Machine Learning Scientist, Reinforcement Learning

About the role

Profluent is seeking a motivated Machine Learning Scientist to lead reinforcement learning research for biomolecular design. You will work at the intersection of generative modeling, protein engineering, and advanced AI to create next‑generation protein design tools.

Key responsibilities

• Design and develop state‑of‑the‑art online and offline reinforcement learning algorithms for protein design.
• Collaborate with machine learning and protein design teams to adapt RL techniques from other domains.
• Architect, implement, and optimise core infrastructure for post‑training of protein language models.
• Curate relevant datasets and create evaluation tasks for generative models.
• Implement, analyse, and present computational results to interdisciplinary teams.
• Contribute to the scientific and strategic vision of the company.

Required profile

• PhD or equivalent industry experience in Computer Science, Machine Learning, NLP, Applied Mathematics, Computational Biology, Statistics, or a related field.
• Proven experience conceiving, implementing, and evaluating novel machine learning and reinforcement learning techniques.
• Publications in top conferences (NeurIPS, ICML, ICLR) or journals (Nature, Science, etc.).
• Hands‑on experience with modern deep‑learning frameworks such as PyTorch or Jax.

Required skills

• Python programming.
• PyTorch and Jax.
• Reinforcement learning algorithms.
• Protein language models and protein design concepts.
• Cloud compute platforms (GCP, AWS, Azure, OCI).
• Bioinformatics data extraction and curation.

What we offer

• High‑growth opportunity with meaningful impact on the future of protein design.
• Competitive compensation package.