

The System Engineering team is looking for AI/ML, Software, and Performance Engineers that will be responsible for application performance and benchmarking to enable ARM’s customer base in the datacenter space. Ownership of system performance for AI workloads and related software tools Collaboration with platform, interconnect, software, storage and system management engineering teams to perform system-scale testing and validation Oversight of support functions such as programs management, quality, and DevOps. Core Hiring Focus:

We are expanding our Performance Engineering team and seeking individuals passionate about pushing the limits of AI performance on systems built around Arm platforms. Key areas of recruitment include: Performance Modeling Engineers: Develop analytical and simulation-based performance models for large-scale AI/ML systems. AI and ML Performance Engineers: Optimize LLM and generative model training/inference workloads on Arm-based systems. Systems and Networking Engineers: Advance interconnect and collective communication performance across multi-node clusters, reduce system jitter. Storage Performance Engineers: Design and optimize parallel file system and near computer storage solutions for AI/ML workloads. Resilience and Reliability Engineers: Innovate in checkpointing, recovery, and resilient distributed training. Benchmarking Specialists: Lead evaluation and comparison of Arm-based performance using industry-standard metrics. In Return:

Be part of a groundbreaking team influencing the next generation of Arm systems for AI/ML computing! Collaborate with top engineers and vendors to develop industry-leading AI systems. Access professional growth through sophisticated project involvement and multidisciplinary teamwork. Join a company committed to diversity and inclusion, where your work matters and drives global progress!

Successful hires will work on developing and applying large language models (LLMs) to problems in molecular science and drug discovery. Responsibilities include:

• Scaling and optimizing large model training and inference workflows on cutting-edge DESRES infrastructure
• Pre-training, including designing data pipelines and distributed/parallel training
• Post-training techniques, such as reinforcement learning, contrastive learning, and instruction tuning
• Multimodal learning and integrating non-text modalities (for example, molecular graphs, 3D structures, and time series)

Ideal candidates will have deep expertise in large-scale machine learning systems, LLM architecture and training, and/or multimodal learning, as well as strong Python programming skills. While the application domains include areas such as drug discovery and biomolecular simulation, specific experience in any of these areas is less critical than intellectual curiosity, versatility, and a track record of achievement and innovation in the field of machine learning. For more information, visit www.DEShawResearch.com.

Please apply using the link below:

https://apply.deshawresearch.com/careers/Register?pipelineId=921&source=NeurIPS_1

The expected annual base salary for this position is USD 300,000 - USD 800,000. Our compensation package also includes variable compensation in the form of sign-on and year-end bonuses, and generous benefits, including relocation and immigration assistance. The applicable annual base salary paid to a successful applicant will be determined based on multiple factors including the nature and extent of prior experience and educational background. We follow a hybrid work schedule, in which employees work from the office on Tuesday through Thursday, and have the option of working from home on Monday and Friday.

D. E. Shaw Research, LLC is an equal opportunity employer.

Miami, Florida

As a ML/Research Engineer at Citadel Securities, you will work closely with researchers to design and build the next generation library for deep learning within the firm. You will combine the best available open-source tools with deep internal expertise in modelling and predicting financial markets. Your work will empower 100+ researchers to iterate faster on their agenda and perform experiments that were not possible before. Opportunities may be available from time to time in any location in which the business is based for suitable candidates. If you are interested in a career with Citadel, please share your details and we will contact you if there is a vacancy available.