• Build prototypes and demos across the product portfolio — serverless inference, databases, MLflow, MLOps, and vertical use cases in Physical AI and HCLS — that become assets for sales, product, and engineering teams
• Support new customers hands-on through POC design, technical onboarding, and validation; act as the bridge between their ML team and the platform during the critical first months
• Go deep on emerging applied AI — new training techniques, inference optimizations, agentic architectures, new frameworks — and turn findings into working prototypes, writeups, and product recommendations
• Feed the product roadmap with specific, grounded feedback; be the voice of "here's what broke in three customer POCs last month and here's what needs to change"
• Develop reusable technical assets — notebooks, reference architectures, benchmark results — that reduce onboarding friction at scale

• You've fine-tuned large models, debugged distributed training jobs, built production RAG or agentic pipelines, and optimized inference on GPU infrastructure — not just read about it
• You're fluent in the modern ML stack: PyTorch, HuggingFace, CUDA fundamentals, Kubernetes for ML, MLflow or equivalent, vector databases
• You've worked with enterprise ML teams — whether as a solutions engineer, customer engineer, or an ML engineer who collaborated closely with customers
• You read papers and implement them — not for credit, but because it's how you stay sharp
• You communicate with calibration: you can explain activation checkpointing tradeoffs to an ML engineer in the morning and the cost implication to a CTO in the afternoon

• Experience in any of our vertical domains: Physical AI / robotics / simulation, HCLS (drug discovery, medical imaging, clinical NLP), or enterprise AI application development
• Familiarity with MLOps at scale (Kubeflow, Metaflow, Argo, Ray)
• Prior work at a cloud provider or AI infrastructure company
• You've shared technical work publicly — notebooks, talks, blog posts that people actually use

• Competitive salary and comprehensive benefits package.
• Opportunities for professional growth within Nebius.
• Flexible working arrangements.
• A dynamic and collaborative work environment that values initiative and innovation.

In 2023, IMC acquired Altas Technologies, a rapidly growing algorithmic trading firm with the ambition of creating tomorrow’s most technically advanced trading stack. By combining Altas’ highly advanced trading strategies with IMC’s execution and scaling capabilities, this move strengthens IMC's position in the market and is an important step towards ensuring stability and long-term growth for the firm.

The Altas team builds robust, fully automated systems to predict and trade cryptocurrency markets.  As a technology-oriented and scientifically-minded group, we design and build our own cutting-edge systems, from high-performance trading platforms to distributed ML- and research infrastructure. You’ll join at a very exciting time in the growth of the team, and will be able to contribute to our success in scaling our crypto strategies. In this role you'll work together with an ambitious team of researchers and quant developers to design and implement HFT monetization frameworks end-to-end and from the ground up. 

Your Core Responsibilities

As a Quantitative Researcher, you will be operating as a senior and core member of our high-performance, dedicated, multi-disciplinary team, with a background in Mathematics, Physics, Computer Science, Robotics, Data Science, Engineering, Statistics. The one thing we all have in common, regardless of our individual backgrounds, is a creative and scientific mindset.

You will not get a lot of top-down instruction telling you exactly what to do and when to do it. You'll get directional advice, useful frameworks, access to interesting challenges, and plenty of freedom to execute as you see fit. We are meritocratic by nature and believe that empowering talent in our organization is the only way to achieve our highly ambitious goals.

We are looking for a creative scientifically minded individual who can lead the growth of the firm’s trading research to improve execution quality in delta 1 mid-frequency (intraday) trading context. As a Quantitative Researcher, you will work together with the engineering team to implement and test execution improvements, and the research team to improve the strategies' capacity and profitability from the perspective of trading costs.

• You will have full ownership of the design and development of models and algorithms responsible for trading and optimal execution of billions in monthly volume
• You will work together with peer researchers and quant developers to design and improve HFT monetization frameworks end-to-end and from the ground up. This includes HFT alpha research, market impact modeling, ML modeling, execution benchmarking, and performance profiling
• Analyze large orders- and trades datasets for post-trade analytics, and improve monitoring capabilities to ensure we are always on top of the market, decomposing PnL and identifying blind spots / opportunities in the algorithms.

Your Skills and Experience

• An advanced degree with excellent grades in Mathematics, Physics, Computer Science, Data Science, Engineering, Statistics or any other highly quantitative field
• Experience as a Researcher in (high to mid-frequency) D1 execution research, ideating trades- and book features that are predictive of execution costs and short-term price movements.
• Demonstrable experience and interest in crypto markets is highly preferred.
• Significant practical experience with large messy datasets.
• Understanding of position management, market risk, and the optimal execution side of quant strategies (implementation shortfall, trade cost modelling).
• Scientific mindset with experience in data analysis with Python & any SQL dialect. Strong eye for detail and highly structured, using a rigorous process to ensure results are reliable and well tested
• Serious about clean code, simple but well-architected systems, and continuous improvement
• Sense of ownership taking full responsibility and accountability for your contributions
• Technical accomplishments are considered a plus: Kaggle, Hackathons, Olympiads, publications