New York

Software Developer

Technology is integral to virtually everything the D. E. Shaw group does, which is why we seek exceptional software developers with a range of quantitative and programming abilities. Members of our technical staff collaborate on challenging problems that directly impact the firm’s continued success, utilizing their excellent analytical, mathematical, and software design skills as well as some of the most advanced computing resources in the world. Software developers have the opportunity to be part of a collegial, collaborative, and engaging working environment.

What you'll do day-to-day

Specific responsibilities may include formulating statistical models for our computerized trading strategies, developing distributed systems to analyze and react to incoming data in real time, and creating tools for advanced mathematical modeling.

Who we're looking for

• Successful developers have traditionally been the top students in their programs and have extensive software development experience.
• We welcome outstanding candidates at all experience levels.
• The expected annual base salary for this position is 225,000USD. Our compensation and benefits package includes substantial variable compensation in the form of a year-end bonus, guaranteed in the first year of hire, a sign-on bonus, a relocation bonus, and benefits including medical and prescription drug coverage, 401(k) contribution matching, wellness reimbursement, family building benefits, and a charitable gift match program.

Remote - Americas

Applied Machine Learning Engineer

At Shopify, our Applied Machine Learning Engineers tackle the most challenging technical problems in commerce. By leveraging vast datasets and cutting-edge machine learning technologies, like multimodal LLMs, embeddings, etc., you will develop ways that redefine how merchants connect with buyers. Your work will empower merchants with advanced tools and systems that enhance search, discovery, and agents at a global scale.

As an Applied Machine Learning Engineer, you will work with petabyte-scale data and utilize state-of-the-art ML methods to build and deploy models that serve millions of users. You'll be at the forefront of AI innovation, using technologies including LLM posttraining, reinforcement learning, and model quantization, to redefine what’s possible in e-commerce.

Key Responsibilities:

• Analyze and interpret large-scale datasets to drive model development and optimization
• Collaborate with cross-functional teams to integrate ML solutions into Shopify's core products
• Design, build, and deploy agents and multimodal LLMs that improve merchant and buyer interactions
• Stay current with the latest advancements in machine learning technologies and frameworks
• Document and share technical insights and best practices across teams

Qualifications:

• Extensive experience in building and deploying machine learning models at scale
• Proficiency in using ML frameworks (e.g., TensorFlow, PyTorch) and programming languages like Python
• Strong analytical and problem-solving skills in solving real-world product problems
• Excellent communication skills and the ability to work in a fast-paced, collaborative environment

This role may require on-call work.

Ready to deploy your next breakthrough model? Join the team that’s making commerce better for everyone.

At Shopify, we pride ourselves on moving quickly—not just in shipping, but in our hiring process as well. If you're ready to apply, please be prepared to interview with us within the week. Our goal is to complete the entire interview loop within 30 days. You will be expected to complete a live pair programming session, come prepared with your own IDE.

The Department of Materials Science and Engineering (DMSE) together with the Schwarzman College of Computing (SCC) at Massachusetts Institute of Technology (MIT) in Cambridge, MA, seeks candidates at the level of tenure-track Assistant Professor to begin July 1, 2026 or on a mutually agreed date thereafter.

Materials engineering has always benefitted from theoretical and computational approaches to unveil relationships between structure, properties, processing, and performance. Recent advances in computing, including but not limited to artificial intelligence, are poised to dramatically advance the understanding and design of complex matter. DMSE and SCC jointly seek candidates with experience and interest in combining fundamental scientific principles with algorithmic innovations to empower discovery, understanding, and synthesis of materials with applications across critical societal domains --- healthcare, manufacturing, energy, sustainability, climate, and next-generation computing. This search encompasses all materials classes and scales, and is open to candidates with industry and start-up experience. Candidates are expected to develop research programs that target innovation in computational approaches well-suited to materials science and engineering research.

The successful candidate will have a shared appointment in both the Department of Materials Science and Engineering and SCC in either the Department of Electrical Engineering and Computer Science (EECS) or the Institute for Data, Systems, and Society (IDSS), depending on best fit.

Faculty duties include teaching at the undergraduate and graduate levels, advising students, conducting original scholarly research, and developing course materials at the graduate and undergraduate levels. Candidates are expected to teach in both the Department of Materials Science and Engineering and in the educational programs of SCC. The normal teaching load is two subjects per year.

Candidates should hold a Ph.D. in Materials Science and Engineering, Computer Science, Physics, Chemical Engineering, Chemistry, Applied Mathematics, or a related field. A PhD is required by the start of employment. The pay range for a 9-month academic appointment at the entry-level Assistant Professor rank (excluding summer salary): $140,000 - $150,000. The pay offered to a selected candidate during hiring will be based on factors such as (but not limited to) the scope and responsibilities of the position, the individual's work experience and education/training, internal peer equity, and applicable legal requirements. These factors impact where an individual's pay falls within a range. Employment is contingent upon the completion of a satisfactory background check, including verifying any finding of misconduct (or pending investigation) from prior employers.

Applications should include: (a) curriculum vitae, (b) research statement, (c) a teaching and mentoring plan. Each candidate should also include the names and contact information of 3 reference letter writers, who should upload their letters of recommendation by November 30, 2025.

Please submit online applications to https://faculty-searches.mit.edu/dmse_scc/register.tcl. To receive full consideration, completed applications must be submitted by November 30, 2025.

MIT is an equal opportunity employer. We value diversity and strongly encourage applications from individuals from all identities and backgrounds. All qualified applicants will receive equitable consideration for employment based on their experience and qualifications and will not be discriminated against on the basis of race, color, sex, sexual orientation, gender identity, pregnancy, religion, disability, age, genetic information, veteran status, or national or ethnic origin. See MIT's full policy on nondiscrimination. Know your rights.

Waddle Labs: - we are an early-stage startup - we build robotics models to solve physical bottlenecks in science (eg. wet lab experiments) - YC W26

The other companies on this career site know what they're doing. We don't. Do you want to help us figure it out?

If you want to find out more, reach out to wave@waddlelabs.ai with 1 sentence about what you’re interested in.

AI Engineer - Agentic AI for scientific workflows

Location: Boston (US) / Barcelona (Spain)

Position overview:

As an AI Engineer specialized in Agentic AI workflows, you will play a key role in building new verifiable workflows for science and engineering. Your responsibilities will include designing, prototyping, developing and testing our pipeline. You will also manage data curation, conduct benchmarking to evaluate performance, analyze reasoning flaws and propose solutions. Close collaboration with our dedicated cross-functional team - consisting of AI Engineers, Software Engineers, Physicists and AI scientists - will be essential to the success of the project.

Your mission:

• AI Development: Contribute to the development of novel AI reasoning models and architectures, focusing on advanced reasoning techniques and application to scientific fields where rigour and reliability are fundamental.
• Data & Benchmarking: Supervise dataset curation, run benchmarks, and analyze performance results to guide improvements.
• Collaboration: Work closely with a cross-functional team of engineers and scientists, collaborating on solving challenging problems at the intersection of AI, physics and engineering.
• Documentation and Reporting: Develop detailed technical documentation and present research findings to internal teams and external stakeholders.
• Research & Publication: Contribute to cutting-edge research and publish results in top AI conferences and journals, helping advance the global AI research community whenever opportunities arise.

Key requirements:

• Master’s degree in Data Science, Computer Science, Information Technology, Artificial Intelligence, Physics or related field.
• 2+ years of experience, preferably in a mathematical, engineering, scientific, or technical setting.
• At least one year of experience in agentic AI applications
• Strong communication skills
• Ability to collaborate effectively within a multidisciplinary and multicultural environment
• Curiosity, and a proactive, solution-oriented mindset
• Excitement to work in a dynamic and fast-paced environment, thrives in ambiguity
• Personal interest in exploring most recent AI developments and keeping up to state of the art

Technical skills:

• Proficiency in Python
• Understanding of fundamental computer science principles
• Proficiency in agentic and deep learning frameworks
• Solid understanding of machine learning principles and architectures
• Fundamentals of statistics
• Hands-on experience with large language models
• Excellent research and analytical skills

Preferred Qualifications (Nice to Have):

• Proven excellence in relevant areas (e.g., awards, competition wins)
• Demonstrated curiosity and passion for AI (e.g., personal projects, outreach activities, hobby work) or proven contributions to open-source projects
• Proven ability to independently solve complex problems or lead challenging projects
• Academic or practical background in physics or other natural sciences
• Experience with good coding practices and software development standards
• Familiarity with recent AI pipelines and protocols e.g. MCP tools/servers

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.

Stellenbosch University, South Africa

The Department of Mathematical Sciences at Stellenbosch University (South Africa) has a 2-year postdoctoral position available in the area of machine learning for wildlife monitoring and conservation. The project will look at:

zero-shot capabilities of foundation models on challenging real-world datasets typical in African wildlife and environment monitoring (e.g., camera trap imagery);

few-shot learning and generative modelling to deal with these large, unlabelled, long-tailed, noisy image sets.

Applicants must have obtained a PhD degree within the last 4 years, in a field related to the project's themes. The fellowship must commence by 1 March 2026 (preferably sooner).

Applications and supporting documents can be submitted through this online form.

Applications close 15 December 2025.

Enquiries: Prof. Willie Brink (wbrink@sun.ac.za).

New York / Chicago

Flow Traders is committed to leveraging the most recent advances in machine learning, computer science, and AI to generate value in the financial markets. We are looking for Quantitative Researchers to join this challenge.

As a Quantitative Researcher at Flow Traders, you are an expert in mathematics and statistics. You are passionate about translating challenging problems into equations and models, and have the ability to optimize them using cutting-edge computational techniques. You collaborate with a global team of researchers and engineers to design, build, and optimize our next generation of models and trading strategies.

Are you at the top of your quantitative, modeling, and coding game, and excited by the prospect of demonstrating these skills in competitive live markets? Then this opportunity is for you.

Global: United States, Europe and Asia

At Citadel, our mission is to be the most successful investment team in the world. Quantitative Researchers play a key role in this mission by developing next-generation models and trading approaches for a range of investment strategies. You’ll get to challenge the impossible in quantitative research by applying sophisticated and complex statistical techniques to financial markets, some of the most complex data sets in the world. As an intern, you’ll get to challenge the impossible in research through an 11 week program that will allow you to collaborate and connect with senior team members. In addition, you’ll get the opportunity to network and socialize with peers throughout the internship. Our signature internship program takes place June through August. Occasionally, we can be flexible to other times of the year. You will be able to indicate your timing preference in the application.

Palo Alto, CA

Position Description: As a Software Engineer for the Optimus team, you will build the tools and infrastructure to make and measure improvements to neural network architecture, visualize data, assist with exporting and deploying neural networks to the bot, and evaluate experimental results. You will help us automate the entire workflows of training, validation, and production of the Optimus. Most importantly, you will see your work repeatedly shipped to and utilized by thousands of Humanoid Robots in real world applications.

Responsibilities: Build and improve our Python training infrastructure for stable and faster training

Build the tooling and infrastructure for reporting and visualizing model metrics and performance

Build the pipelines to run and validate our PyTorch models

Manage, analyze, and visualize our training and test datasets

Coordinate with the team managing the hardware cluster to maintain high availability / jobs throughput for Machine Learning

Build and improve tooling to deploy trained neural nets to Tesla hardware

Requirements: Practical experience programming in Python and/or C++

Proficient in system-level software, particularly hardware-software interactions and resource utilization

Understanding of modern machine learning concepts and state of the art deep learning

Experience working with training frameworks, ideally PyTorch

Demonstrated experience scaling neural network training jobs across clusters of GPU’s

Optional: Previous experience in deep learning deployment

Optional: Profiling and optimizing CPU-GPU interactions (pipelining compute/transfers, etc)