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R&D AI Software Engineer / End-to-End Machine Learning Engineer / RAG and LLM

About Pathway

Pathway is an enabler for Live AI, allowing organizations to run contextualized ML models connected to ever-changing enterprise data. In addition to being an infrastructure provider delivering an AI framework, we are working to advance the state-of-the-art.

Pathway is VC-funded, with some amazing BAs (such as Lukasz Kaiser, co-inventer of Transformers). Pathway's CTO has co-authored papers with Goeff Hinton and Yoshua Bengio. We have just raised a $10M+ seed, with exciting developments ahead. The management team includes growth leaders who have scaled companies with multiple exits, and who have built online communities reaching millions of users.

Out client portfolio is focused around mobility, IoT data, logs and transactions, and also includes larger actors such as NATO and national postal services. We have a vibrant community centered around our developer frameworks, with almost 10,000 stars on GitHub: https://github.com/pathwaycom/

Our European offices are in Paris, France and Wroclaw, Poland. Our HQ is in Menlo Park, CA.


The Opportunity

We are currently searching for 2 ML/AI Engineers with a solid software engineering backbone who are able to prototype, evaluate, improve, and productionize end-to-end Machine Learning projects with enterprise data.

For representative examples of the type of projects you would be expected to create or deliver, see our AI pipelines templates.

If you would consider it would be fun to create a hybrid Vector/Graph index that beats the state of the art on a RAG benchmark, to deliver a working AI pipeline to a client in a critical industry, or to pre-process datasets in a way which would boost LLM accuracy in inference & training - this is the job for you!

You Will

  • help design experimental end-to-end ML/AI pipelines
  • contribute to addressing new use cases, beyond state of the art
  • improve/adapt AI pipelines for production, working directly with client data (often live data streams)
  • invent ways to pre-process data sources and perform tweaks (reranking, model paramater configuration...) for optimal performance of AI pipelines.
  • design benchmark tasks and perform experiments.
  • build unit tests and implement model monitoring.
  • contribute high-quality production code to our developer frameworks, used by thousands of developers.
  • help to pre-process data sets for LLM training.

The results of your work will play a crucial role in the success of both our developer offering and client delivery.

Cover letter

It's always a pleasure to say hi! If you could leave us 2-3 lines, we'd really appreciate that.

You Are

  • A graduate of a 4+-year university degree in Computer Science, where you have received A-grades in both foundational courses (Algorithms, Computational Complexity, Graph Theory,...) and Machine Learning courses.
  • Passionate about delivering high-quality code and solutions that work.
  • Good with data & engineering innovation in practice - you know how to put things together so that they don't blow up.
  • Experienced at hands-on Machine Learning / Data Science work in the Python stack (notebooks, etc.).
  • Experienced with model monitoring, git, build systems, and CI/CD.
  • Respectful of others
  • Curious of new technology - an avid reader of HN, arXiv feeds, AI digests, ...
  • Fluent in English

Bonus Points

  • Successful track-record in algorithms (ICPC / IOI), data science contests (Kaggle), or a HuggingFace leaderboard.
  • Showing a code portfolio.
  • PhD in Computer Science.
  • Authoring a paper at major Machine Learning conference.
  • You like playing/tinkering with new tools in the LLM stack.
  • You are already a part of the Pathway community, or have been recognized for your work in one of our bootcamps.

Why You Should Apply

  • Join an intellectually stimulating work environment.
  • Be a technology innovator that makes a difference: your code gets delivered to a community of thousands of developers, and to clients processing billions of records of data.
  • Be part of one of an early-stage AI startup that believes in impactful research and foundational changes.
  • Type of contract: Full-time, permanent
  • Preferable joining date: January 2025. The positions are open until filled – please apply immediately.
  • Compensation: competitive base salary (80th to 99th percentile) based on profile and location + Employee Stock Option Plan + possible bonuses if working on client projects. The stated lower band of EUR 72k/ USD 75k for the salary base concerns senior candidates; mid-senior or mid candidates may be considered with adapted salary bands.
  • Location: Remote work. Possibility to work or meet with other team members in one of our offices: Paris, France, or Wroclaw, Poland. Possibility to visit our Menlo Park, CA headquarters for several months. As a general rule, permanent residence will be required in the EU, UK, US, or Canada.
    (Note for candidates based in India: We are proud to be part of the current Inter-IIT as well as a partner of ICPC India. Top IIT/IIIT graduates are more than welcome to apply regardless of current location.)

If you meet our broad requirements but are missing some experience, don’t hesitate to reach out to us.

Average salary estimate

$87500 / YEARLY (est.)
min
max
$75000K
$100000K

If an employer mentions a salary or salary range on their job, we display it as an "Employer Estimate". If a job has no salary data, Rise displays an estimate if available.

What You Should Know About R&D AI Software Engineer / End-to-End Machine Learning Engineer / RAG and LLM, Pathway

At Pathway, we’re on the cutting edge of Live AI, and we’re looking for talented R&D AI Software Engineers to join our innovative team. As a pivotal part of our company, you’ll have the chance to work on exciting end-to-end machine learning projects that leverage existing enterprise data to create exceptional value for our clients. You’ll be prototyping and improving AI pipelines, addressing diverse use cases, and utilizing your strong programming background to implement high-quality production code. With Pathway’s stellar reputation and support from industry pioneers like Lukasz Kaiser and Geoff Hinton, you’ll be part of something truly groundbreaking. If you’re passionate about machine learning and wish to develop your skills further in a stimulating, supportive environment, this role is perfect for you! Imagine creating a state-of-the-art hybrid Vector/Graph index or pre-processing datasets to enhance the accuracy of large language models – this is your chance to make a significant impact. The work you do at Pathway will not only benefit our esteemed client portfolio, which includes major players like NATO and national postal services, but also contribute to a vibrant community centered around our developer frameworks. Whether you’re based in our European offices in Paris or Wroclaw, or prefer the flexibility of remote work, we’re excited to have you join us on this journey of technological innovation and research excellence.

Frequently Asked Questions (FAQs) for R&D AI Software Engineer / End-to-End Machine Learning Engineer / RAG and LLM Role at Pathway
What are the responsibilities of an R&D AI Software Engineer at Pathway?

As an R&D AI Software Engineer at Pathway, you will be responsible for designing and improving end-to-end machine learning pipelines, specifically tailored to handle enterprise data. You will work with live data streams, develop experimental pipelines, and implement processing techniques for optimal AI performance. Additionally, you'll contribute to creating benchmark tasks, unit tests, and maintaining model monitoring.

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What qualifications are required for the R&D AI Software Engineer position at Pathway?

The ideal candidate for the R&D AI Software Engineer role at Pathway should hold a degree in Computer Science and have strong academic achievements in both foundational and machine learning courses. Practical experience in machine learning within the Python stack is also essential. We value candidates who are detail-oriented, innovative, and proactive in their approach to challenges.

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What type of projects can I expect to work on as an R&D AI Software Engineer at Pathway?

In the position of R&D AI Software Engineer at Pathway, you’ll engage in a variety of projects, such as building AI pipelines to enhance client data usage, creating hybrid vector/graph indexing systems, and developing methodologies that improve dataset processing for large language models. These projects will push the boundaries of what AI can achieve in enterprise settings.

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What is the work environment like for R&D AI Software Engineers at Pathway?

At Pathway, the work environment is intellectually stimulating and innovative. You’ll collaborate with a dynamic team committed to groundbreaking advancements in AI technology, with enthusiasm for experimenting and exploring new tools and methods. The flexible structure allows for remote work, while also providing opportunities to meet and engage with colleagues in our European offices or Menlo Park HQ.

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What opportunities for growth does Pathway offer to R&D AI Software Engineers?

Pathway is committed to the professional growth of its team members, offering a vibrant community and opportunities to engage with thought leaders in AI. You will have the chance to enhance your skills through hands-on projects, collaboration with industry experts, and potential involvement in influential research. The exposure to cutting-edge technologies will further advance your career in machine learning.

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Common Interview Questions for R&D AI Software Engineer / End-to-End Machine Learning Engineer / RAG and LLM
Can you describe an end-to-end machine learning project you've worked on?

When discussing a previous project, be sure to outline the problem you were solving, the data you used, the specific techniques you implemented, and the overall outcomes. This showcases your practical experience and your ability to manage all phases of a project.

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What is your approach to preprocessing data for machine learning?

Explain your methodology for data preprocessing, including techniques like normalization, handling missing values, and feature extraction. Highlight any advanced techniques you’ve implemented to improve model accuracy, showing your deep understanding of machine learning workflows.

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How do you handle model monitoring and validation?

Discuss specific tools or frameworks you've used for model monitoring, and detail how you ensure the integrity and accuracy of deployed models. Providing examples of metrics you track and methods for validation will demonstrate your expertise in maintaining machine learning systems.

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What programming languages and tools do you prefer for machine learning projects?

Talk about your proficiency in languages like Python and any libraries or frameworks such as TensorFlow, PyTorch, or scikit-learn. Mention your familiarity with CI/CD pipelines, version control systems like Git, and how you integrate these tools into your workflow.

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Describe a challenging problem you faced while working on a machine learning project and how you overcame it.

Use the STAR (Situation, Task, Action, Result) method to detail a specific instance, focusing on your analytical skills, creativity, and perseverance. This not only highlights your problem-solving abilities but also shows your resilience in technical environments.

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How do you keep up with the latest advancements in AI and machine learning?

Share your habits around reading research papers, attending conferences, or following industry leaders. Mention specific sources like arXiv or Machine Learning conferences, as this demonstrates your commitment to staying informed and continuously learning in the field.

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What are the key factors for creating effective ML pipelines?

Discuss essential components such as data quality, feature engineering, model selection, and evaluation metrics. Emphasize the iterative nature of pipeline development and how testing and validation play a critical role in optimizing performance.

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Can you explain the concept of transfer learning in machine learning?

Articulate transfer learning and give examples of how it is applied in real-world scenarios, particularly in NLP or computer vision. Discuss how it can save time and resources while also improving model accuracy, illustrating your understanding of advanced machine learning techniques.

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What challenges do you foresee in working with live data streams?

Identify potential issues such as data latency, consistency, and the need for real-time processing. Discuss strategies for addressing these challenges, showing your foresight and ability to adapt to the fast-paced demands of the industry.

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How do you approach collaboration in a software engineering environment?

Highlight your collaborative approach, such as participating in code reviews, pair programming, and clear communication. Discuss how you value feedback and diverse perspectives, which are critical for achieving project goals within a team setting.

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Founded in 2009, Pathway Genomics is a privately held global precision medical diagnostic company with mobile applications. The company offers genetic testing and Artificial Intelligence to physicians for their patients to support the treatment of...

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