# Ross Altman > ML Engineer • Data Scientist • GenAI, Foundation Models, Scientific ML • Systems-level Thinking & Innovation Location: Greater Boston, United States Profile: https://flows.cv/rossaltman I’m a senior machine learning engineer and data scientist working at the intersection of research, data, and production ML, with over a decade of experience spanning physics, biotechnology, and applied AI. My work focuses on building and deploying scalable data and machine learning systems — from statistical analysis and interpretable modeling to foundation models and generative pipelines — that support high-stakes scientific and product decisions. I’ve led initiatives that move ideas from early research through production, often in resource-constrained environments where precise and strategic problem framing, informed use of real-world constraints, and end-to-end stewardship matter more than headcount. A consistent theme in my work is applying mature frameworks from mathematics, physics, and other established fields to emerging problem domains, where an interdisciplinary perspective can unlock non-obvious, high-impact insights from complex, real-world data. I enjoy collaborating with people who value systems-level thinking, rigorous experimentation, and the commitment required to carry promising ideas through to robust, real-world solutions. ## Work Experience ### Staff Machine Learning Engineer @ Inari Jan 2024 – Present | Cambridge, Massachusetts Lead research and applied ML initiatives focused on building and scaling genomic foundation models, generative design of CRISPR enzymes for gene editing, and high-throughput in silico target screening supporting biological discovery, predictive design, and product development. Key contributions: • Led a small, cross-functional team in the design and training of a biologically-informed foundation model for plant genomics, improving predictive performance of gene regulation and enabling automated screening and design workflows across multiple research programs. • Architected and deployed an internal ML platform integrating protein structure prediction, generative modeling, and workflow orchestration to support high-throughput biological screening in a secure environment. • Built scalable protein-protein interaction screening and prioritization pipelines that significantly reduced experimental load and accelerated target discovery cycles. • Developed cross-species transfer learning approaches to integrate and enrich heterogeneous biological datasets, improving inference quality in data-sparse regimes. • Automated quantitative phenotyping workflows using computer vision and graph-based analysis to transform large collections of noisy images into interpretable features. ### Data Scientist @ Inari Jan 2019 – Jan 2024 | Cambridge, Massachusetts Led statistical modeling and deep learning efforts to support target discovery for gene editing, CRISPR protein engineering, experimental design and library generation, and data-driven decision making across multiple research and product pipelines. Key contributions: • Built and deployed statistical and machine learning models to estimate phenotypic impact of genome editing, enabling data-driven screening and more efficient use of experimental resources. • Developed protein sequence-based modeling pipelines to explore functional and stability-related variation, supporting downstream CRISPR enzyme engineering efforts. • Applied context-aware sequence modeling and representation learning techniques to score and prioritize variants driving high-impact pathogenic traits in large plant populations. • Collaborated closely with biologists to translate research questions into quantitative modeling problems and communicate results effectively. • Mentored interns and early-career scientists on applied modeling, data analysis, and ML best practices. ### Data Science Fellow @ Insight Data Science Jan 2018 – Jan 2018 | Greater Boston Area • Build a Word2Vec-based pipeline to boost the content relevance of Wikipedia page previews, resulting in a 17% improvement in clickstream engagement. • Developed and deployed a Chrome extension using Flask and AWS for real‐time user‐side data retrieval and ranking. ### Graduate Research Assistant — Physics @ Northeastern University Jan 2011 – Jan 2017 | Boston, MA Advisor: Brent D. Nelson Focus: String theory, topology, machine learning. • Developed high-performance, distributed C++/Python algorithms to construct a massive database of 100k vacuum state solutions compatible with Type‐IIB string theory, effectively bridging high-dimensional geometry with the Standard Model. • Applied deep “equation learner” networks to extrapolate the solution space structure of the full string landscape. • Built and maintained data platforms and search tools used by international research collaborators. • Co-founded an interdisciplinary research effort focused on applying ML to fundamental physics problems. ### Graduate Teaching Assistant — Physics @ Northeastern University Jan 2011 – Jan 2017 | Boston, MA • Engaged and motivated audiences in both introductory and advanced topics as a course lecturer. • Facilitated teamwork and creative experimental design as a lab instructor. • Provided detailed feedback and performance reviews as a graduate course grader. • Instructed non-native English speaking students on various technical topics through the U.S. Pathway Program. ### Graduate Teaching Assistant — Physics @ Rensselaer Polytechnic Institute Jan 2010 – Jan 2011 | Troy, NY • Engaged students in introductory physics topics as a lecturer. • Encouraged creativity and teamwork as a physics lab instructor. • Provided detailed feedback to students as a course grader. ### Graduate Research Assistant — Physics @ Cornell University Jan 2009 – Jan 2010 | Ithaca, NY Advisor: Prof. Richard V. E. Lovelace Focus: Computational astrophysics • Implemented high-performance simulation of stellar occultation events using C/C++. • Researched and developed algorithms for recovering atmospheric information of exoplanets from observed lightcurve data. ### Undergraduate Research Assistant — Physics @ Cornell University Jan 2007 – Jan 2007 | Ithaca, NY Advisor: Saul Teukolsky • Contributed objective C code for an ongoing software project for simulating binary black hole systems. ### Research Intern — Organic Chemistry @ Stony Brook University Jan 2003 – Jan 2005 | Stony Brook, NY Advisor: Nancy Goroff • Determined the energetically favorable conformations of complex macromolecules using MacroModel software. • Developed an algorithm in C++ to determine the potential for bonding between various sites in each molecular system. • Helped design a cyclodextrin scaffold-directed method for synthesizing kekulene. ## Education ### Masters of Engineering in Applied Physics Cornell University ### Doctor of Philosophy (Ph.D.) in Theoretical Physics Northeastern University ### BS in Applied and Engineering Physics Cornell University ## Contact & Social - LinkedIn: https://linkedin.com/in/ross-altman - Portfolio: http://www.rossealtman.com - GitHub: https://github.com/knowbodynos --- Source: https://flows.cv/rossaltman JSON Resume: https://flows.cv/rossaltman/resume.json Last updated: 2026-03-31