Position Overview

Be on the leading edge of applying machine learning and data modeling to the world of advanced cyber security. As part of the Data Science team you will directly impact the product by modeling complex problems and developing production software. You will develop these models and algorithms in collaboration with Security Researchers, who are experts in cyber security attack techniques. This work provides the core detection capability for our customers.

Responsibilities

• Leverage large datasets to develop machine-learning and statistical models that can differentiate between normal and attack behavior.
• Own the prototyping, development, and testing of complex detection algorithms that implement these models and provide customers with real-time visibility.
• Analyze detection algorithm performance and present key findings to business leaders.
• Collaborate with Security Research, Data Engineering, and Software Engineering teammates.

Required Experience

• MS + 2 years of Data Science experience or PhD in Computer Science, Mathematics, Physics or related discipline (or equivalent experience)
• Hands-on experience with machine-learning and statistical techniques
• Strong experience with Python and object-oriented programming
• Ability to work with datasets using SQL or other libraries (pandas, numpy, etc.)
• Familiarity with data structures and algorithms
• Linux and Git proficiency

Desirable Qualifications

• Familiarity with Spark or other distributed computing systems
• Experience with cloud computing platforms (AWS, Azure, GCP, etc.)
• Database experience (SQL or NoSQL variants)
• Experience working in a software development team
• Programming experience in C++, Scala/Java, Go, or other languages

The Data Platforms Health team builds native platform solutions that enable healthcare-specific platform capabilities. This development role in the Database Platforms department will contribute to AI-based features and workflows within the InterSystems Health Connect Cloud, InterSystems FHIR Server, InterSystems IRIS for Health and InterSystems Health Connect products.

The team needs a developer who can drive AI model and AI architecture design and development for intelligent tooling features for healthcare builders and administrators. The team also needs thought leadership to adopt evolving AI technologies so that InterSystems can be a leader in AI-driven features and workflows.

You are encouraged to apply if this role sounds interesting to you, even if you have doubts about being a competitive candidate. InterSystems prides itself on investing in its team members.

This position is in the InterSystems headquarters in Boston, MA.

Responsibilities

You will design, build, train, and deploy machine learning and agentic AI models, and consume LLMs and custom models using REST APIs. Working with product management and support specialists, you will design and implement data cleansing and feature extraction tools to improve the accuracy of predictions and data generation for a healthcare setting. Your responsibilities will include integrating the models into existing products and refining the models to meet evolving product needs.

All work must follow the team’s development workflow including use of source control, design and code reviews, and clear documentation. You are responsible for the quality of your code and must design automated tests to verify the effectiveness of the models and features.

This role will require you to respond to technical questions about expected product behavior and recommended best practices from support specialists and sales engineers. You will also collaborate with the release engineering team to ensure that your model and tools are appropriately deployed in cloud and on-prem environments.

The technology stack includes Python, AWS Bedrock, ObjectScript, and InterSystems IRIS.

As a leader, you are expected to create project plans with estimates, track execution against the plan, and proactively communicate anticipated delays in the project. With support from your manager, you will coordinate with other teams and overcome blockers and inter-team misalignments to deliver your projects. You will also provide thought leadership in the areas of Gen AI, agentic AI, and other evolving technologies, and encourage other team members to adopt these technologies for the platform.

Qualifications Required

• Bachelor’s degree in Computer Science, Engineering, or a related technical discipline.
• 8+ years of hands-on professional experience as a software developer or similar. Open to considering less experience in combination with a Graduate degree.
• Experience with data analysis, data cleansing, feature extraction, and model training.
• Experience with Python and common libraries like Pandas, NumPy, and Scikit-learn.
• Experience with model training and evaluation.
• Experience with Agent orchestration and runtime, MCP, tool registration, and multi-model routing.
• Experience with design or consumption of REST APIs.
• Strong verbal and written communication skills.

Preferred Qualifications

• Self-starter with curiosity to experiment and learn, and confidence to challenge the status quo.
• Professional experience with machine learning algorithms like neural networks and LLMs.
• Professional experience with programmatic access of LLMs and/or open-source models.
• Experience with observability of model performance and runtime costs.
• Experience with model governance and design of guardrails for model security.
• Professional experience with deep learning libraries like TensorFlow, PyTorch, or Keras.
• Professional experience with health messaging standards, especially FHIR.
• 3+ years of experience with other programming languages like Java, C#, C++, or ObjectScript.

We are an equal-opportunity employer and do not discriminate because of race, color, religion, sex, national origin, ancestry, marital status, veteran status, age, disability, sexual orientation or gender identity or expression or any other legally protected category. InterSystems is an E-Verify Employer in the United States.

Position

Manifold's AI team is actively training protein foundation models on our proprietary experimental datasets. Our generative antibody design model, mBER, has already demonstrated controllable de novo binder design across multiple million-scale screening campaigns, and the team is now scaling foundation model capabilities to push well beyond current performance. We are looking for an AI/ML Scientist to join this effort. You will work alongside our existing model training team to accelerate the development of foundation models fine-tuned on Manifold's data, bringing additional depth in pre-training methodology, architecture development, and large-scale training. Your work will directly improve mBER's design capabilities and unlock new modeling paradigms for the broader team. You'll own foundation model projects end-to-end, from architecture selection and training infrastructure to evaluation against real experimental outcomes, while contributing to the team's shared research agenda.

This is an on-site role and can be based in either Boston, Massachusetts or San Francisco, California. Please only apply if you reside in these cities or are open to relocate. 

Responsibilities

• Advance the team's ongoing foundation model training efforts—pretraining, fine-tuning, and evaluating folding, docking, language, and generative design models on Manifold's proprietary experimental data
• Bring depth in training methodology, architecture selection, and optimization to complement the existing team's expertise
• Develop and scale training pipelines for distributed, multi-GPU and multi-node training runs
• Integrate foundation model outputs into mBER to improve binder design success rates and enable new design capabilities
• Design and execute ML experiments with clear hypotheses, rigorous evaluation frameworks, and systematic analysis
• Establish best practices for mixed-precision training, gradient checkpointing, and computational efficiency at scale
• Produce clear documentation and analysis supporting architecture and training decisions

Required Qualifications

• Demonstrated experience pretraining and/or fine-tuning protein foundation models (folding, docking, language models, or generative design) with published or otherwise demonstrable results
• Strong familiarity with AlphaFold architecture and training methodology
• 2+ years of hands-on experience with PyTorch and/or JAX for deep learning
• Experience with large-scale model training: distributed training, multi-GPU/multi-node setups, mixed precision, gradient checkpointing
• Solid understanding of deep learning architectures (transformers, attention mechanisms, diffusion/flow matching) and optimization techniques
• Experience working with protein structure data (PDB, mmCIF) and/or protein sequence datasets
• Strong statistical analysis and experimental design skills
• Proficiency in Python scientific computing stack (NumPy, Pandas, scikit-learn)
• Self-directed researcher who can balance guidance with independence
• Excellent written and verbal communication skills for cross-functional collaboration

Preferred Qualifications

• Experience with protein generative design methods (e.g., RFdiffusion, ProteinMPNN, flow matching approaches)
• Experience with protein language models (e.g., ESM family)
• Published research in computational biology, protein design, or structural biology
• Experience training on proprietary or domain-specific biological datasets
• Familiarity with Ray for distributed computing
• Experience with Kubernetes (EKS) and cloud computing platforms (AWS)
• Knowledge of protein engineering, directed evolution, or structural biology wet lab techniques
• Experience working with agentic AI coding tools for fast, parallelized execution of modeling experiments
• Previous biotech/pharma industry experience

This Role Might Be Perfect For You If:

• You have deep experience training protein foundation models and want to apply that expertise to some of the richest proprietary experimental datasets in the field
• You're excited about pushing beyond public model performance by leveraging unique, large-scale in vivo screening data
• You thrive in high-ownership roles where you can drive research direction while collaborating with a tight-knit, world-class team
• You want your models to directly impact real drug discovery programs

If you're excited to train the next generation of protein foundation models on uniquely powerful experimental data, please reach out to careers@manifold.bio.

Base Salary Range: $140,000-225,000

This reflects the typical offer range for this role, based on experience, role scope, and internal equity. Final compensation decisions are made using a consistent leveling framework and consider the candidate’s experience, interview performance, and expected impact.

This role is eligible for:

• Annual performance-based target bonus
• Stock options
• Comprehensive medical, dental, and vision coverage
• 401(k) plan
• Flexible paid time off and holidays
• Perks including on-site gym, onsite lunch, and commuter support

Our compensation ranges are reviewed annually to ensure alignment with market trends and internal equity.

Position

Manifold Bio is seeking a talented Machine Learning Research Engineer to join our growing AI team. You will work closely with our research scientists to implement, scale, and optimize machine learning systems that power our de novo antibody design platform and advance our protein design capabilities. Your efforts will contribute to building production-ready ML infrastructure that enables breakthrough discoveries in protein therapeutics. You will be expected to take ownership of engineering challenges in our ML pipeline, from data processing and model training to deployment and monitoring, while collaborating closely with our research team to translate cutting-edge ideas into robust, scalable systems.

This is an on-site role and can be based in either Boston, Massachusetts or San Francisco, California. Please only apply if you reside in these cities or are open to relocate. 

Responsibilities

• Implement and optimize machine learning models for protein design
• Build and maintain scalable data processing pipelines for large-scale protein and molecular datasets
• Develop and deploy ML infrastructure for distributed training and inference across GPU clusters
• Collaborate with research scientists to translate experimental ML approaches into production-ready code
• Design and execute ML experiments with clear hypotheses and rigorous analysis
• Optimize model performance and computational efficiency for large-scale protein design tasks
• Build tools and utilities to support rapid prototyping and experimentation by the research team

Required Qualifications

• Bachelor's or Master's degree in Computer Science, Machine Learning, Computational Biology, or related field
• 2+ years of hands-on experience with PyTorch and/or JAX for deep learning applications
• Strong proficiency in Python scientific computing stack (NumPy, Pandas, scikit-learn)
• Experience with distributed computing and GPU optimization techniques
• Familiarity with protein structure analysis, computational biology, or analogous problems in natural sciences
• Understanding of modern deep learning architectures and optimization techniques
• Experience implementing research papers or translating ML approaches to production systems
• Proficiency with version control (Git), testing frameworks, and software engineering best practices
• Strong problem-solving skills and ability to work independently on technical challenges
• Excellent written and verbal communication skills for cross-functional collaboration

Preferred Qualifications

• Experience training LLMs or diffusion generative models
• Knowledge of cloud computing platforms (AWS, GCP) and containerization (Docker, Kubernetes)
• Background in computational biology, bioinformatics, or structural biology
• Experience with large-scale data engineering and ETL pipelines
• Familiarity with MLOps practices and model deployment frameworks

This Role Might Be Perfect For You If

You are passionate about leveraging state of the art machine learning approaches to solve challenging disease areas

• You enjoy translating research ideas into high impact, productionized, scalable code
• You have rich AI/ML experience and are looking to pivot into biotech

If you're excited to build scalable ML systems that revolutionize protein therapeutic discovery, please reach out to careers@manifold.bio.

Base Salary Range: $140,000-225,000

This reflects the typical offer range for this role, based on experience, role scope, and internal equity. Final compensation decisions are made using a consistent leveling framework and consider the candidate’s experience, interview performance, and expected impact.

This role is eligible for:

• Annual performance-based target bonus
• Stock options
• Comprehensive medical, dental, and vision coverage
• 401(k) plan
• Flexible paid time off and holidays
• Perks including on-site gym, onsite lunch, and commuter support

Our compensation ranges are reviewed annually to ensure alignment with market trends and internal equity.

Mission Summary:

At Motional, we're transforming how autonomous vehicles discover critical intelligence hidden within petabytes of multimodal sensor data. Our next-generation autonomous driving stack depends on finding the rare edge cases, long-tail scenarios, and model errors that matter most. Omnitag, our ML-powered multimodal data mining framework, is the engine that powers this discovery.

As a Machine Learning Engineer on the Data Mining team, your mission is to help build the "Brain" of this engine. You will work with state-of-the-art foundation models to extract insights from Motional’s driving data, working at the intersection of large-scale representation learning and data retrieval. By building smarter mining tools and efficient data pipelines, you will accelerate the model improvement lifecycle for teams working on post-training analysis, error diagnosis, and dataset curation.

What You'll Do:

• Build and Train ML Pipelines: Develop, train, and fine-tune machine learning models for multimodal sensor data (e.g., vision, LiDAR). Focus on implementing supervised and self-supervised learning approaches to improve data search and retrieval.
• Support Model Deployment: Implement scalable data preprocessing and augmentation pipelines. Assist in applying standard optimization techniques (e.g., batch inference, quantization) to ensure models run efficiently in production environments.
• Data Mining & Analysis: Help develop embedding-based search tools and "active learning" workflows to identify critical driving scenarios.
• Monitor Production Performance: Help build and maintain dashboards to monitor model health, data drift, and system performance. Identify regressions and assist in the operational support of our data mining services.
• Learn and Apply Best Practices: Follow software engineering standards (version control, CI/CD, unit testing) for ML code. Participate in code reviews and contribute to technical documentation.
• Collaborate Across Teams: Work closely with senior engineers and machine learning engineers to translate model prototypes into maintainable, scalable engineering solutions.
  
  

What We're Looking For (Must-Haves):

• BS or MS in Computer Science, Machine Learning, or a related field.
• Hands-on experience with PyTorch (preferred) or TensorFlow/JAX. You should be comfortable training models and evaluating them using standard metrics.
• Strong proficiency in Python with the ability to write clean, modular, and well-documented code.
• Working knowledge of version control, unit testing, and basic software design patterns.
• Experience working with large datasets, including proficiency in SQL and data libraries like Pandas and NumPy.
• A solid grasp of the full ML lifecycle, from data cleaning and feature engineering to validation and deployment basics.
• A proactive learner who thrives on constructive feedback and is eager to grow within a high-stakes engineering environment.
  
  

Bonus Points (Nice-to-Haves):

• MS/PhD in Computer Science, Machine Learning, or related field.
• Experience with agentic systems, autonomous reasoning, chain-of-thought models, or LLM-based planning.
• Background in autonomous driving, robotics, or real-time decision-making systems.
• Familiarity with multimodal learning, sensor fusion, or embodied AI.
• Experience building active learning loops, using the model to find the data that breaks the model.
• Experience with ML-based data mining, active learning, or contrastive learning.
• Knowledge of model serving tools (TF Serving, Triton, TorchServe) and MLOps platforms.
• Publication in top-tier conferences (e.g., ICCV, CVPR, ECCV)
  
  

We encourage a hybrid schedule with in-office time at one of our locations in Boston, Pittsburgh, or Las Vegas to support collaboration, or this role can be fully remote.

About the Position 

As a Data Scientist on the platform prediction team, you'll translate our probability of success predictions into measurable portfolio-level outcomes. You'll architect core systems — order management, execution simulation, portfolio construction, risk monitoring, and performance attribution — that let us rigorously evaluate signals from our AI-driven predictions in public and private equities and our internal portfolio.

This role sits at the intersection of quantitative finance, healthcare data, and AI-driven drug development. If you're excited about applying portfolio construction and risk management fundamentals to one of the most consequential prediction problems in healthcare, this is the role.No other company — hedge fund or pharma — has a technical data science position translating drug development experience into durable AI-native portfolio strategies. The skills you develop here — portfolio construction over assets with radically asymmetric risk profiles, clinical trial analytics, AI/ML in production, and risk management across multi-year horizons — can directly impact the delivery of new and effective therapeutics to patients by best aligning impactful medicines with economic incentives.

Responsibilities

• Work with the team to implement and maintain core portfolio engine: order management system, execution simulation layer, portfolio construction service, and performance tracking
• Design risk frameworks that quantify exposure across a portfolio of drug development bets with radically different risk profiles, timelines, and failure modes
• Run rigorous backtesting experiments with strict temporal constraints to evaluate Formation strategies against baseline approaches and measure marginal signal from new evidence sources
• Coordinate across the organization to integrate internal Formation data sources (clinical trial data, genomic evidence, real-world data) and proprietary tooling into portfolio analytics pipelines
• Work with product and engineering teams to build dashboards and reporting that communicate portfolio performance, risk metrics, and strategy comparisons to both technical and executive stakeholders
• Collaborate with the broader data science team to ensure portfolio-level evaluation feeds back into model improvement and evidence prioritization

About You 

Required Qualifications

• MS or PhD in a quantitative field (statistics, finance, physics, computational science, engineering, or related)
• 1-3 years in a quantitative research, data science, or analytics role — finance, healthcare, academic research, or consulting all count; substantive internships qualify
• Strong Python programming skills with experience in data-intensive workflows (pandas, numpy, scipy)
• Solid grasp of core portfolio construction and risk concepts: position sizing, rebalancing, Sharpe ratio, drawdown, volatility, benchmark comparison
• Demonstrated ability to work with messy, real-world datasets — comfortable with data wrangling, deduplication, and quality assessment
• Clear communicator who can present quantitative results to both technical peers and business stakeholders

Preferred Qualifications

• Experience with backtesting frameworks or portfolio simulation (vectorbt, Backtrader, or custom implementations)
• Exposure to healthcare, pharma, or biotech data (clinical trials, claims data, -omics, real-world evidence)
• Familiarity with alternative data in a research or investment context
• Experience with probability-of-success modeling, drug development decision analysis, or health economics
• Comfort with LLMs or AI/ML pipelines in a production or research setting
• Familiarity with dashboard/visualization tools (Streamlit, Plotly, Dash) and pipeline orchestration (Dagster, Airflow)

Healthcare OR finance domain knowledge is valued; both are not required.

Total Compensation Range: $154,500 - $202,000

Job Title:                Intern, Quantum Calibrations, Quantum Computing Services 
Job Type:                 Full-time, 3-6 months
Job Location:          Boston, MA 
Reports to:            VP of Quantum Computing Services                                                                                                                             

Summary:  

QuEra is seeking motivated interns to join our growing team focused on developing the calibration and performance characterization framework for QuEra’s neutral-atom quantum computers. As an intern, you will collaborate with physicists, software engineers, and hardware specialists to design benchmarking protocols, build physics-informed models of device behavior, and develop predictive tools for real-time qubit state evolution.

This internship offers a unique opportunity to gain hands-on experience at the interface of quantum hardware and data science, contributing to the development of calibration and performance characterization frameworks for scalable, reliable neutral-atom quantum computers.

This role is part of QuEra’s Quantum Computing Services team and is based at QuEra’s Boston headquarters. The team’s mission is to develop QuEra’s neutral-atom technology into cutting-edge quantum computing products and services.

Core Responsibilities

• Develop and test data analysis routines to fit, visualize, and interpret experimental data.
• Design and execute device benchmarking and characterization experiments.
• Build and apply Hamiltonian learning routines to infer effective system parameters from time-evolution data.
• Develop predictive models for key performance metrics (gate fidelity, coherence, readout error) using time-series calibration history and machine learning methods.
• Collaborate closely with physicists, engineers, and software developers to integrate your work into QuEra’s operational stack.

Qualifications

Required

• Foundational understanding of quantum mechanics and open quantum systems at the graduate level.
• Experience designing and analyzing experiments involving quantum or physical systems
• Strong programming skills in Python, including familiarity with libraries such as NumPy, SciPy, Pandas, and Matplotlib.
• Enjoys hands-on experimentation, iteration, and uncovering structure in data.
• Strong analytical, problem-solving, and communication skills.

Preferred

• Familiarity with quantum gate characterization protocols (randomized benchmarking, process tomography, GST) and error budget decomposition.
• Exposure to Hamiltonian learning, system identification, or quantum noise spectroscopy methods.
• Experience applying machine learning (Gaussian processes, time-series models, or neural networks) to physical or experimental data.
• Exposure to laboratory instrumentation, data acquisition, or experimental control systems.

Education & Experience

• Currently pursuing or recently completed a Master's or PhD in Physics, Applied Physics, Computer Science, Electrical Engineering, Applied Mathematics, or a related quantitative field.

QuEra is committed to cultivating a diverse work environment and is proud to be an equal opportunity employer. We highly value diversity in our current and future employees and do not discriminate (including in our hiring and promotion practices) based on race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status, or any other characteristic protected by law.

Role overview

The Manager, Data Science will lead an Inventory & Dealer Data Science team focused on developing, deploying, and optimizing machine learning models that power CarGurus’ products and business insights. Sitting within the broader Data Science organization, this team is responsible for modeling automotive marketplace dynamics and dealer behavior. The team owns Machine Learning solutions end-to-end across the ML lifecycle, from R&D to production. This leader is accountable for the team’s strategic direction and impactful delivery, while building a culture of innovation, technical excellence, and collaboration. The Manager partners with Product, Engineering, Analytics, and cross-functional stakeholders to ensure team efforts advance CarGurus’ goals in intelligence-powered products.

What you'll do

• Oversee the development, training, and evaluation of machine learning models covering Inventory and Dealer intelligence. Leverage modeling techniques including recommendations, demand forecasting, churn risk prediction, and valuation algorithms.
• Facilitate experimentation, A/B testing, and measurement of production models, ensuring robust evaluation of business impact
• Build and develop a high-performing team - providing regular coaching, feedback, and performance assessments; ensure equitable access to growth opportunities.
• Drive innovation, continuous improvement, and adoption of best-in-class ML and AI practices. Champion best practices across the organization.
• Participate in roadmap planning, aligning the team’s work with broader business priorities and long-term strategy. Flag resource constraints and escalate tradeoff decisions when needed.
• Act as a visible, accessible leader - communicating objectives, updates, risks, and wins to all stakeholders.
• Partner with peers in the Machine Learning Platform, Product and Data teams, and other stakeholders to facilitate effective cross-team working relationships and represent data science in cross-functional forums.

What you'll bring

• 5+ years of experience in a data science or machine learning engineering role
• 2+ years of people leadership experience
• Deep expertise in machine learning models and their application to classification, regression, ranking, and recommendation problems. Familiarity with standard evaluation metrics and statistical best practices.
• Experience deploying and owning models in production and a working understanding of MLOps practices.
• Strong working knowledge of Python development best practices and the Python ML ecosystem (e.g., scikit-learn, PyTorch, XGBoost, numpy, pandas).
• Experience with cloud platforms, particularly AWS, including deploying, monitoring, and troubleshooting ML models.
• Effective communication and facilitation abilities with both technical and non-technical stakeholders (including data scientists, product management, senior leadership, and engineers).
• Experience contributing to the technical direction for a team and a strategic mindset for prioritizing projects and aligning them with business goals.
• Knowledge of standard software development methodologies and a history of driving continuous improvement.
• Experience in a collaborative, cross-functional environment, with the ability to build effective working relationships and foster an inclusive team culture.

Summary

Klaviyo is looking for a Lead Data Science Analyst to join our GTM Strategic Analytics & Insights team. In this role, you will serve as a senior individual contributor at the intersection of advanced data science, AI/LLM-driven innovation, and Go-to-Market strategy. You will build and maintain sophisticated predictive and inferential models, conduct deep-dive statistical analyses, and develop AI-first solutions that unlock meaningful insights across the pre and post Sales Customer lifecycle.

The successful candidate will partner closely with GTM leadership to shape how Klaviyo understands, measures, and accelerates new business and customer outcomes; from pre-sales motion through onboarding, expansion and retention. You will operate with a strong bias toward AI-augmented workflows and bring a modern, LLM-aware approach to every analytical challenge.

The ideal candidate is intellectually curious, strategically minded, and energized by hard problems. They bring deep technical fluency across the full data science stack, a demonstrated ability to influence senior stakeholders through clear storytelling, and a genuine commitment to building AI-first solutions in a fast-moving SaaS environment.

How You Will Make a Difference

• Build and maintain advanced models: Build and maintain advanced predictive and time-series models: design, train, deploy, and monitor models across use cases such as demand forecasting, capacity planning, deal scoring, and customer propensity; incorporate seasonality, exogenous drivers, and backtesting frameworks to ensure accuracy and robustness
• Apply statistical rigor: lead deep-dive analyses leveraging regression, causal inference, hypothesis testing, correlation analysis, and other statistical methods to surface actionable signals from complex, large-scale datasets
• Develop AI/LLM-powered solutions: architect and implement AI-first analyses and tooling using large language models, prompt engineering, retrieval-augmented generation (RAG), and related techniques to automate insight generation, surface qualitative signals at scale, and augment team capabilities
• Own forecasting and decision systems: Own end-to-end forecasting and operational decision systems, including time-series demand forecasting, capacity planning models (e.g., Erlang-based staffing), and production pipelines that power GTM and Support planning workflows; ensure reliability, scalability, and business adoption of outputs
• Drive customer intelligence: develop and maintain prospect, deal health, archetype, and capacity models that inform GTM strategy, planning, and growth initiatives
• Define the measurement framework: identify, create, and steward benchmarks and metrics that meaningfully represent growth, engagement, and success outcomes
• Communicate with impact: distill complex analyses into clear, cohesive narratives with executive-ready materials that drive decisions at the senior leadership level
• Collaborate cross-functionally: partner with Systems & Engineering, GTM Operations, Rev Ops & Planning, Product, Business Intelligence, Data Science, and Finance to ensure analytical solutions are integrated, scalable, and trusted

Who You Are

• 6+ years of professional experience in an advanced analytics or data science role; SaaS experience strongly preferred
• Deep expertise in statistical inference and modeling, including supervised techniques (regression, classification, gradient boosting, decision trees) and unsupervised techniques (clustering, PCA, anomaly detection, topic modeling)
• Hands-on experience designing and deploying AI/LLM-based solutions, including prompt engineering, fine-tuning, RAG pipelines, or LLM-integrated analytics workflows; you approach new problems with an AI-first mindset
• Familiarity and experience with distributed coding projects, including using Git for code management.
• Advanced proficiency in Python (pandas, numpy, scikit-learn, xgboost, statsmodels, and LLM/AI libraries such as LangChain, OpenAI SDK, or HuggingFace) and SQL; working knowledge of DBT
• Own and scale end-to-end data pipelines, including orchestration with Airflow and transformation/modeling with dbt; design reliable, testable, and modular workflows that support production-grade analytics and machine learning use cases, with a focus on performance, data quality, and maintainability.
• Develop and iterate on time-series forecasting frameworks using approaches such as ARIMA/SARIMAX, ETS, MSTL, and machine learning-based models; evaluate performance through rigorous backtesting and continuously improve model accuracy and business applicability
• Experience building data visualizations and dashboards across platforms such as Tableau, ThoughtSpot, matplotlib, seaborn, plotly, or similar tooling.
• Strong project ownership: experienced operating to a roadmap, managing milestones and deliverables, and delivering high-quality work product in a timely manner
• Comfortable with autonomy and ambiguity, with a proactive orientation toward identifying and solving problems before they're fully defined
• Excellent written and verbal communication skills, including experience preparing materials for executive audiences

Summary

Klaviyo is looking for a Lead Data Science Analyst to join our GTM Strategic Analytics & Insights team. In this role, you will serve as a senior individual contributor at the intersection of advanced data science, AI/LLM-driven innovation, and Go-to-Market strategy. You will build and maintain sophisticated predictive and inferential models, conduct deep-dive statistical analyses, and develop AI-first solutions that unlock meaningful insights across the pre and post Sales Customer lifecycle.

The successful candidate will partner closely with GTM leadership to shape how Klaviyo understands, measures, and accelerates new business and customer outcomes; from pre-sales motion through onboarding, expansion and retention. You will operate with a strong bias toward AI-augmented workflows and bring a modern, LLM-aware approach to every analytical challenge.

The ideal candidate is intellectually curious, strategically minded, and energized by hard problems. They bring deep technical fluency across the full data science stack, a demonstrated ability to influence senior stakeholders through clear storytelling, and a genuine commitment to building AI-first solutions in a fast-moving SaaS environment.

How You Will Make a Difference

• Build and maintain advanced models: Build and maintain advanced predictive and time-series models: design, train, deploy, and monitor models across use cases such as demand forecasting, capacity planning, deal scoring, and customer propensity; incorporate seasonality, exogenous drivers, and backtesting frameworks to ensure accuracy and robustness
• Apply statistical rigor: lead deep-dive analyses leveraging regression, causal inference, hypothesis testing, correlation analysis, and other statistical methods to surface actionable signals from complex, large-scale datasets
• Develop AI/LLM-powered solutions: architect and implement AI-first analyses and tooling using large language models, prompt engineering, retrieval-augmented generation (RAG), and related techniques to automate insight generation, surface qualitative signals at scale, and augment team capabilities
• Own forecasting and decision systems: Own end-to-end forecasting and operational decision systems, including time-series demand forecasting, capacity planning models (e.g., Erlang-based staffing), and production pipelines that power GTM and Support planning workflows; ensure reliability, scalability, and business adoption of outputs
• Drive customer intelligence: develop and maintain prospect, deal health, archetype, and capacity models that inform GTM strategy, planning, and growth initiatives
• Define the measurement framework: identify, create, and steward benchmarks and metrics that meaningfully represent growth, engagement, and success outcomes
• Communicate with impact: distill complex analyses into clear, cohesive narratives with executive-ready materials that drive decisions at the senior leadership level
• Collaborate cross-functionally: partner with Systems & Engineering, GTM Operations, Rev Ops & Planning, Product, Business Intelligence, Data Science, and Finance to ensure analytical solutions are integrated, scalable, and trusted

Who You Are

• 6+ years of professional experience in an advanced analytics or data science role; SaaS experience strongly preferred
• Deep expertise in statistical inference and modeling, including supervised techniques (regression, classification, gradient boosting, decision trees) and unsupervised techniques (clustering, PCA, anomaly detection, topic modeling)
• Hands-on experience designing and deploying AI/LLM-based solutions, including prompt engineering, fine-tuning, RAG pipelines, or LLM-integrated analytics workflows; you approach new problems with an AI-first mindset
• Familiarity and experience with distributed coding projects, including using Git for code management.
• Advanced proficiency in Python (pandas, numpy, scikit-learn, xgboost, statsmodels, and LLM/AI libraries such as LangChain, OpenAI SDK, or HuggingFace) and SQL; working knowledge of DBT
• Own and scale end-to-end data pipelines, including orchestration with Airflow and transformation/modeling with dbt; design reliable, testable, and modular workflows that support production-grade analytics and machine learning use cases, with a focus on performance, data quality, and maintainability.
• Develop and iterate on time-series forecasting frameworks using approaches such as ARIMA/SARIMAX, ETS, MSTL, and machine learning-based models; evaluate performance through rigorous backtesting and continuously improve model accuracy and business applicability
• Experience building data visualizations and dashboards across platforms such as Tableau, ThoughtSpot, matplotlib, seaborn, plotly, or similar tooling.
• Strong project ownership: experienced operating to a roadmap, managing milestones and deliverables, and delivering high-quality work product in a timely manner
• Comfortable with autonomy and ambiguity, with a proactive orientation toward identifying and solving problems before they're fully defined
• Excellent written and verbal communication skills, including experience preparing materials for executive audiences

Quantum Calibration Engineer, Quantum Computing Services  
                                                                                         

Summary:   

QuEra is seeking Quantum Calibration Engineers to join a growing effort focused on developing, testing and deploying calibration and performance characterization software on QuEra’s neutral-atom quantum computers, translating experimental physics and data analysis into automation. This is a critical effort towards achieving programmability, reliability and peak performance of these machines for research, application development and commercial uses. Your work will directly shape how QuEra’s quantum computers are characterized, stabilized, and improved over time, laying the foundation for scalable, self-calibrating quantum platforms. 

In this role, you will work closely with physicists, software engineers, hardware specialists and actual quantum computers across QuEra’s global sites. You will be responsible for designing efficient measurements of key system parameters, developing robust data-analysis pipelines, and building tools that translate raw experimental results into actionable insights for machine optimization.  

This role is part of QuEra’s Quantum Computing Services team and is based at QuEra’s Boston headquarters. The team’s mission is to develop QuEra’s neutral-atom technology into cutting-edge quantum computing products and services. 

Core Responsibilities  

• Design data-efficient experimental routines, robust data analyses and troubleshooting workflows to extract key system parameters and insights.
• Develop, refine, and maintain calibration pipelines consisting of complex data flows.
• Develop robust data-fitting and optimization routines.
• Validate, document and track calibration results, ensuring reproducibility and traceability across different hardware and software generations, and across machine deployments. 

Qualifications  

Required  

• Strong proficiency in Python, including experience with data analysis and visualization libraries (e.g., NumPy, SciPy, Pandas, Matplotlib). 
• Hands-on experience working with real experimental or measurement data, including noise handling, curve fitting, and model validation. 
• Familiarity with optimization and regression techniques, both classical (e.g., least-squares, nonlinear fits) and statistical (e.g., Bayesian inference, parameter estimation). 
• Comfort working in a hardware-facing environment, integrating software with lab instruments, data acquisition systems, or experimental control frameworks. 
• Solid understanding of version control (Git) and structured software development practices (testing, modularity, documentation). 
• Strong problem-solving ability, scientific curiosity, and a willingness to learn new physical systems quickly.
• Eager to grow from a capable engineer into a domain expert in quantum device 
• Enjoys hands-on experimentation, iteration, and uncovering structure in data. 
• Comfortable working across abstraction layers, from analyzing raw voltage traces to designing experiment-level calibration strategies. 
• Collaborative and adaptable; able to operate in an interdisciplinary environment spanning science, software, and hardware teams.

Nice to have 

• Experience with machine learning or data-driven modeling (e.g., for pattern recognition, drift prediction, or parameter inference). 
• Exposure to experimental physics or optics in a lab environment.
• Theory and practice of control systems.
• Familiarity with Kubernetes, Docker, or distributed analysis systems for scaling calibration and data pipelines. 
• Knowledge of signal processing and time-series analysis for extracting trends or correlations in data.
• Proficiency in C or C++.

Education & Experience  

• Bachelor’s or Master’s degree in Physics, Electrical Engineering, Computer Science, Applied Mathematics, or a related quantitative field. 
• Candidates with diverse or nontraditional backgrounds who demonstrate strong programming and analytical skills are encouraged to apply. 
• Experience or exposure to experimental research, data-driven system optimization, or hardware-software integration (through research, internships, or personal projects) is a strong plus. 

The approximate base salary range for this position is $120,000 - $180,000.

We consistently monitor external market data and update base salary ranges accordingly.  We determine base compensation decisions on several factors, including as geographic placement, role-specific knowledge, skills, and/or experience.  In addition to our base salary offerings, we also provide equity grants for all new hires.

 #LI-DA1

QuEra is committed to cultivating a diverse work environment and is proud to be an equal opportunity employer. We highly value diversity in our current and future employees and do not discriminate (including in our hiring and promotion practices) based on race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status, or any other characteristic protected by law.

About the Role:

We are seeking a high-impact Machine Learning Developer/Engineer to join our integrated discovery team. In this role, you will be the algorithmic engine of our programs, developing and deploying state-of-the-art ML models to generate the quantitative predictions necessary to drive drug discovery. Beyond technical mastery, you will serve as a core scientific partner to medicinal chemists, computational chemists, and biologists, building models that move programs efficiently toward Go/No-Go decision points and candidate nomination.

Key Responsibilities:

• Implement algorithms for hit identification through virtual screening and other high throughput computational methods as part of a cross-functional team
• Adapt and implement cutting-edge ML architectures for co-folding to augment our extensive internal structural biology expertise and capabilities
• Design and deploy active learning frameworks that utilize experimental assay results to iteratively improve model performance and reduce the number of "Design-Make-Test-Analyze" cycles leveraging state-of-the-art de novo design, ADMET predictions, and affinity predictions

Required Qualifications:

• Ph.D. preferred in Computer Science, Machine Learning, Engineering or a related field, or BS/MS + seasoned experience
• Proven experience with protein-ligand co-folding algorithms (e.g., Boltz, AlphaFold, OpenFold, etc) and the ability to integrate these structural insights into broader ML discovery pipelines.
• Advanced proficiency in Python and deep learning libraries (e.g., PyTorch, TensorFlow) is required. You must be capable of building and maintaining production-quality code and data pipelines.
• Exceptional ability to communicate the "why" behind a design to a diverse scientific audience.

Preferred Qualifications:

• Expert-level knowledge of deep learning frameworks, specifically for affinity prediction, ADMET modeling, and the application of LLMs in a biological or chemical context
• Expertise fine-tuning existing models with internally generated structural biology and biology data
• Experience deploying ML/AI algorithms for use by a cross-functional scientific audience
• 1-4+ years of experience in a biotech or pharma setting

Skills & Competencies:

• A demonstrated track record of innovation in the ML/AI space, including developing and validating new architectures or novel applications of existing models to solve complex drug discovery problems including tools for hit identification (virtual screening, HTS)
• Expert level use of protein-ligand co-folding algorithms to small molecule drug discovery ML/AI tools (AlphaFold, Boltz, OpenFold)   
• Experience writing production-level code for ML tasks:
•  Knowledge of key scientific packages (RDKit, scikit-learn, numpy, pandas, pytorch, deepchem, polars, PyG/DGL):
• Write robust, testable, and version-controlled code that adheres to CI/CD and data governance best practices.
• Value clarity, documentation, and structured thinking, especially when working with complex data
• Knowledge of containerization technologies (Docker, Kubernetes) and cloud deployment at scale

About the Role:

We are seeking a high-impact Machine Learning Scientist to join our integrated discovery team. In this role, leading from the bench, you will enable the development, validation and deployment of state-of-the-art ML models to generate the quantitative predictions necessary to drive drug discovery. Beyond technical mastery, you will serve as a core strategic partner to medicinal chemists, computational chemists, and biologists, building models that move programs efficiently toward Go/No-Go decision points and candidate nomination.  This role may be responsible for the management and development of individual team members.

Key Responsibilities:

• Lead the application of Large Language Models (LLMs), co-folding algorithms, and generative chemistry techniques to design novel chemical matter aimed at hitting key program milestones, such as establishing selectivity windows and optimizing drug-like properties
• ML lead on project teams, collaborating intimately with medicinal chemists to refine SAR and with structural biologists to integrate co-folding and structure-based insights into ML workflows
• Data-Driven Decision Making: Synthesize complex ML outputs into clear, actionable design hypotheses that cross-functional scientific stakeholders can use to make high-stakes program decisions
• May be responsible for management and development of internal team members

Required Qualifications:

• Ph.D. in Computational Chemistry, Computer Science, Machine Learning, or a related field
• Demonstrated expertise in statistics, probability theory, data modeling, machine learning algorithms, and the languages used to implement analytics solutions
• 4-7+ years of experience in a biotech or pharma setting performing ML support for small molecule drug discovery with clear evidence of impact on drug discovery programs
• Demonstrated success in a cross-functional environment, including biologists, structural biologists, medicinal and computational chemists, with specific examples of computational designs/algorithms/models that directly led to the achievement of program milestones
• Expert proficiency in Python and deep learning libraries (e.g., PyTorch, TensorFlow) is required. You must be able to build and maintain production-quality code and data pipelines

Preferred Qualifications:

• Proven experience with protein-ligand co-folding models (e.g.,Boltz, OpenFold, AlphaFold, etc) and the ability to integrate these structural insights into broader ML discovery pipelines
• Expertise fine-tuning existing models with internally generated structural biology and biology data
• Expert-level knowledge of deep learning frameworks, specifically for affinity prediction, ADMET modeling, and the application of LLMs in a biological or chemical context

Skills & Competencies:

• A demonstrated track record of innovation in the ML/AI space, including developing and validating new architectures or novel applications of existing models to solve complex drug discovery problems
• Demonstrated expertise using small molecule drug discovery ML/AI tools (AlphaFold, Boltz, OpenFold, ChemProp, DeepChem, Reinvent, etc)  
• Expert level coding for ML tasks including knowledge of key packages (RDKit, scikit-learn, numpy, pandas, pytorch, DeepChem, polars, PyG/DGL).
• Strong interpersonal and communications skills in the "why" behind a design to a diverse scientific audience
• Experience mentoring and developing teams