NeurIPS 2022 Career Website

Here we highlight career opportunities submitted by our Exhibitors, and other top industry, academic, and non-profit leaders. We would like to thank each of our exhibitors for supporting NeurIPS 2022. Opportunities can be sorted by job category, location, and filtered by any other field using the search box. For information on how to post an opportunity, please visit the help page, linked in the navigation bar above.

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As an applied machine learning researcher, you will develop cutting edge ML approaches to analyze and integrate large-scale biological, human, or chemical datasets. You will work with colleagues across different domains of ML and contribute models, methods, algorithms, analysis techniques, and code towards improving the Insitro platform in concrete, measurable ways and to push the field forwards with IP and publications. You will develop techniques and infrastructure for relevant problems in the drug discovery setting inspired by Insitro’s needs under various real world conditions, such as distribution shift, data missingness, small sample sizes, decision making, and many other problems. You will also work closely with a cross-functional team of life scientists, statistical geneticists, bioengineers, computer vision scientists, genomics scientists, medical scientists, and software engineers to integrate human-level data with our high-throughput in-house in vitro genomic and phenotypic data, with the goal of identifying therapeutic targets and developing drugs that have high efficacy and low toxicity.

You will be joining a vibrant biotech startup that has long-term stability, due to significant funding, and is in a high growth phase. A lot can change in this early and exciting phase, providing many opportunities for significant impact. You will work closely with a very talented team, learn a broad range of skills, and help shape insitro’s culture, strategic direction, and outcomes. Join us, and help make a difference to patients!

This role is preferably based in San Francisco Bay Area or Boston, but we are open to discussing other locations in the United States and the UK.

About You - Ph.D. in machine learning, computer science, or a related discipline, or equivalent practical experience (e.g., a Masters degree plus 2 years in relevant industry experience); - Demonstrated ability to use and develop cutting edge statistical and machine learning methods inspired by real problems; - Experience in modern representation learning topics such as self-supervised learning, transfer learning, multi-modal modeling, few-shot learning, robustness and interpretability, uncertainty estimation, and more; - Experience in probabilistic modeling and/or causal inference; - Experience using modern deep learning frameworks (PyTorch, Jax, etc); - Proficiency in Python; - Ability to communicate effectively and collaborate with people of diverse backgrounds and job functions; - Passion for making a difference in the world.

Nice to Have - Publication record in venues such as NeurIPS, ICML, ICLR, AISTATS, AAAI, and related conferences/journals in the sciences; - Hands-on experience working with biomedical or other real world datasets; - Experience with probabilistic programming; - Familiarity with cloud computing services (e.g., AWS or GCP); - Proficiency in scientific engineering and modern engineering practices;


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We are seeking a passionate and talented Postdoctoral Researcher to advance the field of applied machine learning in the context of human genetic data. This individual will conceive and execute analyses clarifying the application of natural language processing models to diverse aspects of early biological target discovery and evaluation while embedded within a collaborative academic/biotech setting. Using large-scale transformer neural networks to model the effects of protein coding variation in the human genome, the successful candidate will lead the development of novel methodology to characterize disease relevance and lay the foundations for an ultra-high throughput experimental framework.

This position is supported by the Maze Advanced Analytics Fellowship Program, which provides funding and scientific engagement to scholars interested in applied research bridging academic and industrial applications. The candidate will be jointly supervised by Dr. Vasilis Ntranos at UCSF and the Data Sciences group at Maze Therapeutics, and will work as part of a cross-functional team comprised of Data Scientists, Functional Genomic Scientists, Human Geneticists, and Computational Chemists to facilitate highly multidisciplinary early discovery research.

QUALIFICATIONS:

  • Ph.D. in Computer Science, Bioinformatics, Mathematics/Statistics, or similar disciplines
  • Strong critical thinking, experimental design, programming, and data analysis skills
  • Knowledge of common machine learning algorithms and methods for analyzing high-dimensional data
  • Familiarity with basic machine learning workflows and computational modeling applications is required; expertise strongly preferred
  • Experience working with modern neural network architectures such as transformers, autoencoders, and/or attention models strongly preferred
  • Familiarity with state-of-the-art machine learning approaches applied to biological sequence data (e.g., in the context of protein prediction tasks) is strongly preferred
  • Enthusiasm for cross-functional collaboration in a highly multidisciplinary intellectual environment
  • Excellent communication, presentation, collaboration, and organizational skills
  • Experience with computational chemistry, computational biology, and/or cloud computing applications strongly preferred

Location San Francisco, CA


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The Foundations of Data Science Institute (FODSI), funded by the National Science Foundation TRIPODS program, is announcing a competitive postdoctoral fellowship. FODSI is a collaboration between UC Berkeley and MIT, partnering with Boston University, Northeastern University, Harvard University, Howard University and Bryn Mawr College. It provides a structured environment for exploring interdisciplinary research in foundations of Data Science spanning Mathematics, Statistics, Theoretical Computer Science and other fields.

We are looking for multiple postdoctoral team members who will collaborate with FODSI researchers at one or more of the participating institutions. These positions emphasize strong mentorship, flexibility, and breadth of collaboration opportunities with other team members -- senior and junior faculty, postdocs, and graduate students at various nodes around the country. Candidates are welcome to contact potential FODSI mentors to identify common interests. Furthermore, postdoctoral fellows will be able to participate in workshops and other activities organized by FODSI.

The fellowship is a one-year full-time appointment, with the possibility of renewal for a second year (based upon mutual agreement) either at the same or at another FODSI institution. The start date is flexible, although most appointments are expected to start in summer 2023. Candidates are encouraged to apply to work with more than one faculty mentor at one or more participating institutions (in-person mentoring is preferred, but remote options will be also considered). The applicants should have an excellent theoretical background and a doctorate in a related field, including Mathematics, Statistics, Computer Science, Electrical Engineering or Economics. We particularly encourage applications from women and minority candidates.

The candidates should apply by December 15 for full consideration.


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Independent Researcher (Generally Intelligent) United States

Remote


Summary

At Generally Intelligent, our remote team is highly independent. This role is about conducting research independently into subjects that are of particular interest to us. Your sole responsibility will be to advance the frontier of human knowledge in a particular area that you are excited about.

Please note: this is an extremely challenging role for which we will hire quite selectively. This role requires both significant prior research experience, as well as comfort and familiarity with working remotely. It also requires a significant overlap between the subjects in which you are interested and the types of questions that we currently want to investigate.

Example research questions

• What are large transformers fundamentally doing? How can we describe their patterns? Could we create these patterns much more efficiently? • How can latent causal factors of the world be discovered in a mostly self-supervised fashion? How can we create models that find information that is more closely correlated with the underlying generative models of the world? How can that information help make agents that learn more quickly and are more robust to distributional shifts? • How can we create smaller, more efficient versions of very large models? Using retrieval, hard visual attention, and other similar techniques, can we create models with effectively the same performance, yet at ≤ 1% of the computational cost? • How can we conduct network architecture search in a practical, feasible sense, without having to define some super restricted space of architectures? What is the right formulation of the search space of architectures?

You are

• Driven, self-motivated, and passionate about your research. We want people who are driven to answer scientific questions. • An effective, independent researcher. Because the role is remote, you should be comfortable working relatively alone and independently. • A very capable software engineer. You will need to implement and run your own experiments, so you should be very comfortable writing complex, bug-free machine learning code. • A good communicator. You will need to communicate your results and questions to both the rest of the team and the broader world via papers and blog posts, both of which require an ability to explain complex ideas in an easy-to-understand manner.

Benefits

• Work directly on creating software with human-like intelligence. • Generous compensation, equity, and benefits. • $20K+ yearly budget for self-improvement: coaching, conferences, etc. • Actively co-create and participate in a positive, intentional team culture. • Spend time learning, reading papers, and deeply understanding prior work.

About us

We started Generally Intelligent because we believe that software with human-level intelligence will have a transformative impact on the world. We’re dedicated to ensuring that that impact is a positive one.


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Research Scientist (Generally Intelligent) United States

San Francisco


Summary

This role is about investigating the fundamental questions of intelligence, knowledge and understanding in order to develop software with human level intelligence. You will collaborate internally and externally with other researchers, and be supported by a team of research engineers.

Research areas of interest • Unsupervised RL • Self-supervised learning • Multi-task RL • Meta-learning • Continual learning • Deep learning theory • Generalization • Human-like learning • Network architecture search • etc.

You are

• A highly accomplished machine learning researcher (e.g., a track record of high quality papers at top conferences like NeurIPS, ICML, ICLR, etc., or equivalent accomplishments in industry). • Able to create research questions that clarify the nature of the problem being solved, and coordinate a research program to successfully answer those questions. • Extremely comfortable running ML experiments. • Able to clearly communicate about your ideas and intuitions. • Excited to mentor a small team of great research engineers.

Benefits

• Work directly on answering the fundamental questions of intelligence, learning, and knowledge, free from politics and pressures to publish or commercialize your research. • Generous compensation, equity, and benefits. • Actively co-create and participate in a positive, intentional team culture. • Frequent team events, dinners, off-sites, and hanging out. • $20K+ yearly budget for self-improvement: coaching, courses, conferences, etc.

About us

We started Generally Intelligent because we believe that software with human-level intelligence will have a transformative impact on the world. We’re dedicated to ensuring that that impact is a positive one.

We have enough funding to freely pursue our research goals over the next decade, and our backers include Y Combinator, researchers from OpenAI, Astera Institute, and a number of private individuals who care about effective altruism and scientific research.

Our research is focused primarily on self-supervised and generative video and audio models. We’re excited about opportunities to use simulated data, network architecture search, and good theoretical understanding of deep learning to make progress on these problems. We take a focused, engineering-driven approach to research.


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Owkin is a French-American startup that uses artificial intelligence to find the right treatment for every patient. Our focus is to use AI to discover and develop better treatments for unmet medical needs, starting with the fight against cancer.

We identify new drug candidates, de-risk and accelerate clinical trials and build diagnostic solutions that improve patient outcomes. Using federated learning, a pioneering collaborative AI framework, we enable medical and biopharma partners to unlock valuable insights from siloed datasets, while protecting patient privacy and securing proprietary data.

Owkin we co-founded by Thomas Clozel MD, a clinical research doctor and former assistant professor in clinical onco-hematology, and Gilles Wainrib, a pioneer in the field of machine learning in biology.

In 2021, Owkin became a ‘unicorn’: we have raised over $300 million through investments from leading biopharma companies (Sanofi and BMS) and venture funds (Fidelity, GV and BPI, among others).

We offer research, applied & project management career opportunities for current students, fresh graduates (PhD), as well as experienced professionals in the field of machine learning, computational biology, data science and software engineering.

We invite you to visit our careers website to view all current openings and/or reach out to meet with our recruiter Gabi Korput during NeurIPS at (gabi.korput@owkin.com)(mailto:gabi.korput@owkin.com).


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Vienna, Austria


The Institute of Advanced Research on Artificial Intelligence (IARAI) invites applicants for a postdoctoral research position in the domain of computer vision and machine learning, focusing on learning visual representations under different settings. Topics of interest include:

  • continual learning
  • metric learning and instance-level retrieval
  • self-supervised, semi-supervised and few-shot learning
  • localization/grounding, alignment, and interpretability
  • multimodal learning, vision-and-language tasks.

The postdoc will work with Principal Investigator Yannis Avrithis (https://avrithis.net/). The preferred starting date is the beginning of 2023. The duration is two years, with potential for extension. The position is full-time and fully funded with a generous remuneration package.


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San Francisco


Summary

In this role you’ll work with our researchers to do cutting-edge deep learning research—conducting experiments, creating infrastructure, and developing tooling & visualizations—with the goal of developing more human-like machine intelligence.

Note: This research role requires being onsite in San Francisco. If you're remote, please take a look at our Machine Learning Engineer (Remote) role, which is more engineering-heavy.

Example projects

• Implement a self-supervised network using contrastive and reconstruction losses. • Create a library on top of PyTorch to enable efficient network architecture search. • Implement networks from newly published papers. • Run massively parallel experiments to understand all variants of an architecture. • Develop more realistic simulations for training our agents. • Create visualizations to help us deeply understand what our networks learn and why.

You are

• Passionate about understanding the fundamentals of intelligence. • Very comfortable writing Python. • Excited to be a world-class ML engineer. • A fan of pair programming. • Passionate about engineering best practices.

Benefits

• Work directly on creating software with human-like intelligence. • Generous compensation, equity, and benefits. • $20K+ yearly budget for self-improvement: coaching, courses, conferences, etc. • Actively co-create and participate in a positive, intentional team culture. • Spend time learning, reading papers, and deeply understanding prior work. • Frequent team events, dinners, off-sites, and hanging out.

About us

We started Generally Intelligent because we believe that software with human-level intelligence will have a transformative impact on the world. We’re dedicated to ensuring that that impact is a positive one.

We have enough funding to freely pursue our research goals over the next decade, and our backers include Y Combinator, researchers from OpenAI, Astera Institute, and a number of private individuals who care about effective altruism and scientific research.

Our research is focused primarily on self-supervised and generative video and audio models. We’re excited about opportunities to use simulated data, network architecture search, and good theoretical understanding of deep learning to make progress on these problems. We take a focused, engineering-driven approach to research.


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Machine Learning Engineer (Generally Intelligent) United States

Remote


Summary

As a remote machine learning engineer, you’ll work very closely with a senior member of our research team on cutting-edge deep learning research, infrastructure, and tooling towards the goal of creating general human-like machine intelligence.

Example projects

• Implement a self-supervised network using contrastive and reconstruction losses. • Create a library on top of PyTorch to enable efficient network architecture search. • Open source internal tools. • Implement networks from newly published papers. • Work on tools for simple distributed parallel training of deep neural networks. • Develop more realistic simulations for training our agents. • Design automated methods and tools to prevent common issues with neural network training (e.g. overfitting, vanishing gradients, dead ReLUs, etc). • Create visualizations to help us deeply understand what our networks learn and why.

You are

• Very comfortable writing Python. • Familiar with PyTorch and training deep neural networks. • Excited to work on open source code. • Passionate about engineering best practices. • Self-directed and independent. • Excellent at getting things done.

Benefits

• Work directly on creating software with human-like intelligence • Very generous compensation • Flexible working hours • Work remotely • Time and budget for learning and self-improvement

About us

We started Generally Intelligent because we believe that software with human-level intelligence will have a transformative impact on the world. We’re dedicated to ensuring that that impact is a positive one.

We have enough funding to freely pursue our research goals over the next decade, and our backers include Y Combinator, researchers from OpenAI, Astera Institute, and a number of private individuals who care about effective altruism and scientific research.

Our research is focused primarily on self-supervised and generative video and audio models. We’re excited about opportunities to use simulated data, network architecture search, and good theoretical understanding of deep learning to make progress on these problems. We take a focused, engineering-driven approach to research.


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Location Fully Remote (global)


Description

Design and build a novel machine learning verification framework utilising all levels of the software (& hardware) stack

This is a rare point-in-time opportunity: to work on one of the world’s most important technology problems while upending the established political and corporate interests that control and price gouge it. Gensyn will allow machine learning engineers and researchers to train models at a higher scale, and lower cost, than AWS; achieved via a highly specialised deep learning compute protocol with minimal verification overhead (read more in our Lite Paper).

🛠 Written in Rust and Python: a trustless protocol that rolls up work from off-chain ML runtimes into a Substrate blockchain for decentralised consensus

🧭 Autonomous environment: fully remote, flat hierarchy, low/no rules: just pure focus on delivering the compute protocol that will push the frontiers of artificial intelligence

Responsibilities

👉 Research novel ML verification - work with determinism, non-determinism, game theory, optimisation theory, and more to design low-redundancy ML verification mechanisms 👉 Work with cutting-edge ML frameworks - fork and modify existing ML frameworks and low-level libraries (e.g. CUDA, OpenCL) to build a new way of performing ML computations 👉 Build the offchain runtime - implement newly proposed techniques for the above in constrained environments in production code for use by ML researchers, engineers, and academics globally 👉 Write - co-author technical reports / conference papers describing the system and discuss with the community

Minimum requirements

Experience with lower level compiler / toolchain tech - Have worked with intermediate representations, ideally focussed on machine learning and graph-based computations ✅ Deep understanding of computational bottlenecks - experience working with low-level computer science principles (e.g. determinism vs non-determinism, floating point implementations, efficient data structures etc..) ✅ Passion for decentralisation - an *understanding of web3 technologies and decentralised principles ✅ *Low-level HW experience - Have worked directly on/with ML hardware (GPUs, TPUs, IPUs, etc..)

Nice to haves

🔥 Rust experience 🔥 Cryptography experience 🔥 Background in computer science research

Compensation

💰 Competitive salary + share of equity and token pool 🌐 Fully remote work 🛫 Quarterly, all expenses paid, company meet-ups around the world (Mexico is next) ⭐ 28 paid holiday days per year 💻 Whatever equipment you need ❤️ Paid sick leave

Apply

Send an email to founders@gensyn.ai


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Location Fully Remote (global)


Description

Determine the most cost-efficient and high performance way to distribute ML model training

This is a rare point-in-time opportunity: to work on one of the world’s most important technology problems while upending the established political and corporate interests that control and price gouge it. Gensyn will allow machine learning engineers and researchers to train models at a higher scale, and lower cost, than AWS; achieved via a highly specialised deep learning compute protocol with minimal verification overhead (read more in our Lite Paper).

🛠 Written in Rust and Python: a trustless protocol that rolls up work from off-chain ML runtimes into a Substrate blockchain for decentralised consensus

🧭 Autonomous environment: fully remote, flat hierarchy, low/no rules: just pure focus on delivering the compute protocol that will push the frontiers of artificial intelligence

Responsibilities

👉 Research novel ML distribution methods - theorise, *design, test, build, and iterate on novel distributed machine learning methods (e.g. Distributed-SGD and Decentralised Mixture of Experts (DMoE)) 👉 *Overcome bandwidth, latency, and data constraints - deeply understand typical distributed training bottlenecks in both hardware and software and work around them in novel ways 👉 Monitor and evaluate distributed training performance - design and perform representative experiments for distributed model training over heterogeneous infrastructure 👉 Build the offchain runtime - implement novel distributed ML methods in production code for use by ML researchers and engineers globally 👉 Write - contribute to technical reports / papers describing the system and discuss with the community

Minimum requirements

Experience with highly distributed model training - have previously built training pipelines using data and model parallelism over distributed (ideally highly distributed) hardware ✅ Experience with huge model training - have previously been a core engineering member of a team training an LLM (e.g. BERT, GPT-X, PaLM, BLOOM, etc..) from scratch ✅ Passion for decentralisation - an ****understanding of web3 technologies and decentralised principles

Nice to haves

🔥 Rust experience 🔥 Publications in distributed ML/DL 🔥 Experience with Byzantine-tolerant distributed optimisation 🔥 Some knowledge of protocol design

Compensation

💰 Competitive salary + share of equity and token pool 🌐 Fully remote work 🛫 Quarterly, all expenses paid, company meet-ups around the world (Mexico is next) ⭐ 28 paid holiday days per year 💻 Whatever equipment you need ❤️ Paid sick leave

Apply

Send an email to founders@gensyn.ai


Apply

Germany


Dr. Tao Lin, TT Faculty at Westlake University, P.R.China, and Dr. Sebastian U. Stich, TT Faculty at CISPA, Germany, are looking for a postdoc.

Applicants with a Ph.D. degree and a strong academic track record in one or more of the following research topics are encouraged to apply: - optimization for deep learning, - distributed and federated optimization, - efficient/robust deep learning and inference.

The candidate will be affiliated with Westlake University (two-year contract) and will interact and collaborate closely with both faculty in a hybrid collaboration form, with the possibility to visit each group with a similar time percentage. He/she will work on foundational machine learning challenges and will lead projects. We offer a competitive salary.

Interested applicants please send application materials (e.g., CV, representative publications, contact of referees) to “lintao [at] westlake.edu.cn” and “stich [at] cispa.de”.


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