Duties: Research and build high-impact end-to-end machine learning systems for customer growth; Drive the design of scalable ML solutions in production; Coordinate the work of applied scientists and machine learning engineers providing research guidance and mentorship; Promote and role-model best practices of data science, engineering, and communication throughout the organization; Mentor engineers and data scientists across the org; Navigate existing ML and AI research and apply it to solve business problems
Requirements: M.S or B.S.in a Computer Science/Statistics/Data Science or related field; 10+ years of building products with Machine Learning and Personalization; Deep understanding of statistics and ML and AI fundamentals; Deep knowledge in multiple ML methodologies, such as LLMs, Foundation Models, Recommendations & Search, Causal Machine Learning, Interpretable ML, Multi-Armed Bandits, Reinforcement Learning and Constrained Optimization

Duties: Develop analytical libraries used for pricing, market analysis and risk-management for Fixed Income Market business. Develop optimization tools for calculating optimal balance sheet, liquidity, and capital charges. Develop web-based front-end interface for stakeholders to view and interact with analytical tools. Create, implement, and support quantitative models for the Fixed Income trading business leveraging a wide variety of mathematical and computer science methods and tools including, mathematical finance, statistics and probability and software engineering. Apply advanced coding skills to code and debug code
Requirements: Requires a Master’s degree, or foreign equivalent in Financial Engineering, Financial Mathematics, Operations Research and Information Engineering, or related quantitative field and 2 years of experience as a Quantitative Analyst, or related position involving quantitative statistical modeling and data analysis for a global financial services institution

Duties: Analyse antibody sequences (liability screening, clustering, etc.) from Sanger and Next Generation sequencing platforms; Develop and implement computational pipelines and algorithms for analysing antibody sequences; Organize data storage in accessible formats for other team members; Prepare data summaries, overviews, and visualizations; Participation on in silico design of humanized antibodies; Integration of Electronic Lab Notebook system in the laboratory
Requirements: Master's or Ph.D. degree in Bioinformatics, Computational Biology, or a related field; Strong background in protein bioinformatics with a focus on sequence analysis; Knowledge of next-generation sequencing (NGS) data analysis and genomics databases; Proficiency in programming languages commonly used in bioinformatics such as Python or R; Familiarity with statistical methods and machine learning techniques applied to biological sequence data

Duties: Research, design, and implement algorithms in Deep Learning for Medical Image Analysis problems; Close collaboration with clinical customers to define, implement and validate clinical prototypes; Advance the state-of-the-art, including generating patents and publications in top journals and conferences; Work on large-scale, real-world problems; Fast prototyping, feasibility studies, specification and implementation of image and video analysis product components; Working on challenging and impactful clinical problems with large-scale data sets
Requirements: Ph.D. in an engineering or science field such as Computer Science, Electrical Engineering, Statistics, or Applied Math; Strong background in Deep Learning and/or Medical Image processing; 4+ years of industry experience or post-doctoral studies in Computer Vision, Machine Learning and Image Understanding preferred

Duties: Build machine learning solutions to respond to and mitigate business risks in ByteDance products/platforms. Such risks include and are not limited to abusive accounts, fake engagements, spammy redirection, scraping, fraud, etc; Improve modeling infrastructures, labels, features and algorithms towards robustness, automation and generalization, reduce modeling and operational load on risk adversaries and new product/risk ramping-ups; Uplevel risk machine learning excellence on privacy/compliance, interpretability, risk perception and analysis
Requirements: Master or above degree in computer science, statistics, or other relevant, machine-learning-heavy majors; Solid engineering skills. Proficiency in at least two of: Linux, Hadoop, Hive, Spark, Storm; Strong machine learning background. Proficiency or publications in modern machine learning theories and applications such as deep neural nets, transfer/multi-task learning, reinforcement learning, time series or graph unsupervised learning

Duties: Contribute to the design and analysis of algorithms for mathematical optimization and machine learning, and to the modeling for complex design, decision, and inference problems; Collaborate in the development and application of software instantiations of numerical algorithms; Document work and results in the form of journal papers and conference proceedings, and present work and results at scientific meetings and conferences; Bring creative and innovative perspectives by proposing algorithmic approaches, models, and solutions; Engage and interact with multidisciplinary science and engineering teams
Requirements: Ph.D. degree in applied mathematics, computer/computational science, data science, operations research, or a related field is required; Fundamental knowledge of mathematical optimization, statistics, or machine learning

Duties: As part of the company's Center for Data Science and Artificial Intelligence (CDSAi) corporate analytics group, applies analytical skills to work on all aspects of the life insurance value chain, ranging from risk models, fraud detection, customer behavior study, process triaging, and marketing prediction to a variety of other analytics solutions. Applies technical data, analytical, and programming skills to ingest, wrangle, and explore external and internal data to create data assets and reports
Requirements: Master's degree in Statistics, Analytics, Computer Science, Mathematics or Machine-Learning (willing to accept foreign education equivalent) and three (3) years of experience performing data analytics and statistical modeling using complex large-sized datasets in the consumer finance domain

Duties: This role is available on the OCI AI Services and Data org. We are addressing exciting challenges at the intersection of artificial intelligence and cutting-edge cloud infrastructure. We are building state of the art data processing, model training and benchmarking platform. As Software Engineer on our team, we build services and tools to manage model lifecycle, model provenance, model catalog and model training. We build shared GPU super cluster that enables customers to easily onboard run, monitor and managing AI models at scale with OCI. You will have the opportunity to work on the LLM accelerators, setup ML training and benchmarking platforms, runtimes, libraries in the open-source projects that enable low friction performance optimized large scale training and inferencing of the world’s most advanced AI models
Requirements: Bachelor’s degree in computer science, engineering, or an equivalent highly technical field; 6+ years of software engineering experience and a proven track record of successfully architecting and shipping high performance, low latency AI/ML enabled products & services; Strong technical understanding in building complex, scalable, low latency streaming/batch processing AI/ML cloud services; Proven track record on running operations for a cloud service; Deep knowledge of large-scale compute, network, and storage systems; Experience working with Distributed Systems

Duties: In this role, you will aid applied research and product development by conceptualizing, designing, and implementing engineering improvements related to large-scale deep neural networks. You would have proven expertise in efficiency optimizations using techniques such as quantization, model pruning, distillation, compute-efficient finetuning, etc. You have to be deeply knowledgeable in ML hardware and software to be successful in this role. Additionally, you need solid software development skills, a love of learning, a passion for solving problems, a bias to action, and effective collaboration with scientists
Requirements: 5+ years of software engineering experience with a track record of delivering quality results; Proven expertise in training and serving infrastructure for LLMs and other large foundation models; Strong problem-solving skills with knowledge of statistical methods; Strong software development experience in languages such as Python and Java; Deep understanding of TensorFlow and/or PyTorch

Duties: Provide strategic direction for prenatal bioinformatics efforts, aligning strategies with company goals and collaborating with cross-functional teams; Lead the Bioinformatics team, fostering innovation, collaboration, and growth. Guide project priorities, resources, and career development; Develop and execute bioinformatics strategies to enhance next-generation sequencing pipelines for the Unity product line, driving innovation
Requirements: PhD in Bioinformatics, Computational Biology, Computer Science, Bioengineering, Physics or related field; Demonstrated ability to manage and lead a team of bioinformaticians; Excellent project management skills and attention to detail; Excellent communication and ability to work collaboratively with a cross-functional team; 10+ years of bioinformatics experience (including PhD years or afterward)

Duties: Design and build core components of a first-of-its-kind data analytic application for maintenance and operations teams within asset intensive businesses; Grow your own skills and capabilities, surrounded by like-minded, crazy-smart, passionate creators focused on building best-in-class products for a huge, woefully underserved market; Create products from the ground up. From idea inception through design to detailed completion, all of your work will be on the critical path for the team and the business; Drive development of end-to-end data management solutions that span ad-hoc, batch and stream data processing at scale
Requirements: 5+ years of previous work experience and B.Sc. or M.Sc. in Computer Science or related field; Sound fundamentals and commitment to quality (read: not this guy https: //twitter.com/hipsterhacker ); Experience with Machine Learning, algorithm analysis, and data modeling; Experience with Scala and/or Java and JVM internals

Duties: Lead Model Validation activities for the IMM model suite, including prioritization of the Book of Work for the team; Conduct independent testing to assess model accuracy and robustness under different scenarios and market conditions, including, where appropriate the evaluation of developer documentation and testing; Assess, quantify and communicate to stakeholders model risks due to model limitations and compensating controls; Develop and apply high-quality validation standards by conducting independent testing to assess model accuracy and robustness under different market conditions; Highlight risks and limitations of models and communicate findings to stakeholders, senior management, and governance committees
Requirements: Masters or Ph.D. degree (or equivalent) in Finance, Mathematics, Physics, or a related quantitative field; The ideal candidate has strong experience with counterparty credit risk modelling framework gained at a financial institution, including experience facing regulatory requests; The ability to effectively communicate with a wide range of stakeholders (incl. senior management and internal/external auditors), both written and verbally

Duties: Conceiving of and conducting independent ML research, motivated by scientific milestones important to our team; Collaborating with colleagues across Flagship to apply our research to proprietary biological data and processes; Working with our ML Engineers to scale and interpret experiments
Requirements: PhD or MSc in a relevant field such as machine learning, computer science, math, physics etc. or equivalent practical experience; Significant deep learning experience relevant for position. Examples include geometric neural networks, generative modeling, representation learning, biological foundation models, multi-agent systems and reinforcement learning

Duties: Accountable and responsible for Global Statistical Programming (GSP) study deliverables and submission activities for a group of Amgen products; Set and drive forward the programming strategies such as functional standards and industry requirements for the assigned product areas globally; Plan and monitor progress of projects and address issues accordingly; Support GSP Functional Service Provider (FSP) relationship and oversight; Ensure the product goals can be met via the FSP staff; Provide GSP product level input to developing and managing resource plans and budgets for GSP
Requirements: Doctorate degree and 2 years of statistical programming in Pharmaceutical industry experience; In depth knowledge of SAS Graph, SAS STAT, SAS Macro and SQL; Advanced SAS analysis and reporting skills; Prior regulatory submission experience; Thorough understanding and experience with data quality and compliance checks

Duties: Design, implement and evaluate leading-edge machine learning methods related to cancer immunotherapy; Explore large-scale immunological datasets, including genomic, transcriptomic, mass spectrometry, and structural data, to derive and integrate orthogonal contributors of effective immunotherapy design; Stay abreast of scientific findings and technological developments and continuously improve computational methodology; Maintain detailed documentation of data analysis methods and results; Present findings and participate in cross-function team meetings
Requirements: PhD degree in Computational Biology, Bioinformatics, Computer Science, or a related field + 2 years of relevant experience, or MS degree + 5 years of relevant experience; Strong track record in machine learning algorithm development, preferably with applications in immunology, genomics, or personalized medicine; Proficiency in handling and interpreting large-scale biological datasets, including genomic and proteomic data; Strong programming skills in Python/R and with ML frameworks (e.g. PyTorch)

Duties: Lead end-to-end data science Enterprise Excellence projects, from problem formulation to model deployment to address Citi Line of Business challenges and opportunities; Collaborate closely with cross-functional teams, including technology, re-engineering, product management, and business stakeholders, to define project requirements and deliverables; Apply advanced statistical and machine learning techniques to analyze large, complex datasets and extract actionable insights; Apply mathematical, problem-solving, and coding skills to manage big data, extracting valuable insights; Develop predictive models, algorithms, and data-driven solutions to optimize business process, improve product performance, and drive strategic decision-making; Design and conduct experiments, tests, and casual inference analysis to evaluate the effectiveness of interventions and initiatives
Requirements: Bachelor’s or advanced degree in Computer Science, Statistics, Mathematics, or related field; 6 to 8 years of relevant experience, with 4+ years of professional experience in data science, with strong foundation in statistical analysis, machine learning, and data visualization; Proficiency in programing languages such as Python, R, or Scala, and familiarity with libraries/frameworks such as TensorFlow, PyTorch, scikit-learn, or spark; Solid understanding of experimental design, hypothesis testing, and casual inference methods; Experience with SQL and relational databases for data manipulation and querying; Proficiency with data mining, mathematics, and statistical analysis

Duties: Development, enhancement and maintenance of market risk capital forecasting models under stress scenarios to ensure ongoing appropriateness and adaptations to new regulations (e.g. FRTB); Contribution to key regulatory deliverables and programs (e.g. FRTB) as well as analysis and interpretation of key regulatory requirements; Perform ongoing monitoring and evaluation of market risk capital forecasting models, review existing models to ensure they remain fit for purpose and make improvements where necessary; Collaborate closely with the model validation team to understand validation findings and remediate any identified issues
Requirements: MSc or equivalent in a quantitative subject (such as quantitative finance, statistics/mathematics, sciences or engineering); Deep understanding of quantitative risk including good knowledge of financial products and their risk representation; Demonstrable experience in delivering enhancements to risk models; Excellent mathematical, analytical, problem solving and troubleshooting skills

Duties: Research and develop our machine learning systems, including heterogeneous computing architecture, management, scheduling, and monitoring; Manage cross-layer optimisation of system and AI algorithms and hardware for machine learning (GPU, ASIC); Implement both general purpose training framework features and model specific optimisations (e.g. LLM, diffusions); Improve efficiency and stability for extremely large scale distributed training jobs; Plan and lead the development of new and advanced data analytic techniques, methodologies and analytical solutions from design, prototyping, and testing; Identify and develop core data and AI science components for the delivery of projects, architect specialised database and computing environments, explore and visualise complex data set to provide incremental business value
Requirements: Bachelor or above degree in distributed, parallel computing principles and know the recent advances in computing, storage, networking, and hardware technologies; Familiar with machine learning algorithms, platforms and frameworks such as PyTorch and Jax; Have basic understanding of how GPU and/or ASIC works; Expert in at least one or two programming languages in Linux environment: C/C++, CUDA, Python

Duties: Ability to quickly learn cutting-edge technologies and algorithms in the field of Generative AI to participate in our journey to build the best LLMs; Responsible for the development and maintenance of key platforms needed for developing, evaluating and deploying LLM for real-world applications; Work with other team members to investigate design approaches, prototype new technology and evaluate technical feasibility; Work closely with Applied scientists to process massive data, scale machine learning models while optimizing
Requirements: Bachelor's degree in computer science or equivalent; 3+ years of non-internship professional software development experience; 2+ years of non-internship design or architecture (design patterns, reliability and scaling) of new and existing systems experience; Experience programming with at least one software programming language

Duties: The Director, Quantitative Model Validator position will report directly to the Head of Model Validation for AI/ML and manages a team of quantitative model validators to test and evaluate the conceptual soundness of models and assess limitations and suitability for use. Subject matter authority in the key model risk area of Artificial Intelligence/Machine Learning and other related modeling areas
Requirements: Has a proven track record with a minimum of eight years of experience with Masters (or six years with PhD) in model risk management in banking or insurance; Has a master’s degree in science, data science, math, statistics, or related area; Previous people leadership experience and shown ability to mentor and develop people; Confirmed ability to apply both strategic and analytic techniques to provide business solutions and recommendations; Ability to work independently with high initiative and comfortable working optimally in a collaborative team environment

Duties: Lead, manage and mentor a team of machine learning engineers and applied scientists; Collaborate with product managers to translate business and product requirements into AI projects; Collaborate with fellow technical leaders to ensure the successful and timely delivery of models and integration of services; Coordinate with global teams to drive projects from research to production; Develop new OCI services and features bringing to bear recent advances in generative AI, machine learning and deep learning; Design and review the architecture of generative AI, including data, model, training, and evaluation, employing standard processes; Participate in project planning, review, and retrospective sessions
Requirements: Bachelor's or Master's Degree or equivalent experience in Computer Science or Sciences; PhD or equivalent experience in the relevant area will be a plus; 10+ years overall experience, with at least 2 years in people manager role; Demonstrated experience in designing and implementing scalable AI models for production; Deep technical understanding of Machine Learning, Deep Learning architectures like Transformers, training methods, and optimizers; Practical experience with the latest technologies in LLM and generative AI, such as parameter-efficient fine-tuning, instruction fine-tuning, and advanced prompt engineering techniques like Tree-of-Thoughts

Duties: Fine-tune, scale and deploy large language models over scientific and patent literature for knowledge extraction in materials synthesis and performance; Utilize and develop new prompt engineering strategies in the materials domain; Use LLM-backed agents for lab orchestration, design of experimental assays, and optimization of process parameters for materials synthesis and testing; Contribute to a digital platform that can continually finetune models as more data becomes available; Continually cultivate scientific/technical expertise through critical review of ML literature, attending conferences, writing publications, and developing relationships with key opinion leaders; Work with the computational team to identify materials design pathways that target desired functional properties and their synthesis
Requirements: PhD in Computer Science, Applied Mathematics, quantitative disciplines with strong focus in ML, or related field; Experience using a cloud computing service to reduce runtime to train and evaluate deep learning models; Coding experience with large language models (e.g. GPT or other autoregressive LLMs); Strong experience with prompt engineering; Hands-on experience implementing, deploying, evaluating, fine-tuning and hyperparameter-tuning deep learning models at scale; Experience in machine learning strategies like lifelong learning, online learning, incremental learning

Duties: Prepare and validate data, and then build and validate a scoring model using logistic regression or alternative quantitative/machine learning methods; Conduct data-driven analysis and create reports. Apply quantitative and qualitative data analysis methods; prepare statistical and non-statistical data exploration. Validate data, identify data quality issues (if any), and work with Technology to address them. Analyze and interpret data reports, draw conclusions and make recommendations answering specific business needs; Document solutions and present results in a simple comprehensive way to non-technical audience, as well as write more formal documentation using statistical vocabulary; Generate new ideas, concepts and models to improve methods of obtaining and evaluating quantitative and qualitative data. Identify relationships and trends in data, as well as any factors that could affect the results of research. Question and validate assumptions. Escalate identified risks and sensitive ...
Requirements: 3+ Years of relevant experience in strong statistical academic background, knowledge of quantitative methods, including predictive modeling techniques; Experience in creating and assessing Statistical Models; Highly-skilled and good working knowledge of Python/SAS & MS Office Suite; Experience in reporting the results of analysis in clear written form, and in presenting the findings during meetings and conference calls; Team working experience (demonstrated team player ability required)

Duties: Develop and optimize scalable ML pipelines to handle large datasets, train intricate models, and seamlessly deploy them into production; Collaborate closely with diverse teams across behavior, perception, research, mapping, and infrastructure to design and implement comprehensive ML solutions; Create frameworks and interfaces to accelerate machine learning development and maximize hardware efficiency; Engineer robust, high-quality software to enhance our vehicles' navigation capabilities and ensure safety on the road
Requirements: Possess a minimum of 4 years of hands-on experience in ML infrastructure or large-scale distributed systems; Demonstrate expertise in ML fundamentals and deep learning frameworks like PyTorch, Jax, Tensorflow, and Keras, with a focus on scalability, optimizations, and profiling; Proficient in Python and experienced in C++ development; Exhibit a strong passion for advancing robotics to improve everyday life experiences

Duties: We are a computer vision company. Computer vision entails churning through large amounts of image data. Unlike other vision companies, we want to run our software on compute constrained devices. We’ve already shown that this is possible in a couple of initial applications, and now we’re ready to expand to tackle the pipeline of projects we have lined up
Requirements: You should have expertise in computer vision (or related field such as computer graphics, machine learning, computational math, etc), and have worked on developing large scale deployable software projects