Valka.ai We're hiring

Join my research team

I'm building a world-class research team at Valka.ai to tackle problems that have never been solved before — from photorealistic neural rendering to generative audio, human motion modeling, and world models. If you love hard research problems and shipping real systems, I'd love to hear from you.

See open roles How to apply

Why join

Frontier research

Work on genuinely unsolved problems in neural rendering, generative AI, and world models.

Real impact

Ship research into products used by millions.

Exceptional team

Join 20+ scientists and engineers collaborating with leading universities.

Open roles

Research Scientist - World Models (Video Understanding and Rendering)

Apply
Remote within US & Canada Intern

Key responsibilities

  • Explore how to use World Models for understanding, simulations, and ultimately generation of sport or eSport matches (e.g., soccer, DOTA).
  • Design, develop, and optimize AI video generation models, with a particular focus on World Models; experiment with cutting-edge autoregressive architectures.
  • Develop and implement state-of-the-art algorithms for synthesizing sport matches.
  • Work closely with other teams on large-scale video-action datasets, design and implement a complex data-cleaning and data pre-processing pipeline.
  • Define robust validation strategies and implement custom evaluation metrics comparing synthetic vs. real gameplay.
  • Stay on the bleeding edge of the relevant literature, e.g., CVPR, NeurIPS, ICML, ICCV, and help to align it with our roadmap.

Required qualifications

  • Pursuing or having PhD in Computer Vision, Machine Learning, or a closely related field.
  • Published at top Computer Vision, AI, or Graphics venues (e.g., CVPR, ICML, ICCV, SIGGRAPH, NeurIPS).
  • Demonstrated hands-on experience with building and running generative CV models (e.g., GANs, DiT, VAE).
  • Solid understanding of neural architectures and paradigms (e.g., Transformers, Denoising Diffusion Models, RNNs, Sequence Models, CNNs).
  • Solid understanding of VAEs (e.g., ELBO).
  • Basic understanding of Reinforcement Learning.
  • Proficiency in Python and PyTorch.

Research Scientist - Sequence Modeling (World Models)

Apply
Remote within US & Canada Intern

Key responsibilities

  • Develop models for real-game simulations for esports (e.g., CS2, Dota2, LoL) as well as classical sports (e.g., soccer).
  • Model generating of long-horizon complex sequence of game events.
  • Explore various ML techniques (Transformers, AR-based techniques, RL, or other sequence modeling methods).
  • Focus research on realistic player behavior, team dynamics, and overall match flow.
  • Define robust validation strategies to compare synthetic gameplay against real match data and build custom evaluation metrics.
  • Handle large datasets, build efficient training and inference pipelines.
  • Stay on the bleeding edge of the relevant literature, e.g., NeurIPS, ICML, ICLR, and help to align it with our roadmap.

Required qualifications

  • Pursuing or having PhD in Computer Vision, Machine Learning, or a closely related field.
  • Demonstrated hands-on experience with sequence modeling.
  • Strong research background in NLP or sequence modeling demonstrated by having publications at top tier conferences such as NeurIPS, ICML, ICLR, CVPR, or ICCV.
  • Proficiency in Python and PyTorch.
  • Solid understanding of neural architectures or paradigms (CNNs, Transformers, diffusion models, autoregressive models etc.).

Research Scientist - Sports Simulation

Apply
Remote within US & Canada Intern

Key responsibilities

  • Design, develop, and optimize 3D and 4D models for realistic simulation of sports matches, including players, equipment, playing surfaces, stadium environments, and dynamic interactions.
  • Explore state-of-the-art 3D scene representations and neural rendering techniques (e.g., 3D and dynamic Gaussian Splatting, NeRF, mesh, or hybrid approaches).
  • Develop simulation pipelines that translate game states, trajectories, and match events into spatially and temporally consistent 3D scenes with physically plausible player motion, ball or object trajectories, contacts, occlusions, camera motion, and multi-agent interactions.
  • Work closely with the sequence-modeling and video-rendering teams while building scalable data-processing, training, reconstruction, and rendering pipelines for large-scale sports datasets.
  • Define robust validation strategies and custom metrics across geometry, motion, physics, temporal consistency, and visual realism.
  • Stay on the bleeding edge of the relevant literature, e.g., CVPR, ICCV, ECCV, SIGGRAPH, NeurIPS, and ICML.

Required qualifications

  • Pursuing or having PhD in Computer Vision, Machine Learning, Computer Graphics, Robotics, or a closely related field.
  • Demonstrated hands-on experience with 3D vision, neural rendering, dynamic scene reconstruction, or learned simulation.
  • Strong research background demonstrated by publications at top-tier conferences such as CVPR, ICCV, ECCV, SIGGRAPH, NeurIPS, or ICML.
  • Solid understanding of 3D scene representations and rendering techniques, such as Gaussian Splatting, NeRFs, meshes, point-based representations, and differentiable rendering.
  • Experience with multi-view geometry, camera calibration, tracking, human reconstruction, motion estimation, or physics-based simulation.
  • Proficiency in Python and PyTorch.
  • Experience with sports data, player tracking, motion capture, or multi-agent environments is a strong plus.

Research Scientist - Video / DiT Rendering

Apply
Remote within US & Canada Intern

Key responsibilities

  • Design, develop, and optimize AI video generation models using diffusion techniques, with a focus on maintaining consistency, realism, and style.
  • Research state-of-the-art video models for generating human-centric videos in real-time.
  • Work closely with other teams on large-scale video datasets, including human motion and gestures, facial expressions, and scene context.
  • Experiment with cutting-edge diffusion architectures for controllable and high-quality video synthesis.
  • Stay on the bleeding edge of the relevant literature, e.g., CVPR, NeurIPS, ICML, ICCV, and help to align it with our roadmap.

Required qualifications

  • Pursuing or having PhD in Computer Vision, Machine Learning, or a closely related field.
  • Demonstrated hands-on experience with research of video diffusion models.
  • Strong research background in image or video synthesis demonstrated by having publications at top tier conferences such as CVPR, NeurIPS, ICCV, SIGGRAPH, and ICML.
  • Experience with real time video generation is a big plus.
  • Proficiency in Python and ML frameworks such as PyTorch.
  • Solid understanding of neural architectures or paradigms (CNNs, Transformers, diffusion models, autoregressive models etc.).

Research Scientist - Motion Modeling

Apply
Remote within US & Canada Intern

Key responsibilities

  • Design, develop, and optimize AI models to generate realistic motion of human bodies (e.g., realistic head motion, lips motion, hand gestures).
  • With a particular focus on hand-object interaction and manipulation.
  • Work closely with the video rendering team.
  • Experiment with cutting-edge architectures for controllable and high-quality motion synthesis, e.g., diffusion-based and AR-based techniques.
  • Define robust validation strategies and implement custom evaluation metrics.
  • Stay on the bleeding edge of the relevant literature, e.g., CVPR, NeurIPS, SIGGRAPH, ICML, ICCV, and help to align it with our roadmap.

Required qualifications

  • Pursuing or having PhD in Computer Vision, Machine Learning, or a closely related field.
  • Hands-on experience with research of generative motion modeling such as motion diffusion, or VQ-VAE based approaches.
  • Strong research background in motion modeling demonstrated by having publications at top tier conferences such as CVPR, NeurIPS, ICCV, SIGGRAPH, and ICML.
  • Proficiency in Python and ML frameworks such as PyTorch.
  • Solid understanding of neural architectures or paradigms (CNNs, LSTM, Transformers, diffusion models, autoregressive models etc.).

Research Engineer, Text-to-Speech

Apply
Remote within Europe & US East Coast Full-time

Key responsibilities

  • Combine results from different experiments into one TTS pipeline, evaluate, ensure model compatibility with other pipelines and the technical requirements, help with demos and cooperate with engineering on deployment of these models.
  • Provide engineering support to research, training, and inference of SOTA TTS models for realistic and emotional voice generation for entertainment and education applications.
  • Track our trained TTS models, assemble the results of the best experiments into one speech production system, evaluate the model performance and quality.
  • Closely collaborate with TTS researcher team, closely cooperate with product engineering and platform teams to ensure smooth deployment.

Required qualifications

  • Experience working with ML models in production (e.g., model formats, quantization, deployment, hardware requirements, model logging, or tracking).
  • Proficiency in Python and key libraries (e.g., PyTorch, Hugging Face Transformers).
  • Experience with training text-to-speech / voice cloning models, understanding of human speech and audio processing.
  • Solid understanding of neural architectures or paradigms (transformers, diffusion models, GANs, AR models, etc.).
  • Familiarity with modern speech synthesis models (GPT-based, flow matching, such as Vevo, StyleTTS, IndexTTS, Maskgct etc.).
  • Contributions to open-source AI tools.
  • Familiarity with AWS / other cloud providers.

Research Scientist, Text-to-Speech

Apply
Remote within Europe & US East Coast Full-time

Key responsibilities

  • Independently translate research papers into experiments, come up with new approaches and conduct model training.
  • Research and train fast and quality SOTA TTS models for realistic and emotional voice generation for entertainment and education applications.
  • Experimenting with different architectures / data to improve the quality and speed of the TTS model(s) and put the best results to production.
  • Staying up to date with current research and coming up with new ideas.

Required qualifications

  • Demonstrated hands-on experience with training text-to-speech / voice cloning models.
  • Solid knowledge of transformers, diffusion models, GANs.
  • Understanding of human speech and audio processing (e.g., sampling, spectrograms, vocoders).
  • Proficiency in Python and key libraries (e.g., PyTorch, Hugging Face Transformers).
  • Ability to keep up to date with research, understand papers, implement approaches; strong ML fundamentals and critical thinking.
  • Familiarity with modern speech synthesis models (GPT-based, flow matching, such as Vevo, StyleTTS, IndexTTS, Maskgct etc.).
  • Contributions to open-source AI tools or research publications in Speech processing field.

Python Research Engineer

Apply
Remote within US & Canada Full-time

Key responsibilities

  • Bridging the Video Generation research team and the product engineering platform team.
  • Taking research models and demos and turning them into robust, deployable, production-ready Python services.
  • Package and containerize AI models (Python/Docker) from research into clean, versioned services with well-defined APIs.
  • Own the engineering side of the tech transfer process: inference specs, environment setup, model mocking, and integration scaffolding.
  • Collaborate closely with research scientists on quantization, TensorRT compilation, and hitting latency budgets (e.g. <200ms real-time response targets).
  • Maintain and operate model services in production, debugging stability and performance issues under load.
  • Contribute to the dual-track delivery model — keeping the engineering platform moving even while research is still iterating.

Required qualifications

  • Strong Python engineering skills, writing production-grade and maintainable code.
  • Experience with owning ambiguous model-to-service transfers end-to-end with a high degree of autonomy.
  • Hands-on experience deploying ML/AI models (inference pipelines, serving frameworks).
  • Familiarity with GPU workloads, containerization, and model optimization concepts.
  • Ability to read and work directly with research code and translate it into reliable services.
  • Experience with video/image generation models, TensorRT, or real-time streaming pipelines.
Valka.ai

How to apply

Send me a short note about yourself, what you'd like to work on, and a link to your CV, Google Scholar, or GitHub. Mention the role you're interested in.

ondrej.texler@valka.ai