Scientist , Computation Protein Design

About Alta

Alta Resource Technologies (Alta) is a next generation mining company that uses synthetic biology to separate critical minerals from conventional and unconventional resources, including e-waste. Founded in 2023, the company is expanding its team in Boulder, CO. The exponential growth of new tech industries, as well as the information technology sector, are driving historic growth in mineral demand and stressing existing supply streams.

Meeting new demand, diversifying the supply chain, and producing minerals in a more sustainable manner requires the rapid development, deployment and scaling of new technologies. This mineral challenge represents a historic opportunity for technology development and value creation. Alta is proud to be at the vanguard of this mega trend.

We are seeking a highly skilled Scientist I, Computation Protein Design to join our team and drive innovation in protein design and engineering through good data practices, robust analytical methods and machine learning. This role focuses on leveraging and optimizing foundational AI models to accelerate our protein engineering pipeline. The ideal candidate will have deep expertise in model fine-tuning, evaluation, and selection, with strong data engineering capabilities to support cutting‑edge computational biology research.

• Fine‑tune, adapt, and use foundational protein models (e.g., Boltz Gen, ESM, Open Fold derivatives) for protein engineering applications
• Develop and implement rigorous evaluation frameworks to assess model performance, including metrics for protein structure prediction, sequence optimization, and functional property prediction
• Conduct comprehensive benchmarking studies to identify and recommend the most suitable foundational models for various protein engineering tasks
• Design and execute computational experiments to validate model predictions against experimental data
• Leverage multiple information sources (including bioinformatic, structural, simulations, and experimental performance data) to improve internal models and develop agentic frameworks

• Collaborate with the Applied Biology team to translate models into actionable insights.
• Create technical documentation including model assumptions, equations, validation results, and recommendations.
• Provide technical mentorship and review for junior engineers and scientists.
• Clearly communicate technical findings, risks and recommendations to leadership and project stakeholders.

• Contribute to developing data management systems that meet FAIR principles.
• Develop analysis code to support the team in analyzing experimental data.
• Engineer data into vectorized format for MCP integration
• Utilize experimental data to validate and improve model development.

• Ph.D. in Computational Biology, Bioinformatics, Computer Science with 1-3 years of relevant industry or post‑doctoral experience, or M.S. with 6+ years of relevant industry experience
• Hands‑on experience with protein foundation models such as ESM-2, ESM-3, ProteinMPNN, RF diffusion, Alpha Fold, or similar architectures
• Knowledge of protein design software and molecular modeling tools (Rosetta, PyMOL, Chimera)
• Demonstrated experience fine‑tuning and working with large‑scale machine learning models, preferably protein or biological sequence models
• Strong proficiency in Python and modern ML frameworks (PyTorch, Tensor Flow, JAX)
• Experience with model evaluation methodologies, including cross‑validation, performance metrics, and statistical analysis
• Solid understanding of protein structure, function, and the principles of protein engineering
• Experience with high-performance computing environments and GPU‑accelerated computing
• Strong communication skills and ability to work collaboratively in interdisciplinary teams
• US Citizenship required.

• Experience with workflow management tools (Nextflow, Snakemake, or similar)
• Familiarity with cloud computing platforms (AWS, GCP, Azure) and containerization (Docker, Singularity)
• Experience with distributed computing frameworks…