ML in PL

ML in PL Association is a non-profit organization devoted to fostering the machine learning community in Poland and promoting a deep understanding of ML methods

[ML in PL @MLinPL]

Are you working at the intersection of AI and healthcare?
AIMed Global Forum 2027 is inviting researchers, clinicians, healthcare professionals, data scientists and innovators to share their work, ideas and evidence with an international community working across AI and medicine.
The Call for Abstracts is now open, with four tracks covering:
🔬 Clinical Translation
💻 Methods & Development
⚖️ Regulatory & Translational Governance
🧑‍⚕️ Ethics & Patient Perspectives
Whether you are developing new methods, exploring real-world implementation, or examining the regulatory, ethical or clinical dimensions of AI in healthcare, we encourage you to submit your work.
📅 Deadline: 12 January 2027
📍 Kraków, Poland | 6–8 May 2027
👉 Submit your abstract: https://t.co/Lubyv1UDh5
#AIMed2027 #AIMedGlobalForum #AIinHealthcare #DigitalHealth

Linkedin: @AIMed Global Forum https://t.co/y5iuqI3kDt
Website: https://t.co/hU2UsI1tnO

[ML in PL @MLinPL]

Mistral joined us as a Platinum Sponsor at ML in PL Conference 2026 — and they're hiring.

If you're interested in working on sovereign AI, private deployment, or the models and infrastructure behind it, their open roles are worth a look.

Warsaw positions: https://t.co/jDM8HiYbIi (Warsaw filter)
Research roles: https://t.co/jDM8HiYbIi (Science department)

Worth a look while the conversations from the conference are still fresh.

[ML in PL @MLinPL]

"Driven by AI" is not just a slogan, but a core pillar of @Comarch's transformation.
Would you like to be a part of this change?
Explore the job opportunities below:

QA Engineer (ML & Agentic AI)
https://t.co/4tNrKGyJs1

Head of AI (Tech & Business Transformation)
https://t.co/zqtSMseoM6

Head of AI & Product
https://t.co/QLjeUrmls3

[ML in PL @MLinPL]

Warsaw AI Safety Day runs two parallel tracks, and the governance one is where some of the harder questions of the day are sitting.

How does a middle power go from rule-taker to rule-shaper when the models are trained elsewhere? And what do we mean by “loss of control”: frontier risk, or European sovereignty? They’re easy to blur and not the same problem. Later come talks on how anyone outside a lab verifies what the lab claims, what defence AI looks like on NATO’s eastern flank, and whether existential risk is a real problem right now. The day closes with a panel in Polish on the less obvious risks of AI development, and the first enforcement of the EU AI Act gets its own slot in the shared programme.

The technical track is just as concrete: why RL training of frontier LLMs fails, how a model’s own values silently shape its answers, what makes an alignment eval worth building, plus biosecurity, robotics safety, and why monitoring has to go beyond the chain-of-thought. Tracks are picked per time block, so you can split your day.

24 October, University of Warsaw, 99 PLN (free tickets on request). We’re a strategic partner, and if you’d like a seat in either room, registration is open at https://t.co/TmxX6vH1E1

[ML in PL @MLinPL]

@RTBHouse Careers offers: https://t.co/AfCD1V7E29

[ML in PL @MLinPL]

Tens of billions of real-time ad auctions a day, each one scored inside a sub-50ms latency budget. That's the environment the ML Research team at @RTBHouse works in, and they're looking for a Senior Machine Learning Researcher to join them in Warsaw.

The core challenge, in their words: moving beyond static feature embeddings to model user behavior as continuous, long-term event trajectories. The role involves researching and designing new deep learning architectures, including their Transformer-based Giga Model family, for CTR, conversion rate, and user value estimation.

What the work actually looks like: formulating hypotheses, reading recent literature, and testing new model topologies and loss functions in PyTorch. Running distributed training experiments on dedicated multi-GPU clusters across terabyte-scale behavioral datasets. Working directly on models that go to production.

No corporate red tape, they say, with a preference for fast iteration, clean ablation studies, and reproducible research that moves measurable online performance.

RTB House was a Platinum Sponsor of ML in PL Conference 2026. Application details in the comments.

#MLinPL #MachineLearning #AI

[ML in PL @MLinPL]

Open positions: https://t.co/9u5EDyL2Dg

[ML in PL @MLinPL]

@ElevenLabs is hiring Research Engineers for its growing Warsaw office. Four roles are open, and each one is a different bound on how good their models can get.

The reasoning behind that setup, as their team puts it: research is the foundation. The advances they make in speech, emotion and latency reach users immediately, and what they learn about quality and edge cases feeds back into the models to refine them.

Research Engineer: training models across modalities, then pushing existing ones further through post-training, data curation, architectural improvements and new training paradigms. Plus the part that gets underrated, designing benchmarks and evaluations rigorous enough to tell you whether a new iteration is genuinely better, and why.

Research Engineer, Inference: model quality only counts if it can be served fast, reliably and at scale. You own the path from research checkpoint to serving infrastructure, including quantization, distillation, KV-cache optimization, batching strategies, custom kernels, and serving systems tuned for real-time streaming workloads where every millisecond is load-bearing.

Research Engineer, Data Infrastructure: large-scale pipelines for collecting, processing, filtering and transforming training datasets, the classifiers, quality filters and labeling models that run inside those pipelines, and curation strategies (deduplication, quality scoring, augmentation) that measurably improve model performance.

Research Engineer, Web Crawlers: sourcing that data from the open web, across billions of pages. Content extraction from messy HTML, deduplication at web scale, freshness and recrawl strategies, politeness and rate-limit handling, and targeted pipelines that find high-value audio, video and multilingual sources and turn them into clean, training-ready datasets.

No formal certifications or degrees required. They look for engineers who can show impressively hard problems solved through artifacts: past projects, designs, GitHub contributions.

ElevenLabs was a Strategic Sponsor of ML in PL Conference 2026. All four role descriptions in the comments.

#MLinPL #MachineLearning #AI

[ML in PL @MLinPL]

Found it? Now for the harder question: could an AI agent spot it, understand the surrounding code, fix it without breaking anything else, and do that reliably across a large codebase?

That's the challenge @jetbrains is hiring AI and ML researchers and engineers for.
JetBrains is redesigning developer tooling for a world where agents plan tasks, use tools, and operate across complex codebases. Its products include:
- Mellum, a family of fast, open-source language models for code intelligence and low-latency AI workloads
- Junie, a coding agent
- Air, a development environment where multiple coding agents run independent task loops side by side

The majority of the AI research and engineering behind these products happens in-house. The work includes training and post-training language models, optimizing inference, and developing evaluations that measure how reliably agents understand code, use tools, and complete real development tasks.
It also means tackling task routing and multi-agent orchestration: deciding which model or agent should handle a task, coordinating parallel work, and making the resulting systems dependable at scale.

Want to build these systems for 15 million developers and counting?
Explore JetBrains' AI and ML roles: https://t.co/8otNuiqfLQ

[ML in PL @MLinPL]

@tenstorrent joined us as a Platinum Sponsor of ML in PL Conference 2026. Now they're hiring in Poland.

The openings sit in three areas: model training, AI/ML customer engineering, and low-level systems programming. If you spent the conference talking about kernels and accelerators rather than benchmarks, one of those is probably for you.

The way the @Tenstorrent team puts it: "Tenstorrent is leading the industry on AI technology, revolutionizing performance expectations, ease of use, and cost efficiency. Teams can expect to work on problems everyday that are improving performance and helping real-world customers."

Their full list of open roles is at https://t.co/PrqrvOrK0R, under Warsaw.

#MLinPL #MachineLearning #AI

[ML in PL @MLinPL]

@Allegro_Group joined the 10th edition of ML in PL Conference as a Platinum Sponsor, and their engineers spent three days in Warsaw talking through what they actually build. A lot of those conversations ended with a version of the same question: are you hiring?

They are. Their tech and data teams have open roles right now, and this is how they describe the place:

At #AllegroTech, we create our own tech future. You'll quickly come to love the autonomy in our teams, the room to grow, the chance to try new things, and the freedom to tackle technical challenges the best way you know how. Join us and feel the drive!

#goodtobehere

All open positions in tech and data: https://t.co/uGJB4mF1PF

#MLinPL #MachineLearning #AI

[ML in PL @MLinPL]

The 10th edition of the ML in PL Conference is officially behind us! 💛

The dust has barely settled at the Copernicus Science Centre, the last tutorial sessions at the Faculty of Physics, University of Warsaw have wrapped up, and we're still trying to take it all in.

Ten editions ago, a group of students had a fairly simple idea: bring together people who shared a passion for machine learning.

A decade later, that idea has grown into something we could hardly have imagined back then. And honestly, we're still trying to wrap our heads around it.

This year's anniversary edition brought together:
- 820 participants from the ML community
- 24 sponsors, including 13 sponsor stands at the Copernicus Science Centre
- 83 posters presented across 3 poster sessions
- 60 talks, covering an incredible range of research topics
- A discussion panel, hands-on tutorials, and countless conversations

Looking back at the past decade, it's quite a journey.
We've had the privilege of welcoming researchers such as Yoshua Bengio, Daphne Koller, Leon Bottou, Kyunghyun Cho, Łukasz Kaiser, and many, many others who have helped shape machine learning as we know it. We've received support from some of the world's leading AI companies, welcomed brilliant minds from leading research institutions, and watched our little student initiative grow into an international scientific conference.

But perhaps the most incredible thing we've built over these ten editions isn't the conference itself.

It's the community. And that's something we're particularly proud of.

Of course, before we get too sentimental, we have some well-deserved congratulations to share!

🏆 Best Poster Awards 2026
Congratulations to:
- - Model-Aware Tokenizer Transfer
- Jolanta Śliwa - Counterfactual Explanations for Monster Design in Tabletop RPGs
- Piotr Kacprzak - Towards Discrete Multi-Scale Time Series Foundation Models

🏆 Best Talk Award 2026
Congratulations to Dawid Rymarczyk for Surgical Unlearning: Precise Concepts Removal from AI Models!

🎮 Conference Game Winners
Congratulations to Hanna Nowicka and the team from AGH University of Krakow: Mateusz Praski, , Piotr Kacprzak, and Michał Szymocha!

And now, the most important part.
To our speakers, sponsors, partners, organizers, and volunteers - thank you for your time, support, dedication, and for making this edition possible.

And above all, to our participants : thank you for being the heart of ML in PL. Whether it was your first edition or your tenth, you're the reason we keep doing this.

When we asked people for their birthday wishes this year, we kept hearing two things: keep growing, but please don't lose that special ML in PL atmosphere.
And you know what? We couldn't have wished for anything better ourselves.
What would you love to see at future editions of ML in PL? We'd genuinely love to hear your ideas.
Thank you, from the bottom of our hearts.
See you next year! 💛
ML in PL Team

[ML in PL @MLinPL]

Day 3 of ML in PL Conference 2026 brought three things we love about research: difficult questions, fresh ideas, and discussions that don't end when the slides do.

The final day at Warsaw's Copernicus Science Centre covered a wide range of research, from machine unlearning and interpretable representations to diffusion models, optimization theory, and AI-driven scientific discovery.

Dawid Rymarczyk and Franziska Boenisch explored concept removal in AI models, while Diogo Soares presented work on interpretable representation alignment. Mikołaj Zasada discussed SoftMoE for mixture-of-experts in LLMs, and Julianna Piskorz examined context comprehension in diffusion language models.

Patryk Marszałek presented ZEUS for unsupervised tabular data embeddings. Jonas Kneifl explored physical understanding in video diffusion models, Łukasz Janisiów presented PhAME for molecular editing, and Bartłomiej Sobieski discussed explanations of deep lung cancer risk predictions.

The invited talks brought wide perspective: Peter Richtárik on optimization theory, Tomáš Pluskal on machine learning for plant chemistry, João F. Henriques on synthetic and real data in 3D vision, Johannes von Oswald on retrieval-centric deep learning, and Stanisław Jastrzębski on AI-driven discovery in organic chemistry.

Different research questions, different approaches, and plenty to discuss after the presentations.

Thanks to all the speakers, participants, and volunteers who made these three days possible!

[Peter Richtarik @peter_richtarik]

Random photo of Warsaw, Poland.

[ML in PL @MLinPL]

@PyStok Do zobaczenia!

[ML in PL @MLinPL]

@PrzeBiec @cnkopernik @ccaiwut Congrats!

[ML in PL @MLinPL]

@paolomandica Glad to have you!

[ML in PL @MLinPL]

@ComarchGroup Do zobaczenia!

[ML in PL @MLinPL]

@jakub_bilski Awesome to have you here!

[ML in PL @MLinPL]

@AlisdairBroshar @PierreStock @MistralAI Heaviest weights at the conference, confirmed 🐈