Cresta is on a mission to turn every customer conversation into a competitive advantage by unlocking the true potential of the contact center. Our platform combines the best of AI and human intelligence to help contact centers discover customer insights and behavioral best practices, automate conversations and inefficient processes, and empower every team member to work smarter and faster. Born from the prestigious Stanford AI lab, Cresta's co-founder and chairman is Sebastian Thrun, the genius behind Google X, Waymo, Udacity, and more. Our leadership also includes CEO, Ping Wu, the co-founder of Google Contact Center AI and Vertex AI platform, and co-founder, Tim Shi, an early member of Open AI. Join us on this thrilling journey to revolutionize the workforce with AI. The future of work is here, and it's at Cresta. About the role: At Cresta, we are dedicated to building state-of-the-art Machine Learning systems that power real-time, intelligent customer interactions. Our team develops models and platforms that process large-scale, multimodal data—especially speech and text—to extract meaning, improve quality, and deliver actionable insights at scale. By combining applied research with strong engineering discipline, we enable organizations to continuously improve AI-driven experiences in production environments.A key focus of this role is advancing model evaluation, measurement, and quality improvements, with particular emphasis on Automatic Speech Recognition (ASR) and downstream NLP systems. You will design rigorous evaluation frameworks, define quality metrics, and drive systematic improvements to model accuracy, robustness, and reliability. You will work closely with applied researchers, product teams, and platform engineers to ensure that model performance improvements translate into measurable business impact.As a Senior Machine Learning Engineer, you will be at the forefront of applying modern ML and speech/NLP techniques to production systems. Your work will focus on improving ASR quality, building scalable evaluation and benchmarking infrastructure, and enabling continuous model iteration through data-driven insights. Responsibilities - Design, implement, and maintain evaluation frameworks to measure model accuracy, robustness, latency, and real-world performance across ASR and NLP systems. - Lead ASR quality improvement efforts, including error analysis, dataset curation, metric definition (e.g., WER and task-specific metrics), and model iteration. - Analyze large-scale speech and text data to identify failure modes and drive targeted model and data improvements. - Develop, train, and deploy machine learning models for speech recognition and downstream tasks such as classification, entity recognition, information extraction, and structured insight generation. - Partner with applied research to translate experimental improvements into production-ready systems. - Collaborate with product managers, platform engineers, and UX teams to align model quality metrics with customer and business goals. - Optimize ML pipelines and evaluation workflows to operate efficiently and reliably at scale. - Establish best practices for model validation, offline/online evaluation, and continuous quality monitoring in production. Qualifications We Value - Master’s or Ph.D. in Computer Science, Machine Learning, AI, or a related field. - 5+ years of hands-on experience building, evaluating, and deploying ML models in production. - Strong background in speech recognition (ASR), speech processing, or closely related domains. - Deep experience with model evaluation, benchmarking, and error analysis for ML systems. - Proficiency with ML frameworks and libraries (e.g., PyTorch, TensorFlow, Hugging Face). - Solid understanding of modern ML techniques, including transformer-based models and large-scale training. - Experience building data pipelines and tooling for large-scale experimentation and quality analysis. - Strong passion for improving real-world AI system quality, with a track record of delivering measurable, production-grade improvements. Perks & Benefits: We offer a comprehensive and people-first benefits package to support you at work and in life: - Comprehensive medical, dental, and vision coverage with plans to fit you and your family - Flexible PTO to take the time you need, when you need it - Paid parental leave for all new parents welcoming a new child - Retirement savings plan to help you plan for the future - Remote work setup budget to help you create a productive home office - Monthly wellness and communication stipend to keep you connected and balanced - In-office meal program and commuter benefits provided for onsite employees Compensation at Cresta: Cresta’s approach to compensation is simple: recognize impact, reward excellence, and invest in our people. We offer competitive, location-based pay that reflects the market and what each individual brings to the table. The posted base salary range represents what we expect to pay for this role in a given location. Final offers are shaped by factors like experience, skills, education, and geography. In addition to base pay, total compensation includes equity and a comprehensive benefits package for you and your family. OTE Range: $205,000–$270,000 + Offers Equity This posting will be used to fill a newly-created role. We have noticed a rise in recruiting impersonations across the industry, where scammers attempt to access candidates' personal and financial information through fake interviews and offers. All Cresta recruiting email communications will always come from the @cresta.ai domain. Any outreach claiming to be from Cresta via other sources should be ignored. If you are uncertain whether you have been contacted by an official Cresta employee, reach out to recruiting@cresta.ai

• Drive high-impact initiatives that turn global operational pain points into production-ready ML products. • Act as the bridge between complex data streams and real-world aviation impact. • Collaborate with technical and operational teams to scale forecasting, predictive analytics, and computer vision capabilities. • Take ownership of ML initiatives from requirements gathering to design, implementation, and delivery. • Define the scope, milestones, and deliverables for assigned initiatives. • Manage timelines and proactively communicate progress, risks, and dependencies. • Engage directly with non-technical stakeholders to clarify operational and business requirements. • Translate business needs into structured technical specifications. • Design and implement business logic, gather and transform structured and non-structured data using Python, SQL, and other ML frameworks. • Develop and maintain data pipelines to support model training, evaluation, and deployment. • Perform exploratory data analysis to understand data patterns, limitations, and quality issues. • Ensure implemented solutions are scalable and maintainable within the existing data ecosystem. • Work closely with Data Science Specialists and analytics colleagues to align with broader ML strategy.

Role Description Adaptive Biotechnologies is seeking a Senior Machine Learning Scientist to contribute to the development of deep learning models for TCR–pMHC specificity prediction. In this role, you will: - Design, implement, train, and evaluate machine learning models that predict interactions between T cell receptors and peptide–MHC complexes. - Integrate sequence and structural information in model development. - Work as part of a collaborative modeling team to implement new modeling ideas and improve predictive accuracy. - Leverage large-scale proprietary immune receptor datasets and shared GPU infrastructure for rapid experimentation and model development. - Collaborate closely with computational scientists, immunologists, and machine learning engineers. - Contribute to diagnostic and therapeutic initiatives both within Adaptive and through external partnerships. Qualifications - PhD in a quantitative discipline (e.g. Machine Learning, Computational Biology, Computer Science) + 5 years progressive experience applying machine learning to real-world scientific or biological problems OR equivalent combination of education and experience. - Progressive experience in the development and deployment of deep learning methods. - Strong hands-on experience in Python and modern ML tooling (PyTorch preferred). - Strong experience in deep learning architecture design and implementation. - Experience working with large datasets and high-performance computing environments. - Ability to independently define, scope, and execute complex technical research problems. - Strong written and verbal communication skills, with ability to present highly technical material to diverse audiences. - Demonstrated ability to collaborate effectively in cross-functional, multi-disciplinary teams. - Driven by impact: motivated to see models transition from research to clinical and commercial application. Requirements - Experience in one or more of the following areas desired: - Demonstrated track record of implementing novel machine learning solutions to biological problems, as demonstrated by publications, conference papers, patents, or delivered products. - Protein structure prediction. - Protein design and generative modeling. - Protein language models. - Large-scale biological datasets. - Immunology and immune receptor specificity (antibody or TCR). Benefits - Salary Range: $144,600 - $217,000 - Equity grant - Bonus eligible

Role Description Adaptive Biotechnologies is seeking a Principal Machine Learning Scientist to lead the development of deep learning models for TCR–pMHC specificity prediction. In this role, you will leverage a large and growing proprietary dataset to design, train, and evaluate models that predict interactions between T cell receptors and peptide–MHC complexes. Your work will focus on developing new approaches that integrate sequence and structural information and rigorously testing their performance in practical settings. You will work closely with computational scientists, immunologists, and machine learning engineers across the organization. The team brings together expertise in immune biology, experimental assay development, and large-scale machine learning, with access to proprietary immune receptor datasets, shared GPU infrastructure, and engineering support for model development and training. This role sits at the intersection of modeling and experimental data generation. As model results highlight gaps in available data or suggest new experimental directions, newly generated datasets can provide additional signal for improving and validating the models. Models developed in this role contribute directly to diagnostic and therapeutic initiatives both within Adaptive and through external partnerships. This position offers the opportunity to advance predictive modeling of immune receptor specificity while seeing those advances translated into meaningful clinical and commercial applications. Key Responsibilities and Essential Functions - Design, implement, and train novel deep learning architectures for TCR–pMHC specificity prediction. - Extend and adapt advances in protein language models, structure prediction, generative modeling, and representation learning to the immune receptor setting. - Leverage and influence scalable training infrastructure to support large-scale model development and experimentation. - Lead rigorous benchmarking and evaluation strategies to ensure models are scientifically sound and practically superior. - Define Modeling and Data Strategy. - Translate biological principles of T cell recognition into principled modeling decisions. - Influence large-scale experimental data generation to maximize modeling leverage and long-term performance gains. - Evaluate emerging ML advances and determine when and how to incorporate them into Adaptive’s modeling roadmap. - Shape the long-term technical direction of machine learning in immune receptor prediction across the organization. - Drive Impact Across the Organization. - Partner with computational biology, immunology, translational, and engineering teams to ensure models are capable, scalable, reproducible, and aligned with therapeutic and diagnostic goals. - Clearly communicate complex modeling insights to scientific leadership, executives, and external partners. - Contribute to intellectual property development and high-impact publications. Qualifications - PhD in a quantitative discipline (e.g. Machine Learning, Computational Biology, Computer Science, etc.) + 12 years progressive experience in machine learning, applied statistics or related field in a life sciences or biotech environment or similar combination of education and experience; an advanced degree preferred. - Masters + 15 years of progressive experience, or - Bachelors + 17 years of progressive experience. - Progressive experience in the conception, development and deployment of deep learning methods including substantial hands-on model development. - Demonstrated track record of innovating and implementing novel machine learning solutions to biological problems, as demonstrated by publications, conference papers, patents, or delivered products. - Deep expertise in python and modern ML tooling (PyTorch preferred). - Experience with version control and ML experiment tracking. - Proven ability to independently define, scope, and execute complex technical research problems. - Excellent written and verbal communication skills, with ability to present highly technical material to diverse audiences. - Exceptional depth in deep learning architecture design and implementation. - Comfortable operating at the frontier of both ML and immunology. - Strategic thinker capable of influencing technical direction across teams. - Driven by impact: motivated to see models transition from research to clinical and commercial application. Preferred - Experience in one or more of the following areas desired: - Protein structure prediction. - Protein design and generative modeling. - Protein language models and large-scale foundation models. - Working with large-scale biological datasets. - Immunology and immune receptor specificity (antibody or TCR). Compensation - Salary Range: $183,400 - $275,000. - Other compensation elements include: - equity grant. - bonus eligible.