• Help design, train, with a focus towards deploying onboard machine learning models that improve the performance/latency of the Kodiak autonomy stack which includes quantization, pruning, converting to ONNX/TensorRT, custom GPU kernels and profiling.
• Help identify and achieve parity between onboard and deployed offboard models using validation on HIL 
• Collaborate with cross-functional teams including perception, planning, simulation, and autonomy infrastructure to integrate ML models into production systems.
• Analyze real-world driving logs to identify edge cases and improve model robustness and safety.
• Use profiling tooling to additionally help improve the train time for models by identifying and removing bottlenecks.
• Contribute to the development of scalable AI infrastructure that supports continuous learning and deployment across Kodiak’s fleet.

• MS or PhD in Computer Science, Robotics, Machine Learning, or a related technical field, or equivalent practical experience.
• 6+ years experience deploying machine learning or deep learning systems in production environments.
• Proficiency in Python, C++ and modern ML frameworks such as PyTorch or TensorFlow.
• Experience working with large-scale distributed training systems.
• Solid understanding of optimization, and machine learning fundamentals.
• Strong software engineering fundamentals including testing, debugging, and system design.
• Ability to work collaboratively across teams to solve complex autonomy and robotics challenges.

What we offer:

• Competitive compensation package including equity and annual bonuses
• Excellent Medical, Dental, and Vision plans through Kaiser Permanente, Cigna, and  MetLife (including a medical plan with infertility benefits)
• MetLife Legal Services, Identity & Fraud Protection, Hospital Indemnity Insurance, Accident Insurance, & Critical Illness Insurance
• Flexible PTO, 10 paid holidays, and generous parental leave policies
• Our office is centrally located in Mountain View, CA
• Office perks: dog-friendly, free catered lunch, a fully stocked kitchen, and free EV charging
• Long Term Disability, Short Term Disability, Life Insurance
• Wellbeing Benefits - Headspace through Cigna, Calm through Kaiser, One Medical, Gympass, Spring Health through Cigna, Rula (mental health navigation) 
• Fidelity 401(k)
• Commuter, FSA, Dependent Care FSA, HSA
• Various incentive programs (referral bonuses, patent bonuses, etc.)

We are looking for a Senior Software Engineer specializing in Machine Learning to join our Revenue ML team at Discord. This team partners with our revenue product groups, focusing on both consumer revenue and our emerging Ads initiative. This role will specifically contribute to our Ads ML efforts, helping to build and scale ML capabilities in areas such as ads measurement, targeting, and delivery ranking.

As part of this team, you will play a critical role in developing foundational ML models that enhance ad relevance, optimize performance, and drive revenue. This is a unique opportunity to work on an early-stage Ads ML platform and have a direct impact on the business's success. Our tech stack includes Python, ML frameworks like PyTorch and TensorFlow, large-scale data infrastructure, and real-time ad-serving technologies.

What You'll Be Doing:

• Design, develop, and deploy machine learning models for ads targeting and ranking.
• Develop sophisticated ML solutions such as identity graph to enhance ad targeting.
• Build and optimize ad ranking models to serve the most effective ads based on campaign objectives (e.g., app installs, link click).
• Improve ads targeting and ranking by leveraging both on-platform and off-platform signals.
• Collaborate cross-functionally with product, engineering, and business teams to define and execute on the Ads ML roadmap.
• Scale our ML infrastructure to support an increasing number of concurrent ad campaigns while ensuring low-latency decision-making.
• Drive research and implementation of state-of-the-art ML techniques in the field of online advertising.

What You Should Have:

• 5+ years of experience as a Machine Learning Engineer or Data Scientist.
• 3+ years of experience specifically in Ads ML (ads ranking, personalization, optimization, privacy-compliant user modeling, targeting, or measurement).
• Strong proficiency in Python and familiarity with deep learning frameworks such as PyTorch or TensorFlow.
• Experience with applied deep learning (e.g transformers, embedding models).
• Proven track record of designing, implementing, and scaling ML-driven ad systems in real-world applications.
• Experience working with real-time ML inference, A/B testing, and optimization frameworks.
• Experience translating ML evaluation results and performance metrics into actionable product roadmap items.
• Ability to connect business objectives to ML solutions, with the flexibility to shift focus toward the highest-impact problems as priorities evolve.

Bonus Skills:

• Strong understanding of performance advertising and how ML impacts revenue and advertiser retention.
• Knowledge of ad tech industry standards and ads ecosystem including targeting, retrieval, ranking, pacing, frequency, auction, etc.
• Experience with large-scale recommendation systems.
• Experience with large-scale data infrastructure and distributed computing

The US base salary range for this full-time position is $220,000 to $247,000 + equity + benefits. Our salary ranges are determined by role and level. Within the range, individual pay is determined by additional factors, including job-related skills, experience, and relevant education or training. Please note that the compensation details listed in US role postings reflect the base salary only, and do not include equity, or benefits.

What we offer:

• Competitive compensation package including equity and annual bonuses
• Excellent Medical, Dental, and Vision plans through Kaiser Permanente, Cigna, and  MetLife (including a medical plan with infertility benefits)
• MetLife Legal Services, Identity & Fraud Protection, Hospital Indemnity Insurance, Accident Insurance, & Critical Illness Insurance
• Flexible PTO, 10 paid holidays, and generous parental leave policies
• Our office is centrally located in Mountain View, CA
• Office perks: dog-friendly, free catered lunch, a fully stocked kitchen, and free EV charging
• Long Term Disability, Short Term Disability, Life Insurance
• Wellbeing Benefits - Headspace through Cigna, Calm through Kaiser, One Medical, Gympass, Spring Health through Cigna, Rula (mental health navigation) 
• Fidelity 401(k)
• Commuter, FSA, Dependent Care FSA, HSA
• Various incentive programs (referral bonuses, patent bonuses, etc.)

About The Role

The AI team is building cutting-edge solutions that bring the power of AI directly to edge devices while seamlessly integrating with cloud infrastructure. We are looking for a Software Engineer to design and develop high-performance, scalable services to support AI workloads across edge and cloud environments.

What You Might Do

• Contribute to design, build, and maintain services that power AI-driven applications, ensuring scalability and performance.
• Develop APIs and microservices that facilitate seamless integration between cloud-based AI models and edge devices.
• Optimize data pipelines and storage solutions for real-time AI inference and processing.
• Work closely with AI researchers, infrastructure engineers, and frontend developers to deliver end-to-end AI-driven solutions.
• Build and optimize an agent orchestration runtime that enables tool use, memory management, and multi-step reasoning across LLMs, APIs, and edge-connected systems.
• Support the implementation of security and privacy best practices for distributed AI systems.

Essential Qualifications

• Requires at least Masters in Computer Science, Engineering, Data Science, Artificial Intelligence or related fields.
• 1+ years of experience in backend software development, with strong proficiency in Python, Java, and C++. 
• Working knowledge in LLM integration into multi-agent systems with strong understanding of agent orchestration, tool use, and memory/context management.
• Comfortable tinkering with LLMs via prompting and experienced with tools use, task decomposition, and structured outputs.
• Familiarity with database management systems (SQL and NoSQL) and data streaming technologies.
• Strong problem-solving skills and ability to work in a fast-paced, agile environment.

Preferred Skills

• Experience with AI model deployment and integration into backend services.
• Exposure to cloud-edge hybrid architectures and distributed AI workloads.
• Familiarity with on-device AI frameworks such as PyTorch, TensorFlow Lite, ONNX Runtime, or CoreML.

Salary Range: $127,000 - $175,000

About the Role
We are looking for an AI Scientist to join our team at the intersection of machine learning and single-cell biology. This role will focus on developing and applying foundation models to large-scale perturb-seq and single-cell datasets to uncover new biological mechanisms and therapeutic opportunities. You will help design scalable ML architectures that can extract insight from high-dimensional perturbation data and integrate across multi-omic modalities.

Key Responsibilities

• Design, train, and refine foundation models tailored to perturb-seq and other single-cell datasets.
  Build pipelines for preprocessing, normalization, and integration of large-scale single-cell and perturbation data.
• Apply ML/AI approaches to model cellular responses to perturbations and identify causal biological insights.
• Collaborate with experimental biologists to align computational methods with experimental design and discovery goals.
• Develop multimodal integration approaches across transcriptomic, epigenomic, and proteomic single-cell readouts.
• Publish methods and results in top scientific journals and present at conferences.
  
  

Qualifications

• PhD or equivalent in Computational Biology, Computer Science, Bioinformatics, or related discipline.
• Strong expertise in single-cell data analysis, particularly perturb-seq 
• Hands-on experience with foundation models or large-scale self-supervised learning (e.g., transformers, variational autoencoders).
• Strong coding skills in Python, ML frameworks (PyTorch/TensorFlow), and single-cell analysis tools (Scanpy, Seurat).
• Experience in developing scalable ML methods for large biological datasets.
  
  

Preferred

• Background in causal inference, generative models, or representation learning for biological systems.
• Familiarity with multi-omics integration and cross-modal foundation models.
• Track record of impactful publications in computational biology, machine learning, or related fields.

Compensation

We offer a competitive compensation and benefits package, seeking to provide an open, flexible, and friendly work environment to empower employees and provide them with a platform to develop their long-term careers. A Summary of Benefits is available for all applicants. We offer a competitive package that includes base salary, bonus, and equity. The base pay range for this position is expected to be $150,000 - $240,000 annually; however, the base pay offered may vary depending on the market, job-related knowledge, skills and capabilities, and experience.

Xaira Therapeutics an equal-opportunity employer. We believe that our strength is in our differences. Our goal to build a diverse and inclusive team began on day one, and it will never end.

TO ALL RECRUITMENT AGENCIES: Xaira Therapeutics does not accept agency resumes. Please do not forward resumes to our jobs alias or employees. Xaira Therapeutics is not responsible for any fees related to unsolicited resumes.

About the Role
We are seeking a Biomedical AI Engineer to design and deploy agentic AI systems that can reason over biomedical knowledge, plan multi-step analyses, and autonomously integrate diverse scientific data sources to accelerate therapeutic discovery.

This role goes beyond traditional LLM + RAG pipelines. You will build AI agents that can retrieve, synthesize, evaluate, and act on biomedical information across literature, omics datasets, and clinical records operating as intelligent systems embedded within discovery workflows.

Key Responsibilities

• Design and implement agentic AI architectures capable of multi-step reasoning, planning, and tool use in biomedical contexts.
• Develop LLM-based agents that dynamically retrieve, rank, and synthesize knowledge from scientific literature, knowledge graphs, omics datasets, and clinical data.
• Build memory and feedback mechanisms that allow agents to refine hypotheses, update context, and adapt to evolving data.
• Integrate external tools (analysis pipelines, simulation engines, statistical models, databases) into autonomous agent workflows.
• Architect scalable systems that combine structured and unstructured biomedical data within production-grade AI environments.
• Collaborate closely with computational biologists, translational scientists, and software engineers to embed agentic systems into real-world discovery pipelines.
• Evaluate robustness, reliability, and interpretability of autonomous systems in high-stakes biomedical settings.

Qualifications

• Degree in Computer Science, Machine Learning, Computational Biology, Biomedical Informatics, or related field.
• Strong hands-on experience with large language models, including fine-tuning, alignment, and structured prompting.
• Experience building agent-based systems or complex LLM orchestration frameworks (e.g., multi-agent systems, tool-using LLMs, planning modules).
• Proficiency in Python and modern ML frameworks (PyTorch, JAX, TensorFlow).
• Experience working with large-scale biomedical datasets (genomics, transcriptomics, clinical records, or scientific corpora).

Preferred

• Experience designing autonomous research assistants or AI systems that perform multi-step scientific reasoning.
• Familiarity with knowledge graphs, hybrid symbolic-neural systems, or multimodal foundation models.
• Understanding of privacy, security, and regulatory considerations in healthcare AI.

Compensation

We offer a competitive compensation and benefits package, seeking to provide an open, flexible, and friendly work environment to empower employees and provide them with a platform to develop their long-term careers. A Summary of Benefits is available for all applicants. We offer a competitive package that includes base salary, bonus, and equity. The base pay range for this position is expected to be $190,000 - $280,000 annually; however, the base pay offered may vary depending on the market, job-related knowledge, skills and capabilities, and experience.

Xaira Therapeutics an equal-opportunity employer. We believe that our strength is in our differences. Our goal to build a diverse and inclusive team began on day one, and it will never end.

TO ALL RECRUITMENT AGENCIES: Xaira Therapeutics does not accept agency resumes. Please do not forward resumes to our jobs alias or employees. Xaira Therapeutics is not responsible for any fees related to unsolicited resumes.

Location: San Francisco, CA (Hybrid) 

What is Verse? 

Energy markets are more volatile than ever. Rapid electrification and the rise of AI are driving unprecedented demand for power, while energy costs continue to rise across the globe. For the world’s largest energy buyers, managing energy has never been more complex or more critical.

Verse helps these organizations manage complex power portfolios with confidence by unifying energy data, planning, forecasting, and operations in one tool. Our Energy Cost Intelligence platform, Aria, brings together energy, finance, and operations teams with real-time, finance-ready intelligence—replacing spreadsheets and consultants with precision across the entire energy lifecycle. Built by an expert team of energy buyers, data scientists, and engineers, Verse enables faster, smarter energy decisions that reduce risk and lower energy costs.

The Role

Verse is seeking a Data Scientist to join our Data Science Team. In this role, you will lead the development and deployment of advanced data-driven solutions across a range of applications, including electricity markets, renewable procurement, and energy risk management. You will shape the machine learning and data modeling foundations that Verse's software is built on. For example, you might spend a cycle deploying electricity market price forecasting pipelines for new regions, developing solar production anomaly detection models, or creating scalable tools for benchmarking energy project financial performance.

Key Responsibilities

• Lead End-to-End Data Science Projects: Own and drive projects from problem definition through scoping, modeling, validation, and production deployment. Translate business problems into scalable, high-impact modeling solutions.
• Statistical & Machine Learning Modeling: Design, develop, and refine statistical and machine learning models (e.g., time series forecasting, probabilistic models) to support decision-making and enhance product capabilities.
• Analytics Engineering & Data Modeling: Perform complex data transformations and develop well-structured data models. Translate business and analytical requirements into scalable, tested, and well-documented datasets.
• Software Development & Productionization: Write clean, efficient, and maintainable Python code. Contribute to integrating models into production systems in a cloud-based environment while leveraging AI coding tools to accelerate development.
• MLOps: Contribute to Verse’s machine learning modeling infrastructure to support scaling of ML models and improving reliability, monitoring, and performance in production.
• Cross-Functional Collaboration: Partner with product, engineering, and business stakeholders to ensure models and insights are aligned with user needs and effectively integrated into workflows.

What We're Looking For (Minimum Qualifications)

• Master’s degree in Computer Science, Statistics, Engineering, Applied Mathematics, or a related quantitative field and 2+ years of professional experience in data science or machine learning; or
• Bachelor’s degree and 4+ years of professional experience in data science or machine learning
• Strong foundation in statistical modeling and machine learning, including time series forecasting and model evaluation
• Experience deploying and maintaining models in cloud-based environments (e.g., AWS, GCP, or Azure) using MLOps practices
• Strong Python expertise, including experience with scientific computing and ML libraries (e.g., NumPy, pandas, scikit-learn, PyTorch, TensorFlow)
• Hands-on experience in orchestrating complex data transformations (e.g. Airflow, Dagster, dbt)
• Strong software engineering practices (version control, testing, code reviews, CI/CD)
• Strong communication skills, with the ability to explain technical concepts to non-technical stakeholders

What Will Make You Standout (Preferred Qualifications)

• Experience in energy, climate tech, or related domains (not required)
• Familiarity with optimization methods or operations research
• Experience developing probabilistic forecasting models and quantifying uncertainty
• PhD in a quantitative field

What Makes Verse a Great Place to Work? 

• Lead with Empathy: We lift each other up with humility and kindness, always putting colleagues and customers first
• Be Honest & Transparent: We prioritize effective communication to build trust with our team, customers, and stakeholders
• Move with Balance & Precision: We believe speed and perseverance must be accompanied by thoughtfulness and reflection
• Leave the World a Better Place: We are passionate about our mission, and we strive to create a sustainable world for future generations

Base Pay Range

$146,000 - $172,000

This is the estimated base salary range for this position, which does not include the value of benefits or a potential equity grant. A wide range of factors are considered in making compensation decisions, including but not limited to skill sets, market conditions, experience and training, licensure and certifications, and business and organizational needs.

Benefits and Employee Perks 

• Competitive compensation and equity grant at a high-growth start up 
• Comprehensive benefits package including medical, dental and vision insurance, and 401k 
• Flexible hours and unlimited PTO 
• Diverse and inclusive working environment 

Verse is an equal opportunity employer. All applicants and employees are considered for hire, promotion, and compensation without regard to race, color, religion, sex, national origin, age, disability, sexual orientation, marital or familial status.

About the Role

Our Engineering team creates innovative product experiences that power personalized marketing at massive scale. We develop cutting-edge applications and systems that process 100s of billions of messages and events per year, enabling marketers to connect effectively with hundreds of millions of consumers. Joining our team offers a high-growth career opportunity to work with some of the world’s most talented engineers in a high-performance and high-impact culture.

As a Senior Software Engineer in our Intelligent Messaging, you will pioneer the future of personalized 1:1 marketing by architecting robust, scalable backend systems powered by advanced AI and LLMs, including cutting-edge Generative AI and GPT technologies. Collaborating with top-tier engineers, data scientists, product managers, and designers, you’ll craft the foundation and reshape the platform of multi-channel messaging experimentation to support innovative, AI-driven applications that empower marketers to forge deep, meaningful connections with consumers at scale. Your impactful work will drive the delivery of billions of tailored messages, transforming consumer shopping experiences and fueling success for the world’s leading brands.

What You’ll Accomplish

• Develop and maintain scalable backend systems for our customer-facing products, ensuring high performance and reliability.
• Collaborate with cross-functional teams to build applications that support multi-channel communications for marketers.
• Implement and optimize systems that process and deliver billions of messages daily.
• Improve code quality through code reviews, testing, and advocating for best practices.
• Identify and address technical debt to ensure the long-term health of our codebase.
• Drive architectural design and technical decisions, and stay current with emerging technologies to enhance our products.

Your Expertise

• 5+ years of professional experience in software development focusing on backend systems.
• Proficiency in Java, Python, or Go, with a strong understanding of object-oriented programming.
• Experience building and maintaining scalable, high-performance applications.
• Proven ability to collaborate effectively with cross-functional teams.
• Solid understanding of software development best practices, including code reviews, writing tests, and continuous integration.

Nice to Haves

• Experience with service-oriented architecture and distributed systems.
• Familiarity with AWS services and cloud infrastructure.
• Knowledge of databases such as DynamoDB, Postgres, or Redis.
• Experience with messaging systems or streaming platforms (e.g., Kafka, Pulsar).
• Familiarity with frontend development with React and TypeScript.
• Experience with DevOps practices and tools such as Docker and Kubernetes.

What We Use

• Our infrastructure runs primarily in Kubernetes hosted in AWS’s EKS.
• Infrastructure tooling includes Istio, Datadog,Terraform, CloudFlare, and Helm.
• Our backend is Java / Spring Boot microservices, built with Gradle, coupled with things like DynamoDB, Kinesis, AirFlow, Postgres, Planetscale, and Redis, hosted via AWS.
• Our frontend is built with React and TypeScript, and uses best practices like GraphQL, Storybook, Radix UI, Vite, esbuild, and PlaywrightOur automation is driven by custom and open source machine learning models, lots of data and built with Python, Metaflow, HuggingFace 🤗, PyTorch, TensorFlow, and Pandas.

You'll get competitive perks and benefits, from health & wellness to equity, to help you bring your best self to work.

For US based applicants:

• The US base salary range for this full-time position is $190,000 - $230,000 annually + equity + benefits
• Our salary ranges are determined by role, level and location

#LI-EZ1

By applying for this position, your data will be processed as per Attentive's Privacy Policy.

• Design and develop multimodal transformer architectures that fuse camera, LiDAR, and radar into unified representations
• Research and implement cross-modal attention mechanisms, token fusion strategies, and efficient multi-stream tokenization
• Build scalable training pipelines for large-scale multimodal transformers across massive real-world datasets
• Explore self-supervised and contrastive pretraining objectives that learn transferable multimodal representations
• Optimize transformer models for real-time inference under latency and compute constraints

• BS, MS, or PhD in AI, Computer Science, or a related field
• 4+ years experience with transformer architectures, particularly in multimodal or multi-stream settings
• Familiarity with cross-attention, token fusion, or modality alignment techniques
• Proficiency in Python and deep learning frameworks like PyTorch or TensorFlow
• Strong understanding of scalable training for large models, including distributed training and mixed-precision optimization
• Passion for building AI that reasons over the full breadth of sensory input to operate safely in the real world

What we offer:

• Competitive compensation package including equity and annual bonuses
• Excellent Medical, Dental, and Vision plans through Kaiser Permanente, Cigna, and  MetLife (including a medical plan with infertility benefits)
• MetLife Legal Services, Identity & Fraud Protection, Hospital Indemnity Insurance, Accident Insurance, & Critical Illness Insurance
• Flexible PTO, 10 paid holidays, and generous parental leave policies
• Our office is centrally located in Mountain View, CA
• Office perks: dog-friendly, free catered lunch, a fully stocked kitchen, and free EV charging
• Long Term Disability, Short Term Disability, Life Insurance
• Wellbeing Benefits - Headspace through Cigna, Calm through Kaiser, One Medical, Gympass, Spring Health through Cigna, Rula (mental health navigation) 
• Fidelity 401(k)
• Commuter, FSA, Dependent Care FSA, HSA
• Various incentive programs (referral bonuses, patent bonuses, etc.)

What You’ll Do
The Field Engineering team at Weights & Biases plays a vital role in ensuring customer success and adoption of our platform. As part of this team, we partner with Sales, Support, Product, and Engineering to lead technical success after the sales process. We work closely with some of the most advanced AI teams in the world, helping them build, optimize, and scale their ML and GenAI workflows across industries such as computer vision, robotics, natural language processing, and large language models (LLMs).

About the Role

We’re hiring a Post-Sales AI Solutions Engineer (AISE), Scale Customer Success to help customers successfully implement and scale AI/ML workflows and GenAI/agentic applications on Weights & Biases. You will design and deliver technical enablement and adoption programs that reach many customers at once, create reusable assets that improve self-serve success, and use product signals and feedback loops to continuously improve outcomes.

Key Responsibilities 

1. Run 1-to-many onboarding and enablement programs
   Own and deliver scalable onboarding and adoption motions (webinars, cohort sessions, group training, and office hours) that help customers get to value quickly and consistently.
2. Build reusable technical assets that drive self-serve success
   Create and maintain playbooks, reference architectures, templates, sample code/notebooks, and troubleshooting guides that standardize best practices and reduce repeated 1:1 work.
3. Operate the scaled motion using signals and feedback loops
   Use product usage signals and customer patterns to segment and trigger the right interventions, track program impact (activation, time-to-first-value, feature adoption), and feed recurring insights back to Support, Product, and Field Engineering to continuously improve the scaled journey.

Who You Are

• 3–5 years of relevant experience in a similar role
• Strong programming proficiency in Python
• Experience with deep learning frameworks (TensorFlow/Keras, PyTorch Lightning) and tools (e.g., Streamlit, LangChain)
• Familiarity with cloud platforms (AWS, GCP, Azure)
• Excellent communication and presentation skills, both written and verbal
• Organized and outcomes-driven: you can run programs, measure impact, and iterate.

Preferred

• Experience with GenAI and LLMs
• Proficiency with HuggingFace, Fastai, scikit-learn, XGBoost, LightGBM, or Ray
• Experience with hyperparameter optimization solutions
• Background in data engineering, MLOps, or LLMOps, with tools such as Docker and Kubernetes
• Familiarity with data pipeline tools

Wondering if you’re a good fit?
We believe in investing in our people, and value candidates who can bring their own diversified experiences to our teams – even if you aren't a 100% skill or experience match. Here are a few qualities we’ve found compatible with our team. If some of this describes you, we’d love to talk.

• You love collaborating with top AI teams to solve real-world ML and GenAI challenges
• You’re curious about how to optimize AI workflows at scale
• You’re an expert in Python and ML tooling, with a passion for enabling customer success

Why Us?
We work hard, have fun, and move fast! We’re in an exciting stage of hyper-growth that you will not want to miss out on. We’re not afraid of a little chaos, and we’re constantly learning. Our team cares deeply about how we build our product and how we work together, which is represented through our core values:

• Be Curious at Your Core
• Act Like an Owner
• Empower Employees
• Deliver Best-in-Class Client Experiences
• Achieve More Together

We support and encourage an entrepreneurial outlook and independent thinking. We foster an environment that encourages collaboration and provides the opportunity to develop innovative solutions to complex problems. As we get set for takeoff, the growth opportunities within the organization are constantly expanding. You will be surrounded by some of the best talent in the industry, who will want to learn from you, too. Come join us!

The base salary range for this role is $139,000 to $204,000. The starting salary will be determined based on job-related knowledge, skills, experience, and market location. We strive for both market alignment and internal equity when determining compensation. In addition to base salary, our total rewards package includes a discretionary bonus, equity awards, and a comprehensive benefits program (all based on eligibility).

At Lyft, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive.

ML & Data Science is at the heart of Lyft's products and decision-making. ML and Data Science professionals at Lyft operate in dynamic environments, moving quickly to build the world's best transportation solutions. We tackle a wide range of challenges—from shaping long-term business strategy with data, to making critical short-term decisions, to developing algorithms and models that power both internal systems and customer-facing products.

Lyft Ads is Lyft's advertising platform, connecting brands with high-intent audiences across the rideshare journey. Our offerings span in-app ad formats, in-car tablet experiences, bikeshare and station sponsorships, and programmatic integrations—enabling advertisers to reach riders at key moments of engagement. These products power high-impact use cases across brand awareness, performance marketing, audience targeting, and measurement for enterprise advertisers.

We are seeking an Algorithms Science Manager to lead a team of Data Scientists, Applied Scientists, and Machine Learning Engineers building the algorithmic backbone of Lyft Media. In this role, you will shape the vision, define the roadmap, and drive execution for projects that improve ad relevance, optimize yield, enhance targeting and measurement, and deliver measurable value to our advertising partners. You'll collaborate closely with Product, Engineering, Design, and Sales teams to build models, experimentation frameworks, and production ML systems that inform strategy and power product innovation.

This is a high-visibility, high-impact role with direct influence on Lyft's advertising platform and revenue growth. The ideal candidate will bring deep expertise in algorithm development, machine learning, causal inference, and experimentation; strong business acumen in ads or marketplace contexts; and a proven track record of leading multi-disciplinary technical teams in fast-paced, cross-functional environments.

Responsibilities:

• Lead, mentor, and grow a high-performing, multi-disciplinary team spanning Applied Science. Data Science, and Machine Learning Engineering for Lyft Media.
• Define and execute the technical vision and roadmap for the team, ensuring alignment with overall business strategy and revenue goals across research, modeling, and production ML.
• Design, develop, and deploy algorithms and ML systems that power core advertising capabilities—including ad targeting, audience segmentation, bid optimization, attribution, and yield management.
• Partner with Product, Engineering, and Design to integrate solutions into scalable, production-grade ad serving and measurement systems.
• Establish robust experimentation and causal inference frameworks to measure the impact of algorithmic changes on advertiser outcomes, rider experience, and platform revenue.
• Bridge the gap between research and production—ensuring that applied science innovations translate into reliable, maintainable ML systems at scale.
• Conduct deep analyses of complex, large-scale datasets to uncover opportunities for revenue growth, advertiser performance improvement, and enhanced user experience.
• Champion data-driven decision-making, ensuring that product and go-to-market decisions are informed by rigorous quantitative analysis.
• Drive innovation by staying current with emerging research, technologies, and industry best practices in computational advertising, optimization, and applied machine learning.

Experience:

• PhD (preferred) or Master's degree in a quantitative field such as Machine Learning, Computer Science, Statistics, Engineering, or a related discipline; or equivalent practical experience.
• 8+ years of progressive experience in machine learning, optimization, or causal inference, including building and deploying algorithms in production systems.
• 3+ years of people management experience leading multi-disciplinary technical teams (data science, applied science, and/or ML engineering), with a proven ability to mentor, develop, and retain top talent.
• Demonstrated ability to set a strategic vision for a technical team and translate it into impactful, scalable solutions that drive measurable business outcomes.
• Deep expertise in machine learning, experimental design, causal inference, and statistical methodologies, with a track record of applying them to high-stakes product or marketplace decisions.
• Strong understanding of ML engineering best practices—model training infrastructure, feature pipelines, model serving, and monitoring in production environments.
• Experience in advertising technology, media measurement, or marketplace optimization is strongly preferred.
• Experience navigating complex, ambiguous problem spaces and guiding teams through prioritization, tradeoffs, and execution.
• Strong communication and influence skills, with the ability to engage both technical and executive stakeholders, align priorities, and build consensus.
• Hands-on proficiency with large-scale data processing tools and machine learning frameworks (e.g., PyTorch, TensorFlow, scikit-learn).

Benefits:

• Great medical, dental, and vision insurance options with additional programs available when enrolled
• Mental health benefits
• Family building benefits
• Child care and pet benefits
• 401(k) plan with company match to help save for your future
• In addition to 12 observed holidays, salaried team members have discretionary paid time off, hourly team members have 15 days paid time off
• 18 weeks of paid parental leave. Biological, adoptive, and foster parents are all eligible
• Subsidized commuter benefits
• Monthly Lyft credits and complimentary Lyft Pink membership

Lyft is an equal opportunity employer committed to an inclusive workplace that fosters belonging. All qualified applicants will receive consideration for employment without regards to race, color, religion, sex, sexual orientation, gender identity, national origin, disability status, protected veteran status, age, genetic information, or any other basis prohibited by law. We also consider qualified applicants with criminal histories consistent with applicable federal, state and local law.

Lyft highly values having employees working in-office to foster a collaborative work environment and company culture. This role will be in-office on a hybrid schedule — Team Members will be expected to work in the office 3 days per week on Mondays, Wednesdays, and Thursdays. Lyft considers working in the office at least 3 days per week to be an essential function of this hybrid role. Your recruiter can share more information about the various in-office perks Lyft offers. Additionally, hybrid roles have the flexibility to work from anywhere for up to 4 weeks per year. #Hybrid

The expected base pay range for this position in the San Francisco area is $148,000 - $185,000, not inclusive of potential equity offering, bonus or benefits. Salary ranges are dependent on a variety of factors, including qualifications, experience and geographic location. Your recruiter can share more information about the salary range specific to your working location and other factors during the hiring process.

At Lyft, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive. 

Lyft Ads is one of Lyft’s newest and fastest-growing businesses, focused on building the world’s largest transportation media network. Our mission is to help brands reach riders during key moments of their journey—before, during, and after a ride—by delivering meaningful, contextually relevant ad experiences. We operate at the intersection of mobility data, real-time decision systems, and AI-powered personalization, enabling advertisers to run high-impact campaigns with measurable outcomes.

We are seeking an Algorithms Scientist  to help build the next generation of ads relevance, targeting, optimization, and measurement algorithms that power the Lyft Ads platform. In this role, you will work across large-scale datasets and complex real-time systems to design, prototype, and deploy production-grade machine learning models. You’ll collaborate closely with Engineering, Product, Data Science, and Sales to translate ambiguous business and advertiser needs into rigorous algorithmic solutions that improve ad performance, enhance marketplace efficiency, and drive meaningful revenue growth.

This is a high-impact, highly technical role within a rapidly scaling business line. The ideal candidate brings strong applied machine learning intuition, hands-on modeling experience, and the ability to write clean, efficient production code. You will play a critical role in shaping how advertisers connect with Lyft riders—pushing the boundaries of personalization, measurement, and real-time optimization in a dynamic marketplace.

Responsibilities:

• Design, develop, and deploy production-grade machine learning models and algorithms that power core Lyft Ads capabilities, such as ad relevance, targeting, ranking, bid optimization, pacing, campaign delivery, and measurement.
• Own the end-to-end lifecycle of modeling projects — including problem definition, data exploration, feature engineering, model development, offline evaluation, deployment, and monitoring.
• Collaborate closely with Ads Engineering to integrate models into real-time ad-serving and batch decision systems, ensuring performance across latency, scalability, and reliability constraints.
• Analyze large-scale mobility, behavioral, and ads performance datasets to identify patterns, surface opportunities, and guide ML and AI driven product improvements.
• Implement rigorous model evaluation frameworks, including offline metrics, statistical tests, calibration, sensitivity analysis, and A/B experimentation to validate both model impact and system-level outcomes.
• Build robust training pipelines, feature transformations, and scoring infrastructure, ensuring reproducibility, observability, and long-term maintainability.
• Partner with Product, Engineering, and Sales to translate ambiguous advertiser goals (e.g., increased conversions, reach efficiency, brand lift) into measurable requirements and success metrics.
• Investigate and resolve model behavior issues, production regressions, calibration drift, and performance anomalies in close partnership with Ads Infra teams.
• Drive innovation by staying current with advances in ML for ranking, recommendation, causal inference, optimization, and ads measurement — and proactively identifying opportunities to apply them.
• Contribute to Lyft Ads’ modeling and experimentation infrastructure, through model cards, documentation, reproducibility standards, and code quality improvements.

Experience:

• Master’s, or PhD in Machine Learning, Computer Science, Statistics, Applied Mathematics, Engineering, or related quantitative fields; or equivalent applied industry experience.
• 3–5 years of hands-on ML/applied science experience, ideally involving production models, large-scale systems, or ads/recommendation/relevance domains.
  Strong proficiency in Python and machine learning frameworks such as PyTorch, TensorFlow, JAX, or scikit-learn; ability to write clean, efficient, production-adjacent code.
• Experience working with large-scale datasets and distributed data tools (Spark, Snowflake, Presto, Databricks).
• Practical experience building and evaluating:
• Ranking and relevance models
• Optimization or pacing algorithms
• Predictive models for CTR, CVR, or user response
• Causal or experimentation-based measurement methods
• Understanding of online/offline evaluation techniques, including:
• Offline metrics (AUC, NDCG, MRR, calibration)
• A/B testing methodologies
• Bias correction and counterfactual estimation
• Ability to solve ambiguous problems by structuring analyses, evaluating trade-offs, and proposing algorithmic solutions grounded in scientific rigor.
• Strong communication skills, with an ability to clearly explain model behavior, constraints, trade-offs, and recommendations to engineering, product, and sales partners.
• Demonstrated ownership of modeling work, including debugging, monitoring, documentation, and iteration after deployment.
• Curiosity, initiative, and a track record of delivering measurable improvements through high-quality modeling.

Benefits:

• Great medical, dental, and vision insurance options with additional programs available when enrolled
• Mental health benefits
• Family building benefits
• Child care and pet benefits
• 401(k) plan to help save for your future
• In addition to 12 observed holidays, salaried team members have discretionary paid time off, hourly team members have 15 days paid time off
• 18 weeks of paid parental leave. Biological, adoptive, and foster parents are all eligible
• Subsidized commuter benefits
• Lyft Pink - Lyft team members get an exclusive opportunity to test new benefits of our Ridership Program

Lyft is an equal opportunity employer committed to an inclusive workplace that fosters belonging. All qualified applicants will receive consideration for employment without regards to race, color, religion, sex, sexual orientation, gender identity, national origin, disability status, protected veteran status, age, genetic information, or any other basis prohibited by law. We also consider qualified applicants with criminal histories consistent with applicable federal, state and local law.

Lyft highly values having employees working in-office to foster a collaborative work environment and company culture. This role will be in-office on a hybrid schedule — Team Members will be expected to work in the office 3 days per week on Mondays, Wednesdays, and Thursdays. Lyft considers working in the office at least 3 days per week to be an essential function of this hybrid role. Your recruiter can share more information about the various in-office perks Lyft offers. Additionally, hybrid roles have the flexibility to work from anywhere for up to 4 weeks per year. #Hybrid

The expected base pay range for this position in the San Francisco area is $128,000 - $160,000. Salary ranges are dependent on a variety of factors, including qualifications, experience and geographic location. Range is not inclusive of potential equity offering, bonus or benefits. Your recruiter can share more information about the salary range specific to your working location and other factors during the hiring process.

Total compensation is dependent on a variety of factors, including qualifications, experience, and geographic location. Your recruiter can share more information about the salary range specific to your working location and other factors during the hiring process. 

At Lyft, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive.

ML & Data Science is at the heart of Lyft's products and decision-making. ML and Data Science professionals at Lyft operate in dynamic environments, moving quickly to build the world's best transportation solutions. We tackle a wide range of challenges—from shaping long-term business strategy with data, to making critical short-term decisions, to developing algorithms and models that power both internal systems and customer-facing products.

Lyft Ads is Lyft's advertising platform, connecting brands with high-intent audiences across the rideshare journey. Our offerings span in-app ad formats, in-car tablet experiences, bikeshare and station sponsorships, and programmatic integrations—enabling advertisers to reach riders at key moments of engagement. These products power high-impact use cases across brand awareness, performance marketing, audience targeting, and measurement for enterprise advertisers.

We are seeking an Algorithms Science Manager to lead a team of Data Scientists, Applied Scientists, and Machine Learning Engineers building the algorithmic backbone of Lyft Media. In this role, you will shape the vision, define the roadmap, and drive execution for projects that improve ad relevance, optimize yield, enhance targeting and measurement, and deliver measurable value to our advertising partners. You'll collaborate closely with Product, Engineering, Design, and Sales teams to build models, experimentation frameworks, and production ML systems that inform strategy and power product innovation.

This is a high-visibility, high-impact role with direct influence on Lyft's advertising platform and revenue growth. The ideal candidate will bring deep expertise in algorithm development, machine learning, causal inference, and experimentation; strong business acumen in ads or marketplace contexts; and a proven track record of leading multi-disciplinary technical teams in fast-paced, cross-functional environments.

Responsibilities:

• Lead, mentor, and grow a high-performing, multi-disciplinary team spanning Applied Science. Data Science, and Machine Learning Engineering for Lyft Media.
• Define and execute the technical vision and roadmap for the team, ensuring alignment with overall business strategy and revenue goals across research, modeling, and production ML.
• Design, develop, and deploy algorithms and ML systems that power core advertising capabilities—including ad targeting, audience segmentation, bid optimization, attribution, and yield management.
• Partner with Product, Engineering, and Design to integrate solutions into scalable, production-grade ad serving and measurement systems.
• Establish robust experimentation and causal inference frameworks to measure the impact of algorithmic changes on advertiser outcomes, rider experience, and platform revenue.
• Bridge the gap between research and production—ensuring that applied science innovations translate into reliable, maintainable ML systems at scale.
• Conduct deep analyses of complex, large-scale datasets to uncover opportunities for revenue growth, advertiser performance improvement, and enhanced user experience.
• Champion data-driven decision-making, ensuring that product and go-to-market decisions are informed by rigorous quantitative analysis.
• Drive innovation by staying current with emerging research, technologies, and industry best practices in computational advertising, optimization, and applied machine learning.

Experience:

• PhD (preferred) or Master's degree in a quantitative field such as Machine Learning, Computer Science, Statistics, Engineering, or a related discipline; or equivalent practical experience.
• 8+ years of progressive experience in machine learning, optimization, or causal inference, including building and deploying algorithms in production systems.
• 3+ years of people management experience leading multi-disciplinary technical teams (data science, applied science, and/or ML engineering), with a proven ability to mentor, develop, and retain top talent.
• Demonstrated ability to set a strategic vision for a technical team and translate it into impactful, scalable solutions that drive measurable business outcomes.
• Deep expertise in machine learning, experimental design, causal inference, and statistical methodologies, with a track record of applying them to high-stakes product or marketplace decisions.
• Strong understanding of ML engineering best practices—model training infrastructure, feature pipelines, model serving, and monitoring in production environments.
• Experience in advertising technology, media measurement, or marketplace optimization is strongly preferred.
• Experience navigating complex, ambiguous problem spaces and guiding teams through prioritization, tradeoffs, and execution.
• Strong communication and influence skills, with the ability to engage both technical and executive stakeholders, align priorities, and build consensus.
• Hands-on proficiency with large-scale data processing tools and machine learning frameworks (e.g., PyTorch, TensorFlow, scikit-learn).

Benefits:

• Great medical, dental, and vision insurance options with additional programs available when enrolled
• Mental health benefits
• Family building benefits
• Child care and pet benefits
• 401(k) plan with company match to help save for your future
• In addition to 12 observed holidays, salaried team members have discretionary paid time off, hourly team members have 15 days paid time off
• 18 weeks of paid parental leave. Biological, adoptive, and foster parents are all eligible
• Subsidized commuter benefits
• Monthly Lyft credits and complimentary Lyft Pink membership

Lyft is an equal opportunity employer committed to an inclusive workplace that fosters belonging. All qualified applicants will receive consideration for employment without regards to race, color, religion, sex, sexual orientation, gender identity, national origin, disability status, protected veteran status, age, genetic information, or any other basis prohibited by law. We also consider qualified applicants with criminal histories consistent with applicable federal, state and local law.

Lyft highly values having employees working in-office to foster a collaborative work environment and company culture. This role will be in-office on a hybrid schedule — Team Members will be expected to work in the office 3 days per week on Mondays, Wednesdays, and Thursdays. Lyft considers working in the office at least 3 days per week to be an essential function of this hybrid role. Your recruiter can share more information about the various in-office perks Lyft offers. Additionally, hybrid roles have the flexibility to work from anywhere for up to 4 weeks per year. #Hybrid

The expected base pay range for this position in the New York City area is $148,000 - $185,000, not inclusive of potential equity offering, bonus or benefits. Salary ranges are dependent on a variety of factors, including qualifications, experience and geographic location. Your recruiter can share more information about the salary range specific to your working location and other factors during the hiring process.

We want people who are passionate about solving complex cloud infrastructure challenges for our customers.

We are looking for a Solutions Architect with expertise in cloud infrastructure to support the success and retention of DigitalOcean's high-value customers. Reporting to the Manager of Solutions Architecture, you will work directly with Customer Success, Sales, and Support functions to retain and grow DigitalOcean’s largest and fastest growing customers. You will be the technical subject matter expert on DigitalOcean’s portfolio, advising on best practices and guiding customers to the optimal solution to meet their business objectives. You will work closely with other functions within DigitalOcean such as Product, Engineering, and Operations to ensure the needs and insights from the local markets are funneled appropriately back into DigitalOcean's products and services.

This is an amazing growth opportunity for a highly motivated individual to establish and expand our presence within the region. Technical depth, excellent communication skills, and a self-starter mentality are needed.

 What You Will Be Doing: 

As a Senior Solutions Architect II, you will serve as a technical partner to the success and sales teams. You will perform architecture reviews, proof of concepts, demos, uncover customer technical needs, and demonstrate DigitalOcean's value proposition. Working with AEs, GAM and TAM counterparts, you'll help retain large customers and expand their workloads on DigitalOcean.

• Develop deep expertise in the DigitalOcean product portfolio, the evolving cloud landscape, and AI/ML platforms.
• Advocate for customers by providing feedback to product teams, overcoming adoption blockers, and driving new feature development.
• Offer personalized onboarding assistance and guide customers through their cloud journey, resolving technical blockers.
• Design and review architecture tailored to specific business use cases, ensuring optimal solutions for customers.
• Implement and oversee infrastructure management best practices to secure and optimize cloud resources.
• Identify opportunities for cost reduction and performance improvement, helping customers make data-driven cloud optimization decisions.
• Conduct technical consultation sessions, workshops, and training for customers to empower them in managing cloud infrastructure.
• Partner with Growth Account Managers and Customer Success Managers to create success plans that ensure customer retention, expansion, and satisfaction.
• Provide regular presentations articulating product benefits, functionality, and technical solutions.
• Demonstrate strong expertise in AI/ML frameworks like TensorFlow and PyTorch and usage of platforms like Hugging Face, with experience deploying and fine-tuning LLMs and GenAI models.
• Optimize GPU workloads using CUDA or TensorRT and build scalable AI applications like chatbots, inference services or recommendation systems using Kubernetes and NFS.
• Design AI solutions tailored to business needs, manage data pipelines, and integrate databases.
• Liaise with Engineering and Support teams to resolve escalations and growth obstacles in a timely fashion.
• Identify and drive process improvement opportunities and promote technical best practices across the organization.
• Contribute to internal and external documentation, such as "The Navigator’s Guide to DO."
• Build tools to enhance the experience of Premium Support customers and improve platform usability.
• Demonstrate proficiency in DevOps tools like Docker, Terraform, and CI/CD pipelines.
• Leverage expertise in Kubernetes, Managed DBaaS, and the DigitalOcean GenAI Platform.
• Work both independently and collaboratively with a global team of Solutions Architects, Customer Success Managers, and Account Managers.

What We'll Expect From You:

• Proven professional experience with cloud infrastructure, AI/ML platforms, or equivalent education.
• Expertise in AI/ML frameworks (e.g., TensorFlow, PyTorch) and familiarity with platforms like Hugging Face.
• Experience deploying and fine-tuning LLMs (e.g., DeepSeek, Llama, Claude, GPT-4) and GenAI models.
• Deep knowledge of Linux, distributed systems, Kubernetes, NFS, Object Storage, and GPU optimization techniques (e.g., CUDA, TensorRT).
• Hands-on experience leveraging vllm and various quantization methods (e.g., INT4, INT8, and FP8) for efficient model deployment.
• Programming/development experience, particularly in building AI-powered applications (e.g., recommendation systems, chatbots).
• Experience working in a post-sales or technical consultant role with a focus on AI/ML and cloud solutions.
• Knowledge of provisioning, deployment strategies, and tools like Terraform, Kubernetes, and Docker.
• Passionate about customer experience and leveraging AI/ML to solve complex business problems.
• Track record of developing successful technical solutions, including AI/ML use cases, to address customer challenges.
• Ability to balance the demands of multiple stakeholders, define priorities, and set appropriate expectations.
• Strong understanding of AI/ML data pipelines, cloud-native databases, and integration strategies.
• Passionate about technology, open source projects, and applying cutting-edge AI/ML innovations.
• Strong communication skills, with the ability to explain technical AI/ML concepts in clear and concise terms.
• Quickly learn DigitalOcean systems and adapt to rapid changes in cloud and AI/ML trends.
• Fluency in English.
• Highly motivated with a self-starter mentality, ready to tackle AI/ML challenges head-on.

Extra Credit: 

• Cloud certifications are highly desirable (AWS/GCP/Oracle/Azure)
• NVIDIA Certifications around GPU and AI/ML
• Networking: Cisco/Juniper Certifications
• Programming/Scripting: Ruby, Python, Go, Bash
• Source Code: Git
• Automation: Terraform, Ansible, Chef, Puppet, Saltstack
• Virtualization: KVM, Xen
• Databases: MongoDB, MySQL, Redis, PostgreSQL
• Open Source: CoreOS, Docker, Kubernetes (CKA/CKE), Vagrant
• DigitalOcean: API, libraries, services
• Linux: RHCSA/RHCE

Compensation Range: 

• Base: $150,000 - $182,000 / Target OTE: $200,000 - 240,000

*This is a remote role

JR: 2026-7695

#LI-Remote

We are looking for a Manager, Customer Success Engineering (AI & Cloud Support) who is passionate about delivering exceptional support experiences and building high-performing teams.

As a Manager, CSE (AI & Cloud Support) at DigitalOcean, you will operate as a Support Manager leading a team of Customer Success Engineers (CSEs) supporting strategic customers across cloud infrastructure and emerging AI/ML workloads. Reporting to the Senior Manager of Customer Success Engineering, you will own day-to-day support operations, team performance, and customer experience outcomes.

This role is focused on driving operational excellence while building strong technical depth within the team across AI, GPUs, Kubernetes, Databases and core cloud offerings. You will play a key role in ensuring consistent support coverage, developing SMEs, and partnering cross-functionally with Product and Engineering to improve both customer experience and product quality.

The ideal candidate brings a deep understanding of cloud and AI/ML ecosystems, including machine learning operations (MLOps), and enterprise support, combined with a passion for innovation and automation, and a proven ability to lead teams in delivering “white-glove” experiences for strategic customers.

What You’ll Do:

Team Leadership & Development

• Lead, hire, train, mentor and develop a high-performing team of Customer Success Engineers (CSEs), driving accountability, performance, and career growth
• Establish performance metrics (KPIs/SLAs) and conduct regular 1:1s, performance reviews, and career development planning.
• Own end-to-end support operations, including queue management, escalations, and shift planning to ensure consistent 24x7 coverage.
• Drive improvements in key support metrics such as CSAT, response times, resolution times, and overall support quality.
• Build and strengthen technical expertise within the team across core areas such as Kubernetes (DOKS), Databases, Compute, and AI/ML workloads.
  
  

Strategic Customer Support

• Act as the ultimate point of technical escalation for our largest, most strategic enterprise customers across Cloud and AI/ML workloads, stepping in to manage critical incidents and high-severity (Sev1/Sev2) issues.
• Design and implement customized support plans, SLAs, and escalation pathways tailored to the needs of strategic accounts.
• Partner closely with Technical Account Managers (TAMs), Growth Account Managers (GAM) to conduct Executive Business Reviews (EBRs) and ensure customers are maximizing the value of our Cloud and AI/ML products.
• Proactively identify risks and opportunities within strategic accounts to improve customer experience, adoption, and retention.

Technical & Cross-Functional Operations

• Serve as the Voice of the Customer (VoC) to Product and Engineering teams, synthesizing support data to advocate for bug fixes, feature requests, and UX improvements.
• Own and continuously improve escalation protocols between AI/ML Support and CloudOps, Infrastructure Engineering, and Product — including Jira escalation routing, Sev1 bridge management, and post-incident documentation.
•  Own the development and maintenance of SOPs, escalation runbooks, HVC support playbooks, and knowledge base content — treating documentation infrastructure as a core operational lever for team scalability.
• Contribute to the vision for AI and automation within support—building intelligent tooling and driving the team toward an automation-first model to improve efficiency, scalability, and customer experience.
• Foster a culture of continuous learning, ensuring the team stays ahead of evolving cloud technologies, AI/ML frameworks, and industry trends.

Key Metrics:

• Customer Satisfaction (CSAT) for strategic accounts
• Time to Response and Resolution (TTR) for strategic customers
• Tier 1 resolution rate vs. escalation rate
• Time-to-escalation and engineering handoff SLA adherence
• SLA adherence and escalation response times
• Support productivity and quality (QA scores)
• Post-incident documentation completion rate

What You’ll Add to DigitalOcean:

• Experience: 5+ years of experience in Technical Support, Customer Success, or Technical Account Management within B2B SaaS, Cloud, or AI/ML environments ideally including experience supporting AI-native, high-growth companies with 24x7 production dependencies on GPU infrastructure.
• Leadership: 2+ years of people management experience leading technical, customer-facing teams, preferably in a high-growth, post-acquisition, or rapidly scaling environment.
• Technical Domain Expertise: Solid understanding of AI/ML concepts, including Generative AI, Large Language Models (LLMs), natural language processing (NLP), and MLOps. Deep familiarity with GPU infrastructure (NVIDIA H100/H200, bare metal GPU provisioning) and AI inference workloads is strongly preferred.
• Coding/Integration: Proficiency in reading and debugging code (Python preferred) and troubleshooting RESTful APIs and cloud architecture.
• Communication: Excellent verbal and written communication skills, with the ability to translate complex technical or AI concepts for diverse audiences OR to both highly technical engineers and non-technical business executives.
• Problem-Solving: Proven ability to remain calm under pressure and de-escalate high-stakes situations with enterprise clients.

Preferred Qualifications:

• Hands-on experience with ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn) and AI toolchains (e.g., LangChain, Hugging Face).
• Experience with major cloud platforms (AWS, Google Cloud, Azure) and their native AI/ML services.
• Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related technical field.
• ITIL or equivalent service management certification

Compensation Range: 

• $125,000 - $153,000

*This is a remote role

JR: 2026-7692

#LI-Remote

We are looking for a Strategic Technical Account Manager who is passionate about making an impact, builds genuine relationships with senior stakeholders, and understands how to integrate infrastructure choices into business strategy.

As a Strategic Technical Account Manager (TAM) at DigitalOcean, you will join a dynamic team dedicated to revolutionizing cloud computing and AI. 

What You’ll Do:

Strategic Relationship Management

• Own the technical relationship with DigitalOcean’s highest-value customers, including Fortune 500 companies and global technology leaders.
• Serve as the trusted technical advisor and executive-facing point of contact, building deep, lasting relationships with VP- and SVP-level stakeholders.
• Drive regular technical and strategic alignment sessions (QBRs, EBRs), proactively shaping the customer’s roadmap to maximize the value of DigitalOcean infrastructure.
• Operate as a thought partner, challenging customers to modernize, optimize, and scale AI/ML and GPU workloads using cutting-edge cloud architecture patterns.

Executive Presence & Influence

• Engage with C-level and VP-level contacts with confidence, clarity, and authority.
• Deliver high-impact, boardroom-ready presentations on architecture decisions, cost-performance tradeoffs, and innovation opportunities.
• Navigate complex org structures, ensuring DigitalOcean maintains executive alignment and account control at every level.

Technical Consultation & Innovation Support

• Provide expert guidance on designing and scaling AI/ML, containerized, and GPU-intensive workloads on DigitalOcean.
• Assist customers with architectural planning, workload migration, performance tuning, and incident response.
• Collaborate directly with engineering and product teams to address customer roadblocks and influence the product roadmap based on enterprise needs.

Growth, Retention & Advocacy

• Own technical success across a portfolio of DigitalOcean’s largest and most strategic accounts.
• Identify opportunities for workload expansion, cost optimization, and operational efficiency.
• Act as the voice of the customer, providing structured feedback to influence DigitalOcean's offerings and go-to-market strategies.
  Lead cross-functional account planning with Account Management, Product, and Engineering.

What You’ll Add to DigitalOcean:

Enterprise Customer Experience

• 7+ years supporting enterprise or strategic accounts in a customer-facing technical role (TAM, Solutions Architect, or equivalent).
• Experience working with large-scale organizations like AMD, Intel, or similar, including managing executive-level relationships and navigating matrixed stakeholders.
• Deep understanding of cloud architecture for AI/ML, including GPU orchestration, inference/training pipelines, and model deployment.

Technical Depth

• Proficient in at least one major cloud platform (AWS, GCP, Azure) and familiar with DigitalOcean or other developer-focused platforms.
• Strong hands-on skills with Linux systems, networking, containers (Docker/Kubernetes), and automation (Terraform, Ansible).
  Programming/scripting in Python, Go, or similar—especially relevant to AI/ML and MLOps use cases.
• Understanding of AI/ML infrastructure stacks: PyTorch, TensorFlow, ONNX, Hugging Face, NVIDIA Triton, etc.
• Understanding of AI/ML infrastructure, including frameworks like PyTorch, TensorFlow, and ONNX, with hands-on experience optimizing GPU workloads on both NVIDIA (CUDA, TensorRT) and AMD (ROCm, MIOpen) platforms.
• Comfort with modern DevOps and data stack tools (CI/CD, GitOps, observability, pipelines).

Executive Presence & Communication

• Proven ability to engage with and influence executive decision-makers.
• Exceptional communication skills, including the ability to translate complex technical topics into strategic business impact.
• Comfortable presenting to large audiences, running technical workshops, and defending architectural decisions.

Bonus Points

• Cloud certifications (AWS SA Pro, GCP PCA, Azure Architect) or NVIDIA certifications.
• Experience with high-performance computing, edge AI, or hybrid/multicloud strategies.
• Background in startup or high-growth environments where agility and influence matter.
• Familiarity with DigitalOcean’s developer ecosystem, API, and service portfolio.

Compensation Range: 

• $174,240 - $217,800

*This is a remote role

#LI-Remote

Who We Are:

Calico (Calico Life Sciences LLC) is an Alphabet-founded research and development company whose mission is to harness advanced technologies and model systems to increase our understanding of the biology that controls human aging. Calico will use that knowledge to devise interventions that enable people to lead longer and healthier lives. Calico’s highly innovative technology labs, its commitment to curiosity-driven discovery science, and, with academic and industry partners, its vibrant drug-development pipeline, together create an inspiring and exciting place to catalyze and enable medical breakthroughs. 

Position Description:

Calico is seeking a machine learning research scientist to develop innovative machine learning algorithms to model and design proteins. This person will work with a team of dedicated experimental biologists to design experiments to test and iteratively refine machine learning predictions.

The ideal candidate should be familiar with state-of-the-art machine learning techniques, including language/sequence modeling, representation learning, generative models, and self-supervised learning techniques. Candidates should have experience implementing, extending, and debugging state of the art machine learning models, and have expertise in designing and building robust data infrastructure and tools. Candidates should be independent, self-motivated and be expected to explore relevant ML literature and apply novel approaches to various protein design problems at Calico. Candidates must demonstrate a strong ability to communicate ideas and results through publications and presentations and be able to work cross-functionally to execute on complex projects.

Position Requirements:

• Ph.D. in Computer Science, Computational Biology, or related technical field; OR M.S. in Computer Science, Computational Biology, or related technical field, plus 2+ years industry experience
• Experience in applying deep learning to model biological sequences and structures
• Expert in TensorFlow, PyTorch, and/or JAX
• Strong software engineering skills and substantial expertise in Python
• Track record of outstanding communication and collaboration in a cross-functional environment
• Strong analytical and quantitative skills
• Must be willing to work onsite at least four days a week

Nice to Have:

• Domain expertise in computational biology, biochemistry or structural biology
• Familiarity with state of the art structure modeling tools such as AlphaFold
• Public recognition in the field, e.g. via scientific publications or success in machine learning competitions
• Prior experience working with biologists
• Prior experience working as an ML Engineer/Scientist

The estimated base salary range for this role is $170,000 - $233,000. Actual pay will be based on a number of factors including experience and qualifications. This position is also eligible for two annual cash bonuses.

Senior Manager, Data Science

Portfolio Management and Loss Mitigation

The Role: 

SoFi is seeking an inspirational and deeply experienced Senior Manager to lead the Portfolio Management and Loss Mitigation function in our Risk Data Science team. Reporting to the Head of Risk Data Science, this role will lead the development, deployment, and governance of portfolio management and loss mitigation models for all credit products.

The ideal candidate is a hands-on leader who can transition the team from traditional modeling to next-generation machine learning platforms, leveraging emerging data sources (e.g., cash flow, alternative bureaus) to significantly improve portfolio performance, reduce losses, and ensure rigorous adherence to Model Risk Management (MRM) standards. This role requires exceptional organizational leadership, an ability to influence stakeholders, and proven success in delivering complex models into a regulated production environment.

What You'll Do: 

• Portfolio Management and Loss Mitigation Excellence: Directly oversee the development and deployment of Next Generation models designed to manage and mitigate portfolio losses while maintaining loss guardrails.
• Drive Next-Generation Capabilities: Incorporate industry trends and advanced techniques (NLP, Graph Mining, LLMs, Deep Learning) to solve complex, high-impact risk problems where established principles may not fully apply.
• Alternative Data Strategy: Spearhead the evaluation and integration of alternative data sources (Macro-Economic variables, tri-bureau, LexisNexis, cash flow data) to enhance predictive power across all credit products.
• Lead the current team of high-performing Staff and Senior Data Scientists. Recruit, mentor, and foster talent through deliberate interactions, succession planning, and creating a high-accountability, low-ego culture.
• Model Risk Management (MRM): Act as the primary owner for all models in the portfolio, ensuring robust documentation, monitoring, and successfully navigating the 2nd Line of Defense (2LOD) review and approval process (SR 11-7 familiarity is mandatory).
• Stakeholder Alignment: Interact and negotiate with senior management and external stakeholders to reconcile competing views and drive critical, high-impact business decisions.
• Automation and Efficiency: Lead efforts to automate model monitoring and governance processes to create scalable and auditable infrastructure.

What You'll Need:

• Experience: 8+ years of progressive experience in credit risk, modeling, and data science within a regulated financial institution (FinTech, Bank, or similar), with at least 3 years in a people management role, managing technical staff.
• Education: Master’s or Ph.D. degree in a quantitative field (Statistics, Computer Science, Engineering, Operations Research, etc.).
• Technical Acumen: Deep expertise in advanced statistical and machine learning modeling techniques (e.g., Gradient Boosting, Deep Learning, Causal Inference).
• Regulatory Knowledge: Detailed working knowledge of model risk management standards (e.g., SR 11-7) and the ability to operate within a highly regulated environment.
• Tools & Platforms: Expert-level proficiency in Python (PySpark, scikit-learn, TensorFlow/PyTorch) and SQL/data warehouse technologies (e.g., Snowflake, Hive). Familiarity with modern MLOps platforms and cloud computing (AWS).
• 3+ years of progressive people management experience is required, including the ability to recruit, mentor, and foster talent within a team of high-performing Staff and Senior Data Scientists.
• Communication: Ability to distill highly complex analytical concepts into clear, concise, and compelling narratives for non-technical leadership.

Job Duties: Design, develop, and apply DS solutions to inform improvements in advertiser experience as well as Reddit ad platform and marketplace performance. Analyse large-scale datasets to identify trends, patterns, and insights that can be used to improve the effectiveness of our advertising platform. Collaborate with product managers and engineers to define product requirements and translate them into data science solutions. Develop Machine Learning models and Data Science methods to improve anomaly detection, prediction and pattern recognition. Communicate findings and recommendations to stakeholders across the organization. Telecommuting permitted. 

Minimum Requirements: Master’s degree (or its foreign equivalent) in Computer Science, Information Management, Statistics, Mathematics, or closely related quantitative field and two (2) years of experience in data science, machine learning, or a related field.

Special Skill Requirements: (1) Statistical modelling (2) Machine learning algorithms (3) Causal inference (4) Experimentation design (5) Large-scale data processing and analysis using tools such as Spark, Hadoop, or Hive (6) Python or R (7) Machine learning libraries such as scikit-learn, TensorFlow, or PyTorch (8) SQL and relational databases Any suitable combination of education, training and experience is acceptable. Experience can be gained through coursework, scholastic achievement, or work experience.

Benefits:

• Comprehensive Healthcare Benefits and Income Replacement Programs
• 401k with Employer Match
• Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support
• Family Planning Support
• Gender-Affirming Care
• Mental Health & Coaching Benefits
• Flexible Vacation & Paid Volunteer Time Off
• Generous Paid Parental Leave 

Submit a resume with references using the apply button on this posting or by email at: applicationsreview@reddit.com at Req.# 1016.238.2.

Pay Transparency:

This job posting may span more than one career level.

In addition to base salary, this job is eligible to receive equity in the form of restricted stock units, and depending on the position offered, it may also be eligible to receive a commission. Additionally, Reddit offers a wide range of benefits to U.S.-based employees, including medical, dental, and vision insurance, 401(k) program with employer match, generous time off for vacation, and parental leave. To learn more, please visit https://www.redditinc.com/careers/.

To provide greater transparency to candidates, we share base pay ranges for all US-based job postings regardless of state. We set standard base pay ranges for all roles based on function, level, and country location, benchmarked against similar stage growth companies. 

The base pay range for this position is: $164,200.00 - $229,900.00 USD

#LI-DNI

About the Role

Thinking Machines builds multimodal-first. We’re looking for new team members to advance the science of visual perception and multimodal learning. We think about how vision and language interact at scale. We design architectures that fuse pixels and text, build datasets and evaluation methods that test real-world comprehension, and develop representations that let models ground abstract concepts in the physical world. Our goal is to create multimodal systems that support seamless integration into real-world environments.

You’ll work at the intersection of visual understanding, multimodal reasoning, and large-scale model training. You’ll help develop the architectures, data, and evaluation tools that teach AI to see, understand, and collaborate. The best candidate is curious about multimodal interfaces, has experience running large scale experiments and is comfortable contributing to complex engineering systems. While we are looking for a person with expertise in multimodality, Thinking Machines Lab operates in a unified fashion and expects new hires to work across modalities as one team.

This role blends fundamental research and practical engineering, as we do not distinguish between the two roles internally. You will be expected to write high-performance code and read technical reports. It’s an excellent fit for someone who enjoys both deep theoretical exploration and hands-on experimentation, and who wants to shape the foundations of how AI learns.

Note: This is an "evergreen role" that we keep open on an on-going basis to express interest in this research area. We receive many applications, and there may not always be an immediate role that aligns perfectly with your experience and skills. Still, we encourage you to apply. We continuously review applications and reach out to applicants as new opportunities open. You are welcome to reapply if you get more experience, but please avoid applying more than once every 6 months. You may also find that we put up postings for singular roles for separate, project or team specific needs. In those cases, you're welcome to apply directly in addition to an evergreen role.

What You’ll Do

• Own research projects on training and performance analysis of multimodal AI models.
• Curate and build large-scale datasets and evaluation benchmarks to advance vision capabilities.
• Work with our data infrastructure engineers, pretraining researchers and engineers, and product team to create frontier multimodal models and the products that leverage them.
• Publish and present research that moves the entire community forward. Share code, datasets, and insights that accelerate progress across industry and academia.

Skills and Qualifications

Minimum qualifications:

• Ability to design, run, and analyze experiments thoughtfully, with demonstrated research judgment and empirical rigor.
• Understanding of machine learning fundamentals, large-scale training, and distributed compute environments.
• Proficiency in Python and familiarity with at least one deep learning framework (e.g., PyTorch, TensorFlow, or JAX). Comfortable with debugging distributed training and writing code that scales.
• Bachelor’s degree or equivalent experience in Computer Science, Machine Learning, Physics, Mathematics, or a related discipline with strong theoretical and empirical grounding.
• Clarity in communication, an ability to explain complex technical concepts in writing.

Preferred qualifications — we encourage you to apply even if you don’t meet all preferred qualifications, but at least some:

• Research or engineering contributions in visual  reasoning, spatial understanding, or multimodal architecture design.
• Experience developing evaluation frameworks for multimodal tasks.
• Publications or open-source contributions in vision-language modeling, video understanding, or multimodal AI.
• A strong grasp of probability, statistics, and ML fundamentals. You can look at experimental data and distinguish between real effects, noise, and bugs.
• PhD in Computer Science, Machine Learning, Physics, Mathematics, or a related discipline with strong theoretical and empirical grounding; or, equivalent industry research experience.

Logistics

• Location: This role is based in San Francisco, California. 
• Compensation: Depending on background, skills and experience, the expected annual salary range for this position is $350,000 - $475,000 USD.
• Visa sponsorship: We sponsor visas. While we can't guarantee success for every candidate or role, if you're the right fit, we're committed to working through the visa process together.
• Benefits: Thinking Machines offers generous health, dental, and vision benefits, unlimited PTO, paid parental leave, and relocation support as needed.

About the Role

The role of pre-training researchers sits at the core of our roadmap. This work advances the science of how large models learn from data. You’ll explore new pre-training methods, architectures, and learning objectives that make model training efficient, robust, and aligned with human goals.

This role blends fundamental research and practical engineering, as we do not distinguish between the two roles internally. You will be expected to write high-performance code and read technical reports. It’s an excellent fit for someone who enjoys both deep theoretical exploration and hands-on experimentation, and who wants to shape the foundations of how AI learns.

Note: This is an "evergreen role" that we keep open on an on-going basis to express interest in this research area. We receive many applications, and there may not always be an immediate role that aligns perfectly with your experience and skills. Still, we encourage you to apply. We continuously review applications and reach out to applicants as new opportunities open. You are welcome to reapply if you get more experience, but please avoid applying more than once every 6 months. You may also find that we put up postings for singular roles for separate, project or team specific needs. In those cases, you're welcome to apply directly in addition to an evergreen role.

What You’ll Do

• Research and develop new methodologies for pre-training.
• Work in areas such as scaling, architecture, algorithms, or optimization of large scale training runs depending on your research interest and experience.
• Design data curricula and sampling strategies that improve learning dynamics and model generalization.
• Collaborate with infrastructure and data teams to conduct large-scale experiments efficiently and reproducibly.
• Publish and present research that moves the entire community forward. Share code, datasets, and insights that accelerate progress across industry and academia.

Skills and Qualifications

Minimum qualifications:

• Ability to design, run, and analyze experiments thoughtfully, with demonstrated research judgment and empirical rigor.
• Experience with distributed or high-performance computing environments.
• Proficiency in Python and familiarity with at least one deep learning framework (e.g., PyTorch, TensorFlow, or JAX). Comfortable with debugging distributed training and writing code that scales.
• Bachelor’s degree or equivalent experience in Computer Science, Machine Learning, Physics, Mathematics, or a related discipline with strong theoretical and empirical grounding.
• Clarity in communication, an ability to explain complex technical concepts in writing.

Preferred qualifications — we encourage you to apply even if you don’t meet all preferred qualifications, but at least some:

• A strong grasp of probability, statistics, and ML fundamentals. You can look at experimental data and distinguish between real effects, noise, and bugs.
• Prior experience training or analyzing large-scale models, or contributing to pre-training or foundation model research.
• Strong publication record or open-source contributions in representation learning, optimization, scaling laws, or other areas of pre-training.
• Familiarity with curriculum learning, data selection, or active learning techniques.
• Experience designing or maintaining evaluation frameworks for large models.
• Contributions to open datasets, research publications, or data tooling.
• PhD in Computer Science, Machine Learning, Physics, Mathematics, or a related discipline with strong theoretical and empirical grounding; or, equivalent industry research experience.

Logistics

• Location: This role is based in San Francisco, California. 
• Compensation: Depending on background, skills and experience, the expected annual salary range for this position is $350,000 - $475,000 USD.
• Visa sponsorship: We sponsor visas. While we can't guarantee success for every candidate or role, if you're the right fit, we're committed to working through the visa process together.
• Benefits: Thinking Machines offers generous health, dental, and vision benefits, unlimited PTO, paid parental leave, and relocation support as needed.

About the Role

The role of pre-training researchers sits at the core of our roadmap. This work blends research with large-scale data engineering to help assemble the pre-training datasets and data systems that underpin the next generation of AI models. You’ll design and implement methods for sourcing, curating, and analyzing pre-training data for quality and performance.

You’ll work with automated pipelines and human-in-the-loop processes, contributing both scientific insight and production-grade code. It’s ideal for someone who enjoys working at the intersection of data, machine learning, and systems, and who’s excited by the challenge of shaping frontier AI.

This role blends fundamental research and practical engineering, as we do not distinguish between the two roles internally. You will be expected to write high-performance code and read technical reports. It’s an excellent fit for someone who enjoys both deep theoretical exploration and hands-on experimentation, and who wants to shape the foundations of how AI learns.

Note: This is an "evergreen role" that we keep open on an on-going basis to express interest in this research area. We receive many applications, and there may not always be an immediate role that aligns perfectly with your experience and skills. Still, we encourage you to apply. We continuously review applications and reach out to applicants as new opportunities open. You are welcome to reapply if you get more experience, but please avoid applying more than once every 6 months. You may also find that we put up postings for singular roles for separate, project or team specific needs. In those cases, you're welcome to apply directly in addition to an evergreen role.

What You’ll Do

• Design and implement techniques for curating, sourcing, and filtering large-scale text, code, and multimodal data.
• Develop data quality metrics and analysis to measure coverage, diversity, and representativeness across sources.
• Collaborate with research and infrastructure teams to scale data processing systems efficiently and reproducibly.
• Investigate and mitigate data risks, including privacy, safety, and licensing concerns, to ensure responsible and ethical data use.
• Continuously evaluate dataset improvements by analyzing their downstream effects on model learning and behavior.
• Publish and present research that moves the entire community forward. Share code, datasets, and insights that accelerate progress across industry and academia.

Skills and Qualifications

Minimum qualifications:

• Proficiency in Python and familiarity with at least one deep learning framework (e.g., PyTorch, TensorFlow, or JAX). Comfortable with debugging distributed training and writing code that scales.
• Bachelor’s degree or equivalent experience in Computer Science, Machine Learning, Physics, Mathematics, or a related discipline with strong theoretical and empirical grounding.
• Clarity in communication, an ability to explain complex technical concepts in writing.

Preferred qualifications — we encourage you to apply even if you don’t meet all preferred qualifications, but at least some:

• A strong grasp of probability, statistics, and ML fundamentals. You can look at experimental data and distinguish between real effects, noise, and bugs.
• Experience with curation, preprocessing, and analysis of large-scale text, code, or multimodal datasets.
• Prior experience in data engineering, dataset construction, or large-scale web data processing for machine learning models.
• Experience evaluating or improving training data quality and knowledge of data ethics, safety, and licensing frameworks relevant to AI dataset creation.
• Contributions to open datasets, research publications, or data tooling.
• PhD in Computer Science, Machine Learning, Physics, Mathematics, or a related discipline with strong theoretical and empirical grounding; or, equivalent industry research experience.

Logistics

• Location: This role is based in San Francisco, California. 
• Compensation: Depending on background, skills and experience, the expected annual salary range for this position is $350,000 - $475,000 USD.
• Visa sponsorship: We sponsor visas. While we can't guarantee success for every candidate or role, if you're the right fit, we're committed to working through the visa process together.
• Benefits: Thinking Machines offers generous health, dental, and vision benefits, unlimited PTO, paid parental leave, and relocation support as needed.

About the Role

The role of post-training researchers sits at the core of our roadmap. This is the critical bridge between raw model intelligence and a system that is actually useful, safe, and collaborative for humans.

Post-training data research work sits at the intersection of human insight and machine learning. Our work combines human and synthetic data techniques, along with other innovative approaches, to capture the nuances of human behavior and use them to steer models. We research and model the mechanisms that create value for people to explain, predict, and optimize for human preferences, behaviors, and satisfaction. Our goal is to turn research ideas into data by scoping well-run data labeling or collection campaigns, and understanding the science behind what makes the data high quality and useful to train our models. We also develop and evaluate quantitative metrics that measure the success and impact of our data and training interventions.

Beyond execution, we explores new paradigms for human-ai interaction and scalable oversight, experimenting with how humans can best supervise, guide, and collaborate with models. It’s interdisciplinary work that blends research, data operations, and technical implementation to advance the frontier of aligned, human-centered AI systems.

This role blends fundamental research and practical engineering, as we do not distinguish between the two roles internally. You will be expected to write high-performance code and read technical reports. It’s an excellent fit for someone who enjoys both deep theoretical exploration and hands-on experimentation, and who wants to shape the foundations of how AI learns.

Note: This is an "evergreen role" that we keep open on an on-going basis to express interest in this research area. We receive many applications, and there may not always be an immediate role that aligns perfectly with your experience and skills. Still, we encourage you to apply. We continuously review applications and reach out to applicants as new opportunities open. You are welcome to reapply if you get more experience, but please avoid applying more than once every 6 months. You may also find that we put up postings for singular roles for separate, project or team specific needs. In those cases, you're welcome to apply directly in addition to an evergreen role.

What You’ll Do

• Design and execute data collection and synthesis strategies for post-training by combining human feedback, preference data, and synthetic examples to guide model behavior.
• Develop pipelines and frameworks for scalable, high-quality human labeling, model-assisted labeling, and synthetic data generation.
• Research and model human preferences and behavior, creating data-driven methods to improve reasoning, truthfulness, and helpfulness.
• Iterate on evals: post-training involves a never-ending loop of defining a set of evaluations, optimizing them, and then realizing your existing evals don’t capture what matters. You’ll be responsible for both making numbers go up, and making sure the numbers are meaningful.
• Design and evaluate metrics and benchmarks that measure data quality, alignment, and the real-world impact of post-training interventions.
• Scale and explore: post-training will involve a combination of scaling the existing methodologies and developing new ones.
• Publish and present research that moves the entire community forward. Share code, datasets, and insights that accelerate progress across industry and academia.

Skills and Qualifications

Minimum qualifications:

• Strong engineering skills, ability to contribute code and debug in complex codebases.
• Experience with data curation, human feedback, or synthetic data generation for large language models or similar systems.
• Ability to design, run, and interpret experiments with scientific rigor and clarity.
• Proficiency in Python and familiarity with at least one deep learning framework (e.g., PyTorch, TensorFlow, or JAX). Comfortable with debugging distributed training and writing code that scales.
• Bachelor’s degree or equivalent experience in Computer Science, Machine Learning, Physics, Mathematics, or a related discipline with strong theoretical and empirical grounding.
• Clarity in communication, an ability to explain complex technical concepts in writing.

Preferred qualifications — we encourage you to apply even if you don’t meet all preferred qualifications, but at least some:

• A strong grasp of probability, statistics, and ML fundamentals. You can look at experimental data and distinguish between real effects, noise, and bugs.
• Prior experience with RLHF, RLAIF, preference modeling, or reward learning for large models.
• Experience managing or analyzing human data collection campaigns or large-scale annotation workflows.
• Research or engineering contributions in alignment, data-centric AI, or human-AI collaboration.
• Familiarity with synthetic data pipelines, active learning, or model-assisted labeling
• PhD in Computer Science, Machine Learning, Physics, Mathematics, or a related discipline with strong theoretical and empirical grounding; or, equivalent industry research experience.

Logistics

• Location: This role is based in San Francisco, California. 
• Compensation: Depending on background, skills and experience, the expected annual salary range for this position is $350,000 - $475,000 USD.
• Visa sponsorship: We sponsor visas. While we can't guarantee success for every candidate or role, if you're the right fit, we're committed to working through the visa process together.
• Benefits: Thinking Machines offers generous health, dental, and vision benefits, unlimited PTO, paid parental leave, and relocation support as needed.

About the Role

The role of post-training researchers sits at the core of our roadmap. This is the critical bridge between raw model intelligence and a system that is actually useful, safe, and collaborative for humans.

This role blends fundamental research and practical engineering, as we do not distinguish between the two roles internally. You will be expected to write high-performance code and read technical reports. It’s an excellent fit for someone who enjoys both deep theoretical exploration and hands-on experimentation, and who wants to shape the foundations of how AI learns.

Note: This is an "evergreen role" that we keep open on an on-going basis to express interest in this research area. We receive many applications, and there may not always be an immediate role that aligns perfectly with your experience and skills. Still, we encourage you to apply. We continuously review applications and reach out to applicants as new opportunities open. You are welcome to reapply if you get more experience, but please avoid applying more than once every 6 months. You may also find that we put up postings for singular roles for separate, project or team specific needs. In those cases, you're welcome to apply directly in addition to an evergreen role.

What You’ll Do

• Develop and tune the recipe: iterate on post-training recipes, consisting of a collection of datasets, training stages, and hyperparameters. Measure how recipe choices affect various metrics.
• Iterate on evals: post-training involves a never-ending loop of defining a set of evaluations, optimizing them, and then realizing your existing evals don’t capture what matters. You’ll be responsible for both making numbers go up, and making sure the numbers are meaningful.
• Debug and understand: while tuning the details of a training configuration, we often observe results that don’t quite make sense. You’ll be responsible for both getting things to work, and developing a deeper understanding, which we can bring to the next problem.
• Scale and explore: post-training will involve a combination of scaling the existing methodologies and developing new ones. We’ll want to both measure how performance metrics scale with dataset size, and explore using a completely different kind of training dataset.
• Publish and present research that moves the entire community forward. Share code, datasets, and insights that accelerate progress across industry and academia.

Skills and Qualifications

Minimum qualifications:

• Proficiency in Python and familiarity with at least one deep learning framework (e.g., PyTorch, TensorFlow, or JAX). Comfortable with debugging distributed training and writing code that scales.
• Bachelor’s degree or equivalent experience in Computer Science, Machine Learning, Physics, Mathematics, or a related discipline with strong theoretical and empirical grounding.
• Clarity in communication, an ability to explain complex technical concepts in writing.

Preferred qualifications — we encourage you to apply even if you don’t meet all preferred qualifications, but at least some:

• A strong grasp of probability, statistics, and ML fundamentals. You can look at experimental data and distinguish between real effects, noise, and bugs.
• Prior experience with RLHF, RLAIF, preference modeling, or reward learning for large models.
• Experience managing or analyzing human data collection campaigns or large-scale annotation workflows.
• Research or engineering contributions in alignment, data-centric AI, or human-AI collaboration.
• PhD in Computer Science, Machine Learning, Physics, Mathematics, or a related discipline with strong theoretical and empirical grounding; or, equivalent industry research experience.

Logistics

• Location: This role is based in San Francisco, California. 
• Compensation: Depending on background, skills and experience, the expected annual salary range for this position is $350,000 - $475,000 USD.
• Visa sponsorship: We sponsor visas. While we can't guarantee success for every candidate or role, if you're the right fit, we're committed to working through the visa process together.
• Benefits: Thinking Machines offers generous health, dental, and vision benefits, unlimited PTO, paid parental leave, and relocation support as needed.

About the Role

Thinking Machines builds multimodal-first. For us, there is no separate multimodal work. It’s at the core of everything we do, from the scientific goals we’re setting to the infrastructure we’re building. We’re looking for researchers to advance the frontier of audio capabilities. You’ll explore how audio models enable more natural and efficient communication/collaboration, preserving more information and capturing user intent.

This is a highly collaborative role. You’ll work closely across pre-training, post-training, and product with world-class researchers, infrastructure engineers, and designers. This is an opportunity to shape the fundamental capabilities of AI systems that millions of people will use.

This role blends fundamental research and practical engineering, as we do not distinguish between the two roles internally. You will be expected to write high-performance code and read technical reports. It’s an excellent fit for someone who enjoys both deep theoretical exploration and hands-on experimentation, and who wants to shape the foundations of how AI learns.

Note: This is an "evergreen role" that we keep open on an on-going basis to express interest in this research area. We receive many applications, and there may not always be an immediate role that aligns perfectly with your experience and skills. Still, we encourage you to apply. We continuously review applications and reach out to applicants as new opportunities open. You are welcome to reapply if you get more experience, but please avoid applying more than once every 6 months. You may also find that we put up postings for singular roles for separate, project or team specific needs. In those cases, you're welcome to apply directly in addition to an evergreen role.

What You’ll Do

• Own research projects on audio training, low-latency inference and conversational responsiveness.
• Design and train large-scale models that natively support audio input and output.
• Investigate scaling behavior such as how data, model size, and compute affect capability and efficiency.
• Build and maintain audio data pipelines, including preprocessing, filtering, segmentation, and alignment for training and evaluation.
• Collaborate with data and infrastructure teams to scale audio training efficiently across distributed systems.
• Publish and present research that moves the entire community forward. Share code, datasets, and insights that accelerate progress across industry and academia.

Skills and Qualifications

Minimum qualifications:

• Ability to design, run, and analyze experiments thoughtfully, with demonstrated research judgment and empirical rigor.
• Understanding of machine learning fundamentals, large-scale training, and distributed compute environments.
• Proficiency in Python and familiarity with at least one deep learning framework (e.g., PyTorch, TensorFlow, or JAX). Comfortable with debugging distributed training and writing code that scales.
• Bachelor’s degree or equivalent experience in Computer Science, Machine Learning, Physics, Mathematics, or a related discipline with strong theoretical and empirical grounding.
• Clarity in communication, an ability to explain complex technical concepts in writing.

Preferred qualifications — we encourage you to apply even if you don’t meet all preferred qualifications, but at least some:

• A strong grasp of probability, statistics, and ML fundamentals. You can look at experimental data and distinguish between real effects, noise, and bugs.
• Experience with real-time inference, streaming architectures, or optimization for low latency.
• Prior experience training or evaluating large-scale audio or multimodal models.
• Publications, releases, or open-source projects related to speech, audio, voice, or similar areas.
• Demonstrated experience in audio or speech modeling, including ASR, TTS, or self-supervised audio learning.
• PhD in Computer Science, Machine Learning, Physics, Mathematics, or a related discipline with strong theoretical and empirical grounding; or, equivalent industry research experience.

Logistics

• Location: This role is based in San Francisco, California. 
• Compensation: Depending on background, skills and experience, the expected annual salary range for this position is $350,000 - $475,000 USD.
• Visa sponsorship: We sponsor visas. While we can't guarantee success for every candidate or role, if you're the right fit, we're committed to working through the visa process together.
• Benefits: Thinking Machines offers generous health, dental, and vision benefits, unlimited PTO, paid parental leave, and relocation support as needed.

The role:

The Marketing Data Science (MDS) team is seeking an experienced Machine Learning professional to join the group. This team is essential for supporting SoFi's Marketing organization by providing data-driven decision-making through rigorous analytics, modeling, experimentation, and measurement.

The primary responsibility of this role is to build and scale machine learning models that drive marketing and growth across SoFi's product suite. The focus will be on developing predictive models for key areas: acquisition, conversion, retention, and customer lifetime value (LTV), utilizing behavioral, transactional, and credit data. This role requires strong collaboration across teams to identify the business needs, manage the end-to-end model development cycle, implement the model in production, and monitor performance, while maintaining strict compliance with all regulatory standards.

What you’ll do: 

• Design, develop, and deploy machine learning models to optimize customer acquisition, onboarding, and lifecycle engagement across financial products (e.g., loans, credit cards, money, invest, crypto and etc).
• Build predictive models for critical business outcomes, including Customer Lifetime Value (LTV), conversion propensity, cross-sell/upsell effectiveness, and retention across various channels (e.g., direct mail, email, in-app, Operations).
• Leverage structured and unstructured data (e.g., behavioral signals, transaction data, credit attributes) to drive audience segmentation and personalization at scale. Develop and maintain a robust feature store to accelerate the end-to-end model development lifecycle.
• Build experimentation frameworks to support A/B testing and measure the incremental business impact of the models. Work with the other team members to incorporate the models in marketing/campaign strategies.
• Collaborate with Fair Lending (FL) and Model Risk Management (MRM) to ensure the models and targeting strategies adhere to fair lending laws, privacy regulations, and responsible marketing practices.
• Support the efficient scoring and implementation of models through both real-time and batch processes, ensuring the timely availability of scores for marketing execution and deep analytics.
• Monitor model performance with a focus on business outcomes (conversion, retention, revenue, risk-adjusted returns) and continuously iterate based on insights.
• Partner closely with team members within MDS and key business stakeholders on defining business requirements, prioritizing the model pipeline, providing development updates, and monitoring performance.
• Stay at the forefront of modeling, data, and AI innovations to support scalable marketing growth in financial services.

What you’ll need:

• Bachelor's degree in Computer Science, Math, Physics, Engineering, or a quantitative field required; Master's degree preferred.
• 8+ years of experience in machine learning, data science or applied AI, ideally in marketing, fintech, or consumer tech.
• Strong proficiency in Python, SQL, and ML frameworks (scikit-learn, PyTorch, TensorFlow).
• Familiarity with ML governance, model monitoring, and compliance frameworks (MRM, Fair Lending, etc.) is a plus.
• Experience with LLMs, prompt engineering, and AI application development preferred.
• Demonstrated experience with experimentation and hypothesis testing.
• High intellectual curiosity and a quick learner of new technical skills.
• Ability to initiate and drive projects independently to completion, and ability to clearly communicate technical information to diverse audiences.
• Strong relationship-building and collaborative skills.
• Ability to thrive in a dynamic, cross-functional environment with keen attention to detail.

About the Role:

The Machine Learning team at Tubi drives the innovation behind personalized user experiences.  With the largest inventory in the industry and hundreds of millions of viewers, we tackle problems in the space of recommendations, search, content understanding and ads optimization that shape the future of streaming.

We are seeking a highly skilled Machine Learning Engineer to contribute to transformative projects in video personalization. In this role, you will design and implement advanced algorithms and systems to improve our personalization strategy. As a senior technical expert, you will tackle complex problems in machine learning at scale, collaborating closely with cross-functional teams to develop and optimize machine learning-driven solutions.

What You'll Do:

• Lead the design, development, and implementation of advanced recommendation systems and algorithms for a global audience
• Conduct deep dives into algorithmic components and systems, ensuring that models are optimized for both performance and scalability across multiple regions and product areas
• Build and deploy high-impact robust ML pipelines, including data extraction, feature development, model training, testing, and deployment
• Continuously monitor, evaluate, and optimize the performance of deployed models, ensuring they meet business goals and provide high-quality user experiences.
• Work closely with Product, Engineering, and Data Science teams to align on product requirements, set expectations, and deliver machine learning-driven solutions that improve user engagement

Your Background:

• 8+ years of industry experience building production Machine Learning systems
• MSc or Ph.D. in Computer Science, Machine Learning, Statistics, Mathematics, or a related field
• Experience with deep learning technologies for recommendation systems, including TensorFlow, PyTorch, or similar frameworks
• Proficiency in building and deploying full-stack machine learning pipelines: data extraction, data mining, model training, feature development, testing, and deployment.
• Solid understanding of statistical concepts such as hypothesis testing, regression analysis, and performance evaluation metrics for machine learning.
• Ability to deep dive into individual components and systems, as well as understand the overall architecture of machine learning solutions.

#LI-Hybrid #LI-SC1

About the Role:

The Machine Learning team at Tubi drives the innovation behind personalized user experiences. With the largest inventory in the industry and hundreds of millions of viewers, we tackle problems in the space of recommendations, search, content understanding, and ads optimization that shape the future of streaming.

We are seeking a Director of Machine Learning Engineering and Infrastructure to lead a hybrid team bridging advanced ML engineering with world-class infrastructure design. In this role, you will own the strategic direction and execution for scaling our machine learning capabilities while ensuring our distributed systems and infrastructure can support innovation at massive scale. You will combine technical depth with leadership excellence to guide teams that deliver both foundational ML systems and high-performance distributed services.

What You'll Do: 

• Lead and manage high-performing teams across ML engineering and ML infrastructure, fostering a culture of innovation, collaboration, and growth.
• Define and execute the strategic roadmap for ML systems, including recommendation, personalization, and ads optimization.
• Oversee the design, development, and deployment of scalable ML pipelines: data ingestion, feature engineering, model training, evaluation, and serving.
• Architect distributed systems to support ML workloads at scale, ensuring reliability, observability, and operational excellence.
• Partner closely with Product, Engineering, and Content teams to align on business goals and deliver impactful ML-driven experiences.
• Support best practices in experimentation, evaluation, and ML system monitoring.
• Ensure cost efficiency, scalability, and performance in ML infrastructure investments.

Your Background:

• 10+ years of industry experience spanning machine learning engineering and distributed systems.
• 3+ years of leadership and management experience, with a proven ability to build and lead strong technical teams.
• MSc or Ph.D. in Computer Science, Machine Learning, or related field, or equivalent practical experience.
• Proven expertise in building and deploying end-to-end ML systems at scale, including recommendation and personalization systems.
• Strong background in distributed systems architecture, including low-latency services, streaming platforms, and large-scale serving.
• Hands-on experience with deep learning frameworks (e.g., TensorFlow, PyTorch) and ML infrastructure technologies.
• Track record of delivering high-quality, scalable, and fault-tolerant systems.
• Excellent communication skills and ability to influence product and technical strategy.
• Proven experience deploying large-scale serving systems on AWS and demonstrated expertise in leveraging Databricks for large-scale data processing and ML workflows

What We're Looking For

We are looking for a Senior Application Security Engineer to help secure our AI, machine learning, and inference platforms. This is a hands on technical role focused on building security into modern AI infrastructure, inference systems, and developer platforms.

You will work closely with platform engineers, ML engineers, and infrastructure teams to identify risks, design secure architectures, and build security tooling that enables engineers to move quickly and safely.

This role is execution focused. You will drive technical implementation, perform deep security reviews, and help build out our application security capabilities alongside the CISO and engineering leadership.

This role can be based out of one of our office hubs (San Francisco or Seattle) with in-office requirements of at least 2 days per week, plus occasional team/company offsites. We are not able to offer visa sponsorship for this position at this time.

What You’ll Do

Secure AI and Machine Learning Systems

• Perform threat modeling across AI platforms, inference services, and ML pipelines
• Identify risks such as prompt injection, model extraction, adversarial inputs, and data leakage
• Review model serving architectures and inference pipelines
• Partner with ML engineers to secure training, fine tuning, and deployment workflows
• Help design isolation and security controls for multi tenant AI workloads

Application Security Engineering

• Perform architecture and design security reviews
• Conduct targeted code reviews for high risk components
• Identify security gaps in APIs, micro-services, and distributed systems
• Build secure patterns for authentication, authorization, and service to service communication
• Help engineering teams implement secure defaults and guardrails

Inference Platform Security

• Secure customer facing inference APIs and services
• Protect against abuse, model extraction, and adversarial behavior
• Design rate limiting, isolation, and workload protection controls
• Build monitoring and detection for anomalous inference behavior

AI Supply Chain and Model Security

• Evaluate open source models and dependencies
• Secure model artifacts and distribution pipelines
• Implement integrity validation and provenance controls
• Help secure container images and runtime environments

Security Automation and Tooling

• Build security automation for AI and application pipelines
• Integrate security scanning into CI/CD workflows
• Develop tooling to help engineers detect and fix issues early
• Improve developer experience with security guardrails

What You'll Need

Required Experience

• Strong background in application security engineering
• Experience performing threat modeling and architecture reviews
• Experience securing APIs and distributed systems
• Experience working in cloud environments such as AWS, GCP, or Azure
• Experience with containers and Kubernetes
• Strong scripting or programming skills such as Python, Go, or similar
• Experience working closely with engineering teams to implement security improvements

AI and Machine Learning Experience

• Experience securing ML pipelines, inference systems, or data platforms
• Familiarity with risks such as prompt injection, model extraction, and adversarial inputs
• Experience reviewing model serving architectures
• Understanding of training data security and data leakage risks

It's a Strong Plus If You Have

• Red team or offensive security experience
• Experience crafting payloads and evaluating CVEs for exploitability in diverse environments
• Experience with GPU infrastructure or high performance computing
• Experience with Hugging Face, PyTorch, TensorFlow, or similar frameworks
• Experience with LLM systems, RAG pipelines, or agent frameworks
• Experience building security automation pipelines
• Experience securing multi tenant infrastructure

What Success Looks Like

• Security is embedded into AI platform architecture early
• Engineering teams ship quickly with secure defaults
• Inference platforms are resilient against abuse and extraction
• Model pipelines are secure and auditable
• Security tooling scales with engineering growth

Why This Role Matters

This role sits at the intersection of application security, AI infrastructure, and developer platforms. You will help define how we secure modern AI systems while enabling engineers to move quickly and safely.

You will work closely with engineering leadership and the CISO to build practical, scalable security capabilities that support rapid innovation in AI and machine learning.

Compensation

The Role

The Risk Data Science team is looking for a Data Scientist/Senior Data Scientist to develop advanced machine learning and statistical models, guide measurement, strategy, and data-driven decision making to support various credit risk and operational areas at SoFi. The Data Scientist will work closely with Credit, Risk, Product, Engineering, and Operations teams to design solutions for underwriting, portfolio management, loss mitigation, and loss forecasting etc. These tasks involve researching and applying state of the art modeling methodologies to solve complex business problems. This role is very rewarding as your work will have a direct and immediate impact on the business’ profitability.

What You’ll Do

• Develop, implement, and continuously improve machine learning and statistical models that support various credit, risk, and operational procedures including but not limited to underwriting, portfolio management, loss mitigation, and loss forecasting, etc.
• Present model performance and insights to Credit, Risk, and Business Unit leaders.
• Proactively identify opportunities to apply advanced modeling approaches to solve complex business problems.
• Explore and leverage in-house and external data sources to enhance model predictive power.
• Collaborate with the Model Risk Management team to demonstrate models are developed with high level rigor that satisfy Model Risk Management and Governance requirements. 
• Perform ongoing monitoring of the models through the construction of dashboards and KPI tracking
• Collaborate with the Product and Engineering teams to improve the model development, deployment, monitoring, and model re-calibration/re-build process..
• Explore and apply in-house and open-source machine learning and statistical tools and algorithms to develop and improve models.

What You’ll Need

• Master’s degree in Statistics, Econometrics, Mathematics, Operations Research, Physics, Computer Science, Engineering, or quantitative field required. PhD degree preferred.
• 2+ years of relevant work experience in building and implementing machine learning and statistical models.
• Excellent logic reasoning and communication abilities when interpreting business requirements and translating them into effective data solutions.
• Strong skills in writing efficient SQL queries and Python code to create complex attributes, especially with large datasets.
• Strong sensitivity to details in data and proactively investigate them to uncover unknown patterns.
• Strong knowledge of databases and related languages/tools such as SQL, NoSQL, Hive, etc. 
• Demonstrated sophisticated experience in building efficient and reliable pipelines that interact with large datasets stored in SageMaker and Snowflake, automating recurring processes such as data extraction and processing, feature selection, model training, model monitoring, and generating documentation templates to support reproducibility and cross-functional collaboration.
• Excellent knowledge of machine learning and statistical modeling methods for supervised and unsupervised learning. These methods include (but are not limited to) regression, classification, clustering, outlier detection, novelty detection, decision trees, nearest neighbors, support vector machines, ensemble methods and boosting, neural networks, deep learning and its various applications. Continuously following the advancement of machine learning and artificial intelligence to update your knowledge and skills in order to solve business problems with the most efficient methodologies
• Strong programming skills in Python and machine learning libraries (e.g., sklearn, lightgbm, xgboost, pytorch, tensorflow, keras, etc.)

Nice To Have

• Experience in a lending organization.
• Experience with model documentation and delivering effective verbal and written communication.
• Experience in working closely with Product, Engineering, and Model Risk Management teams.
• Experience with AWS or GCP.

Founded in Silicon Valley in 2009 by Marc Andreessen and Ben Horowitz, Andreessen Horowitz (aka a16z) is a venture capital firm that backs bold entrepreneurs building the future through technology. We are stage agnostic. We invest in seed to venture to growth-stage technology companies, across AI, bio + healthcare, consumer, crypto, enterprise, fintech, games, and companies building toward American dynamism. a16z has $100B+ under management across multiple funds.

We’ve established a team that is defined by respect for the entrepreneur and the company-building process; we know what it’s like to be in the founder’s shoes. We’ve invested in companies like Anduril, Airbnb, Coinbase, Cursor, Databricks, Deel, Figma, GitHub, Roblox, SpaceX, and Stripe. Our team is at the forefront of new technology, helping founders and their companies impact and change the world.

The Role

You will join a team of highly autonomous, senior ICs developing both software capabilities for the firm’s investing and operating functions, and external-facing products for the firm’s partners, portfolio, and investors. This role specifically involves leading the technical strategy, architecture, and implementation of machine learning systems that power the firm’s internal and external-facing applications. You will be responsible for the entire model lifecycle, from feature engineering and training to large-scale, low-latency serving and robust MLOps infrastructure.

The Applied Software Group operates in the mode of an internal startup. We own an exceptionally tight feedback loop and take on requests from all corners of the firm. This is a high-visibility role that requires a clear architectural vision, strong software engineering fundamentals, exacting communication skills, and the ability to engage and educate our firm on the technology we are developing and shipping.

This role requires an in-office presence 2-3 days a week in our San Francisco, CA office.

To join our team, you should be excited to:

• Architect, build, and maintain scalable and reliable ML production pipelines (data, training, and inference)
• Collaborate with the Applied Software Group (ASG) team to integrate ML models into user-facing products serving the investing partnership and our operating teams
• Set and evolve MLOps standards for the team, including producibility, automated testing, CI/CD, observability, and model governance across the firm.
• Research and lead the implementation of model serving strategies (e.g., real-time APIs, batch processing) to meet strict latency and throughput requirements
• Drive the technical roadmap for core ML infrastructure, influencing platform choices and building cloud-native systems primarily on GCP.
• Operate with high autonomy, owning outcomes end-to-end, proactively identifying gaps, and raising the technical bar for the team.
• Set a high standard for code quality and system design, producing clear, principled, and extensible solutions that scale with the firm.
• Bring positive energy in every interaction with the team and foster inclusive collaboration
• Learn the inner workings of VC and work closely with the investing partnership

Minimum Qualifications

• 2+ years of professional experience as a Machine Learning Engineer or similar role.
• Strong mathematical intuition and problem-solving skills
• Expertise in Python and its statistical modeling ecosystem (e.g. NumPy, scikit-learn, etc.)
• Solid experience with major ML frameworks like PyTorch, TensorFlow, or JAX
• Proven experience deploying and managing ML models in a production environment using tools like Kubeflow, MLflow, Airflow, or TFX
• Strong understanding of containerization technologies (e.g., Docker, Kubernetes/GKE) as applied to ML serving
• Familiarity with cloud computing environments (preferably GCP), including data storage and compute services
• Low ego, high empathy, and the capacity to collaborate effectively with diverse teams

The anticipated salary range for this role is between $341,000 - $422,000, actual starting pay may vary based on a range of factors which can include experience, skills, and scope.

This role is eligible to participate in the a16z carry program and various discretionary bonus programs as well as benefit and perquisite plans including health, dental, vision, disability, life insurance, 401K plan, vacation, and sick leave.

We are looking for a Staff Data Scientist for our Engagement Ecosystem. You will shape the future of people-facing and business-facing products we build at Pinterest. Your expertise in quantitative modeling, experimentation and algorithms will be utilized to solve some of the most complex engineering challenges at the company. You will collaborate on a wide array of product and business problems with a diverse set of cross-functional partners across Product, Engineering, Design, Research, Product Analytics, Data Engineering and others. The results of your work will influence and uplevel our product development teams while introducing greater scientific rigor into the real world products serving hundreds of millions of pinners, creators, advertisers and merchants around the world.

What you’ll do

• Develop a deep, nuanced understanding of the Pinterest engagement ecosystem and key product surfaces, quantifying ecosystem-level opportunities and risks.
• Lead projects on:
• Tradeoffs between organic engagement and advertising.
• Deep dives on how engagement metrics impact monetization and retention.
• Understanding and predicting the value of core behaviors (e.g., saving, repinning, board creation) as they relate to downstream business outcomes.
• Designing and evaluating interventions that sustainably boost enterprise metrics across product boundaries.
• Design and productionize robust, scalable ML and evaluation frameworks—spanning forecasting, recommendation, and causal inference.
• Advocate for best-in-class experimentation, instrumentation, and metric design; bridge the gap between short-term proxy metrics and long-term business impact.
• Collaborate across disciplines—Product, Engineering, Research, Business, and Design—translating complex data questions into actionable business insights.
• Mentor and guide junior and senior scientists, fostering intellectual curiosity and driving technical excellence.
• Use advanced ML, causal inference, and generative AI techniques to model and explain complex interactions across the Pinterest ecosystem (e.g., organic engagement, ads, content quality, monetization), turning highly ambiguous business questions into clear, testable hypotheses and decision frameworks for Core and Monetization leadership.
• Apply AI-assisted analysis and developer tools (e.g., copilot, intelligent dashboards) to accelerate exploration, improve code quality, and scale insight generation, ensuring we can quickly evaluate tradeoffs like ad load vs. Pinner engagement.
• Champion responsible and scientifically rigorous use of AI across the ecosystem, setting best practices for instrumentation, experimentation, and metric design so that AI-driven changes balance engagement, revenue, and long-term user trust.

What we’re looking for

• 10+ years of hands-on experience in web-scale data environments, with a track record of solving hard, ambiguous problems in product, engagement, or ecosystem analytics.
• Deep expertise in: Machine Learning (recommendation, ranking, prediction, experimentation), Statistical Modeling & Causal Inference (observational and experimental data), Product analytics/strategy (beyond dashboards: root cause, goaling, design collaboration), Programming in Python/R and advanced SQL/Spark.
• Strong product intuition—ability to scope, question, and design the right solutions for ill-defined, high-impact business problems.
• Scientific rigor and healthy skepticism: You challenge assumptions, find flaws, and drive towards robust, reproducible outcomes.
• Exceptional communication: You make the complex simple, and can influence both technical and non-technical audiences.
• Track record mentoring and growing data talent at the staff/senior IC level.
• Cross-functional leadership and the ability to align competing interests towards shared goals.
• Proven track record applying machine learning and/or causal inference to complex, ambiguous product or marketplace problems at scale (e.g., ads, recommendations, multi-sided ecosystems), from problem framing and experimental design through to influencing senior stakeholders and driving product or policy decisions.
• Hands-on experience with modern AI/ML tools and workflows (such as PyTorch, TensorFlow, scikit-learn, or similar, plus LLM-based assistants or copilots) and comfort rapidly adopting new AI capabilities into day-to-day analysis, prototyping, and experimentation workflows.
• Masters degree in a technical field (e.g., Computer Science, Statistics, Mathematics, Engineering, Social Sciences).

In-Office Requirement Statement:

• We let the type of work you do guide the collaboration style. That means we're not always working in an office, but we continue to gather for key moments of collaboration and connection.
• This role will need to be in the office for in-person collaboration 1-2 times/quarter and therefore can be situated anywhere in the country.

Relocation Statement:

• This position is not eligible for relocation assistance. Visit our PinFlex page to learn more about our working model.

#LI-NM4

Sr. Engineering Manager, AI/ML Serving Platform

The AI/ML Serving Platform team provides foundational tools and infrastructure used by hundreds of AI/ML engineers across Pinterest, including recommendations, ads, visual search, growth/notifications, trust and safety. We aim to ensure that AI/ML systems are efficient, healthy (production-grade quality) and fast (for modelers to iterate upon).

Pinterest is seeking a Sr. Engineering Manager to lead the team that builds the serving and deployment infrastructure for all AI/ML models at Pinterest. Systems include:

• Ultra-high-performance C++ model inference engine for production recommendations and content ranking systems. TorchScript + CUDA Graph models on GPU inference, serving 500+M inferences/second.
• Production GenAI & LLM model inference stack for emerging use cases.
• Model routing, deployment, monitoring. Kubernetes-based provisioning.
• Feature fetching, caching, and logging

What you’ll do

• Lead the team to deliver continual improvements in advanced model architectures, cost-efficient resource utilization, and AI/ML developer productivity.
• Set technical direction for the team based on company and org priorities
• Coach and develop talent on the team.

What we’re looking for:

• Experience managing platform engineering teams with many cross-organizational customers
• Experience leading the development of large-scale distributed serving systems
• Experience with AI/ML inference technologies (e.g. PyTorch, TensorFlow) for online serving at Web scale
• Bachelor’s degree in Computer Science, a related field or equivalent experience.

In-Office Requirement Statement:

• We let the type of work you do guide the collaboration style. That means we're not always working in an office, but we continue to gather for key moments of collaboration and connection.
• This role will need to be in the office for in-person collaboration 1-2 times/quarter and therefore can be situated anywhere in the country. 

Relocation Statement:

• This position is not eligible for relocation assistance. Visit our PinFlex page to learn more about our working model.

#LI-REMOTE

#LI-AH2

As a machine learning intern in our Advance Technology Group at Pinterest, you will be exposed to a full spectrum of ML product development. The team focuses on developing cutting-edge technologies for Pinterest’s visual understanding modules and recommender systems.  You'll conduct research that can be applied across Pinterest engineering teams and engage in external collaborations and mentoring, while also having opportunities to deploy features to hundreds of millions of users or conduct research applicable for paper submissions. We offer a 12-week fall internship program remotely or in our San Francisco, Palo Alto, Seattle, or New York offices.

Note to applicants:

By applying to this role, you will be considered for multiple intern roles open across our various ML teams. Please only apply once within the USA or Canada as multiple applications may delay our recruitment process.

Internships are 12 weeks paid from September 21 - December 11, 2026. Depending on the team, our fall internships will be located either remote or hybrid in San Francisco, Palo Alto, New York or Seattle offices.

What you’ll do:

• Develop and launch new user features using unique internal datasets and ML techniques, especially in recommendation systems, computer vision, representation learning, generative AI, and responsible AI.
• Gain hands-on experience with production ML systems, including algorithmic research, infrastructure, data engineering, training, inference, and product, to deliver innovative solutions. You will be exposed to full-stack production ML systems.
• Leverage frontier AI tools and agents to accelerate engineering implementation, including prototyping and experimentation work.
• Validate AI-generated outputs through testing, code review, and critical thinking, ensuring solutions are accurate, maintainable, secure, and aligned with team standards.
• Use AI to better understand unfamiliar code, investigate bugs, and summarize technical context or documentation.
• Contribute in cutting-edge research in machine learning and artificial intelligence that can be applied to Pinterest problems
• Write clean, efficient, and sustainable code
• Take proactive ownership over the completion and quality of your tasks and project with minimal guidance from your mentor, manager, and peers

What we’re looking for:

• This role will be on our Visual Search or Applied Science teams. We are looking for candidates with experience in Computer Vision, Visual Search, User Understanding, Recommendation Systems, Reinforcement Learning, ML efficiency optimization, Generative AI, and LLMs. 
• Ability to legally work full time (40 hours/week) from September-December 2026
• Working towards a PhD degree in Computer Science, ML, NLP, Statistics, Information Sciences or related field
• Mastery of at least one systems language (Java, C++, Python) and one ML framework (Tensorflow, Pytorch, MLFlow)
• Proficiency with AI-native engineering, including the design of agent-friendly codebases.
• High degree of autonomy in learning new agent-first development tools.
• Strong critical thinking when working with AI-generated suggestions, with a clear approach to validating correctness, performance, security, and maintainability.
• Comfort iterating on prompts, refining workflows, and adapting AI-assisted approaches based on the problem, context, and constraints.
• Experience in research and in solving analytical problems
• Strong communicator and team player. Being able to find solutions for open-ended problems.

Preferred Qualifications:

• • Publications in machine learning, AI, data science, data analytics, statistics, or related technical fields
  • Strong passion for research and for answering hard questions with research
  • Passion for applied ML and the Pinterest product

Why Intern at Pinterest?

• Meaningful Work: Contribute to projects that impact millions of users worldwide.
• Mentorship: Learn from and be guided by experienced engineers and researchers in the field.
• Growth and Development: Participate in professional development workshops and networking events to build your skills and connections.

In-Office Requirement Statement:

• We let the type of work you do guide the collaboration style. That means we’re not always working in an office, but we continue to gather for key moments of collaboration and connection
• This role may require you to be located near an office for in-person collaboration, and therefore may need to be located a commutable distance from one of our Pinterest offices.

At Pinterest we believe the workplace should be equitable, inclusive, and inspiring for every employee. In an effort to provide greater transparency, we are sharing the base salary range for this position. Final salary is based on a number of factors including location, travel, relevant prior experience, or particular skills and expertise.

Information regarding the culture at Pinterest and benefits available for this position can be found here.

US based applicants only

The salary for this position is $12,100 monthly.

#LI-HYBRID
#LI-EB1

As a machine learning intern in our Advance Technology Group at Pinterest, you will be exposed to a full spectrum of ML product development. The team focuses on developing cutting-edge technologies for Pinterest’s visual understanding modules and recommender systems.  You'll conduct research that can be applied across Pinterest engineering teams and engage in external collaborations and mentoring, while also having opportunities to deploy features to hundreds of millions of users or conduct research applicable for paper submissions. We offer a 12-week fall internship program remotely or in our San Francisco, Palo Alto, Seattle, or New York offices.

Note to applicants:

By applying to this role, you will be considered for multiple intern roles open across our various ML teams. Please only apply once within the USA or Canada as multiple applications may delay our recruitment process.

Internships are 12 weeks paid from September 21 - December 11, 2026. Depending on the team, our fall internships will be located either remote or hybrid in San Francisco, Palo Alto, New York or Seattle offices.

What you’ll do:

• Develop and launch new user features using unique internal datasets and ML techniques, especially in computer vision, generative AI, and responsible AI.
• Gain hands-on experience with production ML systems, including algorithmic research, infrastructure, data engineering, training, inference, and product, to deliver innovative solutions. You will be exposed to full-stack production ML systems.
• Leverage frontier AI tools and agents to accelerate engineering implementation, including prototyping and experimentation work.
• Validate AI-generated outputs through testing, code review, and critical thinking, ensuring solutions are accurate, maintainable, secure, and aligned with team standards.
• Use AI to better understand unfamiliar code, investigate bugs, and summarize technical context or documentation.
• Contribute in cutting-edge research in machine learning and artificial intelligence that can be applied to Pinterest problems
• Write clean, efficient, and sustainable code
• Take proactive ownership over the completion and quality of your tasks and project with minimal guidance from your mentor, manager, and peers

What we’re looking for:

• This role will be on our Visual Search team. We are looking for candidates with experience in Computer Vision, Visual Search, User Understanding, Generative AI, and LLMs. 
• Ability to legally work full time (40 hours/week) from September-December 2026
• Working towards a Master's degree in Computer Science, ML, NLP, Statistics, Information Sciences or related field
• Mastery of at least one systems language (Java, C++, Python) and one ML framework (Tensorflow, Pytorch, MLFlow)
• Proficiency with AI-native engineering, including the design of agent-friendly codebases.
• High degree of autonomy in learning new agent-first development tools.
• Strong critical thinking when working with AI-generated suggestions, with a clear approach to validating correctness, performance, security, and maintainability.
• Comfort iterating on prompts, refining workflows, and adapting AI-assisted approaches based on the problem, context, and constraints.
• Experience in research and in solving analytical problems
• Strong communicator and team player. Being able to find solutions for open-ended problems.

Preferred Qualifications:

• • Publications in machine learning, AI, data science, data analytics, statistics, or related technical fields
  • Strong passion for research and for answering hard questions with research
  • Passion for applied ML and the Pinterest product

Why Intern at Pinterest?

• Meaningful Work: Contribute to projects that impact millions of users worldwide.
• Mentorship: Learn from and be guided by experienced engineers and researchers in the field.
• Growth and Development: Participate in professional development workshops and networking events to build your skills and connections.

In-Office Requirement Statement:

• We let the type of work you do guide the collaboration style. That means we’re not always working in an office, but we continue to gather for key moments of collaboration and connection
• This role may require you to be located near an office for in-person collaboration, and therefore may need to be located a commutable distance from one of our Pinterest offices.

At Pinterest we believe the workplace should be equitable, inclusive, and inspiring for every employee. In an effort to provide greater transparency, we are sharing the base salary range for this position. Final salary is based on a number of factors including location, travel, relevant prior experience, or particular skills and expertise.

Information regarding the culture at Pinterest and benefits available for this position can be found here.

US based applicants only

The salary for this position is $11,00 monthly.

#LI-HYBRID
#LI-EB1

The goal of a Senior Machine Learning Engineer at Scale is to leverage techniques in the fields of generative AI, computer vision, reinforcement learning, and agentic AI to improve Scale's products and customer experience in production environments. Our machine learning engineers take advantage of robust internal infrastructure and  unique access to massive datasets to deliver improvements to our customers.

Our Public Sector Machine Learning team is focused on deploying cutting-edge models to mission-critical government systems through products like Donovan and Thunderforge. Our work spans multiple modalities, with a strong focus on both large language models and computer vision. On the LLM side, we are developing agentic systems that help solve complex operational and planning challenges for government partners. This includes building agent frameworks that integrate with custom retrieval pipelines and production APIs, as well as evaluation tools to benchmark and refine agent behavior. We're also advancing research in areas like reinforcement learning for agentic LLMs, with successful deployment into real-world operational environments. On the computer vision front, we're training advanced models to increase labeling throughput and automate perception tasks. Our efforts include building large-scale fine-tuning pipelines, training models across multiple modalities, and developing generalizable vision foundation models to support a wide range of defense applications.

You will:

• Take state of the art models developed internally and from the community, use them in production to solve problems for our customers and taskers
• Improve and maintain production models through retraining, hyperparameter tuning, and architectural updates, while preserving core performance characteristics
• Collaborate with product and research teams to identify and prototype ML-driven product enhancements, including for upcoming product lines
• Work with massive datasets to develop both generic models as well as fine tune models for specific products
• Build scalable machine learning infrastructure to automate and optimize our ML services
• Serve as a cross-functional representative and advocate for machine learning techniques across engineering and product organizations
• Be comfortable learning new technologies quickly and managing multiple priorities in a fast-paced environment
• Comfortable with light travel (approximately 10%) for customer interaction and team needs
• This role will require an active security clearance or the ability to obtain a security clearance

Ideally You’d Have:

• Extensive experience with GenAI, Agentic AI, natural language processing, deep learning and deep reinforcement learning, or computer vision in a production environment
• Solid background in algorithms, data structures, and object-oriented programming
• Strong programing skills in Python, experience in Tensorflow or PyTorch

Nice to Haves:

• Graduate degree in Computer Science, Machine Learning or Artificial Intelligence specialization
• Experience working with cloud platforms (eg. AWS or GCP) and deploying machine learning models in cloud environments
• Experience with computer vision, generative AI models, large language models, or agentic systems
• Familiarity with ML evaluation frameworks and agentic model design

As a Data Scientist in our Core organization, you will shape the future of people-facing and business-facing products we build at Pinterest. Your expertise in quantitative modeling, experimentation and algorithms will be utilized to solve some of the most complex engineering challenges at the company. You will collaborate on a wide array of product and business problems with a diverse set of cross-functional partners across Product, Engineering, Design, Research, Product Analytics, Data Engineering and others. The results of your work will influence and uplevel our product development teams while introducing greater scientific rigor into the real world products serving hundreds of millions of pinners, creators, advertisers and merchants around the world.

What you’ll do:

• Develop best practices for instrumentation and experimentation and communicate those to product engineering teams to help us fulfill our mission - to bring everyone the inspiration to create a life they love
• Bring scientific rigor and statistical methods to the challenges of product creation, development and improvement with an appreciation for the behaviors of our Pinners
• Build and prototype analysis pipelines iteratively to provide insights at scale while developing comprehensive knowledge of data structures and metrics, advocating for changes where needed for product development
• Work cross-functionally to build and communicate key insights, and collaborate closely with product managers, engineers, designers, and researchers to help build the next experiences on Pinterest

What we’re looking for:

• 5+ years of experience analyzing data in a fast-paced, data-driven environment with proven ability to apply scientific methods to solve real-world problems on web-scale data
• Extensive experience solving analytical problems using quantitative approaches including in the fields of Machine Learning, Statistical Modeling, Forecasting, Econometrics or other related fields
• Experience using machine learning and deep learning frameworks, such as PyTorch, TensorFlow or scikit-learn
• A scientifically rigorous approach to analysis and data, and a well-tuned sense of skepticism, attention to detail and commitment to high-quality, results-oriented output
• Ability to manipulate large data sets with high dimensionality and complexity; fluency in SQL (or other database languages) and a scripting language (Python or R)
• Excellent communication skills and ability to explain learnings to both technical and non-technical partners
• A team player who’s able to partner with cross-functional leadership to quickly turn insights into actions
• Bachelor’s degree in Computer Science, a related field or equivalent experience

Relocation Statement:

• This position is not eligible for relocation assistance. Visit our PinFlex page to learn more about our working model.

#LI-HYBRID

Senior Machine Learning Engineer - Model Evaluations, Public Sector

The Public Sector ML team at Scale deploys advanced AI systems—including LLMs, agentic models, and multimodal pipelines—into mission-critical government environments. We build evaluation frameworks that ensure these models operate reliably, safely, and effectively under real-world constraints. As an ML Engineer, you will design, implement, and scale automated evaluation pipelines that help customers trust and operationalize advanced AI systems across defense, intelligence, and federal missions.

You will:

• Develop and maintain automated evaluation pipelines for ML models across functional, performance, robustness, and safety metrics, including LLM-judge–based evaluations.
• Design test datasets and benchmarks to measure generalization, bias, explainability, and failure modes.
• Build evaluation frameworks for LLM agents, including infrastructure for scenario-based and environment-based testing.
• Conduct comparative analyses of model architectures, training procedures, and evaluation outcomes.
• Implement tools for continuous monitoring, regression testing, and quality assurance for ML systems.
• Design and execute stress tests and red-teaming workflows to uncover vulnerabilities and edge cases.
• Collaborate with operations teams and subject matter experts to produce high-quality evaluation datasets.
• Comfortable with light travel (approximately 10%) for customer interaction and team needs.

This role will require an active security clearance or the ability to obtain a security clearance.

Ideally you’d have:

• Experience in computer vision, deep learning, reinforcement learning, or NLP in production settings.
• Strong programming skills in Python; experience with TensorFlow or PyTorch.
• Background in algorithms, data structures, and object-oriented programming.
• Experience with LLM pipelines, simulation environments, or automated evaluation systems.
• Ability to convert research insights into measurable evaluation criteria.

Nice to haves:

• Graduate degree in CS, ML, or AI.
• Cloud experience (AWS, GCP) and model deployment experience.
• Experience with LLM evaluation, CV robustness, or RL validation.
• Knowledge of interpretability, adversarial robustness, or AI safety frameworks.
• Familiarity with ML evaluation frameworks and agentic model design.
• Experience in regulated, classified, or mission-critical ML domains.

WHAT YOU'LL DO

As our customer base continues to grow with the excitement around BrazeAI, we’re expanding our team! Join our Forward-Deployed Data Scientist group of creative technical experts who partner with customers to ensure their success. In this role, you will:

• Collaborate with customer Analytics/BI teams and Braze colleagues on implementations, including use case definition, data integration, pipeline setup, and ML model configuration
• Extend product capabilities by improving architecture and developing reusable data pipelines, APIs, and components
• Work closely with the RL pipeline development team to refine and advance our reinforcement learning (self-learning) algorithms
• Contribute to shaping BrazeAI product strategy and roadmap through customer-facing insights and technical expertise
• Provide ongoing technical expertise to ensure successful adoption, measurable outcomes, and long-term customer success

WHO YOU ARE

• Education: Bachelor’s degree in Computer Science, Data Science, Mathematics, Engineering, or a related field required; Master’s or PhD in a relevant technical discipline preferred
• Experience: 3–5+ years of hands-on experience as a Data Scientist, Machine Learning Engineer, or similar role working with large-scale data and production environments. Experience in customer-facing or consulting roles is strongly preferred
• Strong technical expertise: Proficient in Python (Pandas) and core ML libraries (TensorFlow, Keras, scikit-learn, CatBoost, XGBoost). Skilled in SQL for querying/manipulating datasets, with experience in machine learning pipelines and model deployment
• Engineering best practices: You write well-structured, modular, documented code; follow strong development practices (Git, CI/CD, testing frameworks, type-hinting, code reviews); and can build scalable, maintainable solutions
• Nice-to-have skills: Experience with DevOps tools (Airflow, Kubernetes, Terraform, GCP), data integration/ETL, and pipeline optimization, or reinforcement learning algorithms
• Customer collaborator: Comfortable working directly with clients and cross-functional teams, aligning stakeholders, and translating technical concepts into clear business value
• Entrepreneurial problem-solver: You identify opportunities and risks early, troubleshoot obstacles, and drive creative solutions
• Continuous learner: You stay current with industry trends, explore new tools/technologies, and thrive in environments that push you to grow
• Clear communicator: Able to explain complex technical ideas persuasively to both technical and non-technical audiences

For candidates based in the United States, the pay range for this position at the start of employment is expected to be between $98,000-$164,000/year, with an expected On Target Earnings (OTE) between $109,000-$183,000/year (including bonus or commission). Your exact offer may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. In addition to cash compensation, this role qualifies for a comprehensive Total Rewards package that includes equity grants of restricted stock (RSUs) so that you will own a piece of our company.

#LI-Hybrid

About World Labs:

We build foundational world models that can perceive, generate, reason, and interact with the 3D world — unlocking AI's full potential through spatial intelligence by transforming seeing into doing, perceiving into reasoning, and imagining into creating. 

We believe spatial intelligence will unlock new forms of storytelling, creativity, design, simulation, and immersive experiences across both virtual and physical worlds.

We bring together a world-class team, united by a shared curiosity, passion, and deep backgrounds in technology — from AI research to systems engineering to product design — creating a tight feedback loop between our cutting-edge research and products that empower our users.

Role Overview

We are looking for a talented Research Scientist with a strong background in generative modeling, particularly diffusion models, to join our modeling team. This role is ideal for candidates with deep expertise in diffusion models applied to images, videos, or 3D assets and scenes.

While not required, experience in one or more of the following areas is a strong plus:

• Large-scale model training
• Data curation for pretraining or post-training
• Tokenizers and VAEs for image, video, or 3D data
• Long-context architectures
• 3D vision

You will collaborate closely with researchers, engineers, and product teams to bring advanced 3D modeling and machine learning techniques into real-world applications, ensuring that our technology remains at the forefront of visual innovation. This role involves significant hands-on research and engineering work, driving projects from conceptualization through to production deployment.

What You Will Do: 

• Design, implement, and train large-scale diffusion models for generating 3D worlds
• Develop and experiment with large-scale diffusion models to add novel control signals, adapt to target aesthetic preferences, or distill for efficient inference
• Collaborate closely with research and product teams to understand and translate product requirements into effective technical roadmaps.
• Contribute hands-on to all stages of model development including data curation, experimentation, evaluation, and deployment.
• Continuously explore and integrate cutting-edge research in diffusion and generative AI more broadly
• Act as a key technical resource within the team, mentoring colleagues, and driving best practices in generative modeling and ML engineering

Key Qualifications:

• 3+ years of experience in generative modeling or applied ML roles, ideally at a startup or other fast-paced research environment
• Extensive experience with machine learning frameworks such as PyTorch or TensorFlow, especially in the context of diffusion models and other generative models
• Deep expertise in at least one area of generative modeling: pre-training, post-training, diffusion distillation, fine-tuning with new conditioning signals, etc for diffusion models
• Strong history of publications or open-source contributions involving large-scale diffusion models
• Strong coding proficiency in Python and experience with GPU-accelerated computing.
• Ability to engage effectively with researchers and cross-functional teams, clearly translating complex technical ideas into actionable tasks and outcomes.
• Comfortable operating within a dynamic startup environment with high levels of ambiguity, ownership, and innovation.
  
  

Preferred Qualifications:

• Contributions to open-source projects in the fields of computer vision, graphics, or ML.
• Familiarity with large-scale training infrastructure (e.g., multi-node GPU clusters, distributed training environments).
• Experience integrating machine learning models into production environments.
• Led or been involved with the development or training of large-scale, state-of-the-art generative models
  
  

Who You Are:

• Fearless Innovator: We need people who thrive on challenges and aren't afraid to tackle the impossible.
• Resilient Builder: Impacting Large World Models isn't a sprint; it's a marathon with hurdles. We're looking for builders who can weather the storms of groundbreaking research and come out stronger.
• Mission-Driven Mindset: Everything we do is in service of creating the best spatially intelligent AI systems, and using them to empower people.
• Collaborative Spirit: We're building something bigger than any one person. We need team players who can harness the power of collective intelligence.

We're hiring the brightest minds from around the globe to bring diverse perspectives to our cutting-edge work. If you're ready to work on technology that will reshape how machines perceive and interact with the world - then World Labs is your launchpad.

Join us, and let's make history together.

Equal Opportunity & Pay Transparency

Equal Employment Opportunity

World Labs is an equal opportunity employer. We do not discriminate on the basis of race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, genetic information, veteran status, or any other characteristic protected under applicable law. We welcome all qualified applicants and are committed to providing reasonable accommodations throughout the hiring process upon request.

California Pay Transparency

In accordance with California law, we disclose the following:

About Scale

At Scale AI, our mission is to accelerate the development of AI applications. For 8 years, Scale has been the leading AI data foundry, helping fuel the most exciting advancements in AI, including: generative AI, defense applications, and autonomous vehicles. With our recent Series F round, we’re accelerating the abundance of frontier data to pave the road to Artificial General Intelligence (AGI), and building upon our prior model evaluation work with enterprise customers and governments, to deepen our capabilities and offerings for both public and private evaluations.

About the ACE team 

The Agent Capabilities & Environments (ACE) team, part of Scale’s Research organization, brings together customer-facing Researchers and Applied AI Engineers. Our core mission includes research on agent environments and RL reward signals, benchmarking autonomous agent performance across real-world scenarios and environments, creating robust data programs to improve Large Language Models (LLMs) agentic capabilities and building foundational tools and frameworks for evaluating models as agents. ACE focuses on autonomous agents that dynamically interact with diverse external environments, including code repositories, GUI interfaces, browsers, and more. 

About This Role

This role is at the intersection of cutting-edge AI research and practical application, with a focus on studying the data types essential for building state-of-the-art agents, such as browser and SWE agents. The ideal candidate will explore the data landscape needed to advance intelligent, adaptable AI agents, guiding the data strategy at Scale to drive innovation. This position requires not only expertise in LLM agents and planning algorithms but also creativity in addressing novel challenges related to data, interaction, and evaluation. You will contribute to impactful research publications on agents, collaborate with customer researchers, and work alongside the engineering team to translate these advancements into real-world, scalable solutions.

Ideally you’d have: 

• Practical experience working with LLMs, with proficiency in frameworks like Pytorch, Jax, or Tensorflow. You should also be adept at interpreting research literature and quickly turning new ideas into prototypes.
• A track record of published research in top ML venues (e.g., ACL, EMNLP, NAACL, NeurIPS, ICML, ICLR, COLM, etc.)
• At least three years of experience addressing sophisticated ML problems, either in a research setting or product development.
• Strong written and verbal communication skills and the ability to operate cross-functionally. 

Nice to have:

• Hands-on experience with open source LLM fine-tuning or involvement in bespoke LLM fine-tuning projects using Pytorch/Jax. 
• Hands-on experience and publications in building applications and evaluations related to AI agents such as tool-use, text2SQL, browser agents, coding agents and GUI agents.
• Hands-on experience with agent frameworks such as OpenHands, Swarm, LangGraph, etc.
• Familiarity with agentic reasoning methods such as STaR and PLANSEARCH 
• Experience working with cloud technology stack (eg. AWS or GCP) and developing machine learning models in a cloud environment.

Our research interviews are crafted to assess candidates' skills in practical ML prototyping and debugging, their grasp of research concepts, and their alignment with our organizational culture. We will not ask any LeetCode-style questions.

About the role

In the role of senior machine learning engineer, you will report to the director of data science and machine learning in the Center for Data and Insights, working from Los Angeles or San Francisco area in California.

In this Machine Learning Engineer role focused on Algorithm Development and End-to-End Production at our VOD streaming company, you will lead the creation and deployment of scalable ML models to detect account sharing fraud, drawing from user behavior data like geolocation, concurrent sessions, and device profiles. This position is essential for collaborating with data scientists, product analysts, and data engineers,  translating research prototypes into production systems that enhance detection accuracy while minimizing user disruption, aligning with proven strategies from streaming giants to boost subscriber growth.

We work a hybrid schedule, in-office three days a week; Tuesday, Wednesday, Thursday. 

Core Areas of Responsibility

• Design and implement machine learning algorithms, including anomaly detection models, to identify unauthorized account sharing in real-time.
• Develop end-to-end ML pipelines for data preprocessing, model training, evaluation, and deployment using cloud platforms.
• Optimize models for performance, scalability, and efficiency to handle high-volume streaming data.
• Integrate ML solutions with existing systems via APIs and establish monitoring for model drift and retraining.
• Collaborate on A/B testing and iteration to refine algorithms based on feedback and evolving evasion tactics.

About You

• Experience: You bring 5+ years of hands-on experience in ML engineering, with a track record in fraud or anomaly detection systems, ideally in online, consumer facing products and services.
• Technical Skills: Expert in Python and frameworks like TensorFlow, PyTorch, or XGBoost, with proficiency in MLOps tools such as MLflow, Docker, and cloud services like AWS SageMaker, Databricks MLFlow, etc. Experience with cloud computing infra and tooling such as AWS/GCP.
• Cross-Functional Collaborations: Experienced in working with data scientists, engineers, and product teams to deploy models that support business goals like revenue optimization.
• Communication Skills: Strong ability to document technical processes and explain algorithm decisions to diverse stakeholders for seamless integration.
• Education Background: Possess a master's degree in computer science, machine learning, or a related quantitative field, with certifications in cloud ML being a plus.
• Communication Skills: Excellent verbal and written communication skills, with the ability to convey complex technical concepts to non-technical stakeholders.

Educational Background: Graduate degree (MS or PhD) in Computer Science, Statistics, or a related field.

About the Team

Our team is composed of passionate Machine Learning Engineers and Data Scientists who have already made a significant impact across our product offerings, content strategy, and user engagement metrics. As we expand our scope, our team is poised to become the cornerstone of innovation and growth across various business verticals within the company. We are dedicated to leveraging advanced machine learning techniques to continue transforming how users interact with and enjoy our offering of video contents and all other services.

Why you will love working at Crunchyroll

In addition to getting to work with fun, passionate and inspired colleagues, you will also enjoy the following benefits and perks:

#LifeAtCrunchyroll #LI-Hybrid

About the role

In the role of Senior Machine Learning Engineer for Recommendation and Personalization, you will report to the Director of Data Science and Machine Learning in the Center for Data and Insights, working from Los Angeles or San Francisco area in California. You will lead the research, development, and test of advanced models, and work together with product and engineering partners to deliver tailored experiences for our fans across the anime ecosystem, including anime video recommendations, digital manga suggestions, merchandise personalization, anime-themed gaming, music, and more. This position will collaborate closely with scientists, engineers, product owners, to prototype innovative algorithms, evaluate their impact, and integrate them into production systems that drive user engagement, retention, discovery, and satisfaction.

We work a hybrid schedule, in-office three days a week; Tuesday, Wednesday, Thursday. This position can be based in our Los Angeles or San Francisco offices.

Core Areas of Responsibility

• Research, design, and implement machine learning algorithms for recommendation systems, including collaborative filtering, content-based models, and deep learning, and generative recommendation approaches to personalize content discoveries.
• Co-develop end-to-end ML pipelines for data ingestion, feature engineering, model training, evaluation, and deployment using scalable cloud platforms with engineers.
• Optimize models for accuracy, latency, and scalability to handle massive user interaction data from streaming and multi-platform, multi-domain experiences across the fandom.
• Integrate personalization solutions with other services, and implement monitoring for model performance, bias detection, and automated retraining.
• Collaborate on A/B testing, experimentation, and iterative improvements to refine recommendations based on user feedback and evolving content trends.

About You

We get excited about candidates, like you, because you have:

Experience: You bring 8+ years of hands-on experience in applied machine learning, with a proven track record in building recommendation systems or personalization engines, ideally in media, entertainment, or e-commerce platforms.

Technical Skills: Expert in Python and frameworks like TensorFlow, PyTorch, or Scikit-learn, with proficiency in MLOps tools such as MLflow, Docker, and cloud services like AWS SageMaker, Databricks, or similar. Experience with big data tools (e.g., Spark) and cloud infrastructure (e.g., AWS/GCP) for handling large-scale datasets.

Cross-Functional Collaborations: Experienced in partnering with data scientists and analysts, engineers, and product teams to deploy models that align with business objectives like increasing user retention and content consumption.

Communication Skills: Strong ability to document research findings, explain algorithmic choices, and present results to diverse stakeholders for effective adoption.

Educational Background: Graduate degree (MS or PhD) in Computer Science, Machine Learning, Statistics, or a related quantitative field, with publications or contributions in recommendation systems being a plus.

About the Team

Our team is composed of passionate Machine Learning Engineers and Data Scientists who have already made a significant impact across our product offerings, content strategy, and user engagement metrics. As we expand our scope, our team is poised to become the cornerstone of innovation and growth across various business verticals within the company. We are dedicated to leveraging advanced machine learning techniques to continue transforming how users interact with and enjoy our offering of video contents and all other services.

Why you will love working at Crunchyroll

In addition to getting to work with fun, passionate and inspired colleagues, you will also enjoy the following benefits and perks:

#LifeAtCrunchyroll #LI-Hybrid

As a Hunter Solutions Architect on the Digital Natives team, you will shape the future of the big data landscape by working with the most sophisticated data engineering and data science teams in the world.

Reporting to the Field Engineering Manager, you will collaborate with customers, product teams, and the broader customer-facing team to develop architectures and solutions using our platform and APIs. You will guide customers through the competitive landscape, best practices, and implementation; and develop technical champions along the way.

The impact you will have:

• You will partner with the sales team and provide technical leadership to help customers understand how Databricks can help solve their business problems.
• Work directly with prospective, greenfield, customers on their 0 to 1 journey with Databricks 
• Consult on Big Data architectures, implement proof of concepts for strategic projects, spanning data engineering, data science and machine learning, and SQL analysis workflows. As well as validating integrations with cloud services, home grown tools, and other 3rd party applications
• Collaborate with your fellow Solutions Architects, using your skills to support each other and our users
• Become an expert in, promote, and recruit contributors for Databricks inspired open-source projects (Spark, Delta Lake, and MLflow) across the developer community.

What we look for:

• 5+ years in a data engineering, data science, technical architecture, or similar pre-sales/consulting role
• Experience building distributed data systems at scale 
• Comfortable programming in, and debugging, Python and SQL
• Experience using agentic coding harnesses and demonstrable experience scaling your work leveraging them 
• Have built solutions with public cloud providers such as AWS, Azure, or GCP
• Expertise in one of the following:
• Data Engineering technologies (Ex: Spark, Hadoop, Kafka)
• Data Science and Machine Learning technologies (Ex: pandas, scikit-learn, pytorch, Tensorflow)
• Available to travel to customers in your region
• [Desired] Degree in a quantitative discipline (Computer Science, Applied Mathematics, Operations Research)
• Nice to have: Databricks Certification

NOTE: If your experience isn’t a perfect match for the job description, but you feel like you’re a good fit for the role, apply.  We want to work with smart, passionate, people who are good at their job, and, a lot of times, those people have non-linear career paths.

Your Impact at LILA

Lila is building a platform where AI and automation co-evolve to solve the hardest problems in medicine. Within Life Science AI (LSAI), the Foundation Models team researches and develops large-scale generative models and reasoning frameworks that power automated scientific discovery across Lila's life science domains.

We are seeking a Principal or Senior Principal Scientist to join this team as a core contributor. You will define and drive research at the intersection of state-of-the-art machine learning and life science data, spanning biological sequences, molecular structures, and multimodal experimental data. As part of a dynamic team, you will design and implement foundational models end to end, from problem formulation and architecture through training at scale, evaluation, and integration into Lila's closed-loop discovery engine.

This is a high-impact IC role for someone who operates at the frontier of generative AI applied to biology. You will shape the technical agenda for foundation model research, collaborate closely with experimental scientists to close the computational-experimental loop, and represent Lila's work to the broader scientific community.

What You'll Be Building

• Drive research on foundation models for life science applications, including but not limited to biological sequence design, structure prediction, and multimodal scientific reasoning
• Design, train, and evaluate large-scale generative models on biological and chemical data, integrating domain-specific constraints and priors
• Contribute to the end-to-end ML process within Lila's "Lab-in-the-Loop" lifecycle: steer data generation strategy, build pipeline models, and design feedback loops where experimental results improve model performance
• Translate complex biological questions into well-defined ML problems and interpret model outputs in collaboration with wet-lab scientists and computational biologists
• Advance research standards and methodology within the foundation models program, contributing insights that influence approaches across adjacent teams
• Represent Lila's foundation model research externally through publications at premier venues, conference presentations, and community engagement

What You’ll Need to Succeed

• PhD in Computer Science, Machine Learning, Computational Biology, or a related quantitative field
• Multiple high-impact first-author or senior-author publications at premier venues (NeurIPS, ICML, ICLR, Nature Methods, Nature Biotechnology, or equivalent)
• Deep expertise in large-scale generative model architectures and training, with hands-on experience training models on distributed infrastructure
• Demonstrated ability to formulate and drive research programs independently, from problem definition through publication and deployment
• Fluency across ML and at least one life science domain (molecular biology, genomics, protein engineering, nucleic acid design, or related), with experience designing computational experiments grounded in biological reality
• Strong track record of cross-functional collaboration with experimental scientists, translating between ML and biology
• Expertise in ML frameworks (PyTorch, JAX, or TensorFlow) and experience with large-scale distributed training infrastructure (AWS, GCP, or on-prem clusters)

Bonus Points For

• Experience in computational protein design, particularly antibody and nanobody engineering
• Experience designing biological sequences or molecular structures with demonstrated wet-lab validation
• Contributions to open-source ML tools, frameworks, or benchmark datasets for scientific applications
• Experience with agentic frameworks or active learning loops in scientific contexts
• High-impact publications or open‑source contributions in AI for Science in relevant venues (NeurIPS, ICML, ICLR, AAAAI, Nature Methods, Nature Biotechnology, or equivalent)

Your Impact at LILA

Lila Sciences is the world’s first scientific superintelligence platform and autonomous lab for life, chemistry, and materials science.  We are pioneering a new age of boundless discovery by building the capabilities to apply AI to every aspect of the scientific method.  We are introducing scientific superintelligence to solve humankind's greatest challenges, enabling scientists to bring forth solutions in human health, climate, and sustainability at a pace and scale never experienced before. Learn more about this mission at  www.lila.ai

At Lila, we are uniquely cross-functional and collaborative. We are actively reimagining the way teams work together and communicate. Therefore, we seek individuals with an inclusive mindset and a diversity of thought. Our teams thrive in unstructured and creative environments. All voices are heard because we know that experience comes in many forms, skills are transferable, and passion goes a long way.

If this sounds like an environment you’d love to work in, even if you only have some of the experience listed below, please apply.

What You'll Be Building

• Agentic AI for science: Systems that perform sequential decision‑making and multi‑step reasoning to solve domain‑specific problems.
• Workflow/code generation: From natural language intent to typed, executable steps for lab instruments.
• Evaluation & reliability: Benchmarks, test suites, and telemetry to measure capability and quantify progress toward scientific goals.

What You’ll Need to Succeed

• PhD or Masters in a quantitative discipline (e.g., Computer Science, Physics, Mathematics, Engineering) with a strong background in machine learning and one domain of science (e.g. biology or materials science).
• Strong grasp of LLMs and agent architectures (planning, tool use, structured function calling, code generation) and how to adapt them to domains.
• Proficiency in modern ML frameworks (e.g., PyTorch, TensorFlow, JAX) and experience implementing scalable solutions for complex tasks.
• Comfort collaborating across disciplines and interfacing with simulations and real lab systems.

Bonus Points For 

• Building long‑horizon agents or RL for control/decision‑making; experience with model‑based or offline RL.
• Designing domain‑specific benchmarks and evaluation harnesses for complex scientific tasks.
• Digital‑twin development, calibration, and sim‑to‑real transfer.
• Publications or open‑source contributions in AI for science (especially publications in top-tier conferences like NeurIPS, ICML, AAAI, ICLR).

Location

San Francisco, CA or Cambridge, MA (Hybrid and On-Site available depending on team needs).

Your Impact at LILA

Lila is building a platform where AI and automation co-evolve to solve the hardest problems in medicine. Within Life Science AI (LSAI), ML engineers build and operate the systems that turn generative models and reasoning frameworks into production capabilities powering automated scientific discovery across Lila's life science domains.

We are seeking a Principal ML Engineer to design, build, and scale the ML infrastructure behind models spanning biological sequence design, molecular structure prediction, antibody engineering, and multimodal scientific reasoning. You will own critical systems end to end, from training pipelines and distributed compute to model deployment and integration into Lila's closed-loop discovery engine.

This is a high-impact IC role for someone who operates at the intersection of ML systems engineering and life science applications. You will shape the technical direction for how ML models are trained, evaluated, and deployed at scale, collaborate closely with AI scientists and experimental researchers to close the computational-experimental loop, and drive Lila's ML infrastructure toward the next generation of capabilities.

What You'll Be Building

• Design, build, and optimize large-scale training pipelines for generative models on biological and chemical data, including distributed training across GPU clusters
• Own production ML systems end to end: model deployment, serving infrastructure, monitoring, and reliability for models used in Lila's scientific workflows
• Architect ML infrastructure that supports rapid iteration across sequence design, structure prediction, and multimodal scientific reasoning workloads
• Drive the engineering side of Lila's "Lab-in-the-Loop" lifecycle: build pipeline models, integrate experimental feedback loops, and ensure model outputs are actionable for downstream scientific workflows
• Define and advance ML engineering standards, tooling, and best practices across the AI organization
• Collaborate with AI scientists to translate research prototypes into robust, scalable production systems, bridging the research-to-deployment gap

What You’ll Need to Succeed

• Master's degree or higher in Computer Science, Machine Learning, or a related quantitative field (or Bachelor's with equivalent professional experience)
• 10+ years of hands-on experience building and operating production ML systems at scale
• Deep expertise in distributed training infrastructure, including experience with large-scale GPU clusters (AWS, GCP, or on-prem)
• Strong software engineering fundamentals: system design, production-grade code, CI/CD, observability, and reliability practices
• Proficiency in ML frameworks (PyTorch, JAX, or TensorFlow) with experience optimizing training and inference performance
• Demonstrated ability to drive technical direction for ML infrastructure independently, from architecture through implementation
• Track record of cross-functional collaboration with research scientists, translating between ML methodology and engineering execution

Bonus Points For

• Experience building training or inference infrastructure for generative models applied to biological sequences, molecular structures, or scientific data
• Experience with agentic frameworks, active learning loops, or closed-loop experimental workflows
• Contributions to open-source ML tools, frameworks, or infrastructure projects
• Familiarity with at least one life science domain (molecular biology, genomics, protein engineering, or nucleic acid design)
• Experience with model evaluation frameworks for scientific applications where ground truth is sparse or delayed

Your Impact at Lila

Lila is building a platform where AI and automation co-evolve to solve the hardest problems in medicine. Within Life Science AI (LSAI), the Foundation Models team researches and develops large-scale generative models and reasoning frameworks that power automated scientific discovery across Lila's life science domains.

We are seeking a Scientist I or II to join this team as a contributor to foundation model research at the intersection of machine learning and life science data. You will work on generative models spanning biological sequences, molecular structures, and multimodal experimental data, contributing to problem formulation, model design, training, evaluation, and integration into Lila's closed-loop discovery engine.

This is an IC role for someone building deep expertise in generative AI applied to biology. You will own research sub-problems end to end, collaborate closely with experimental scientists to close the computational-experimental loop, and contribute to Lila's presence in the broader scientific community.

What You'll Be Building

• Contribute to research on foundation models for life science applications, including biological sequence design, structure prediction, and multimodal scientific reasoning
• Design, train, and evaluate generative models on biological and chemical data, incorporating domain-specific constraints and priors
• Be part of the end-to-end ML process within Lila's "Lab-in-the-Loop" lifecycle: support data generation strategy, build pipeline models, and help design feedback loops where experimental results improve model performance
• Translate biological questions into well-defined ML problems and interpret model outputs in collaboration with wet-lab scientists and computational biologists
• Support research quality and methodology standards within the foundation models program

What You’ll Need to Succeed

• PhD in Computer Science, Machine Learning, Computational Biology, or a related quantitative field (or Master's with equivalent research experience)
• Strong foundation in generative model architectures and training, with hands-on experience in model development and evaluation
• Ability to formulate and execute research independently, from problem definition through experimentation
• Familiarity with at least one life science domain (molecular biology, genomics, protein engineering, nucleic acid design, or related)
• Experience collaborating with experimental scientists or working with biological/chemical data
• Proficiency in ML frameworks (PyTorch, JAX, or TensorFlow) and experience with GPU-based training workflows

Bonus Points For

• Experience in computational protein design or molecular structure prediction
• Experience with active learning loops or closed-loop experimental workflows
• Contributions to open-source ML tools, frameworks, or benchmark datasets for scientific applications
• Familiarity with distributed training infrastructure
• High-impact publications or open‑source contributions in AI for Science in relevant venues (NeurIPS, ICML, ICLR, AAAAI, Nature Methods, Nature Biotechnology, or equivalent)

About Wonderschool

Wonderschool is harnessing the power of technology to provide comprehensive support to childcare providers operating out of their homes as well as in the government and non-profit sectors. Our products enable childcare providers to create high-quality environments and meet the demands of their business, while also helping parents in need of childcare solutions through the creation of an accessible marketplace.

We are seeking a highly skilled and motivated Early Career Software Engineer to join our team.  As the Early Career Software Engineer, you will play a crucial role in leading our software development initiatives, working with a talented team to create innovative solutions. Your expertise in delivery of high-quality software will be essential in driving the development of our cutting-edge applications. 

Role Overview

We are seeking an Early Career Software Engineer with an interest in applied AI to join our growing team. This is a unique opportunity to work in a fast-paced, purpose-driven environment where you can apply your technical skills to create meaningful impact in the childcare ecosystem. You’ll collaborate with cross-functional teams to build scalable, AI-driven solutions that empower childcare providers and improve parent experiences. This position is on site in our San Francisco office, Tuesday, Wednesday, and Thursday to allow for collaboration.

Key Responsibilities

• Design, develop, and maintain robust software solutions with a focus on integrating AI capabilities.
• Collaborate with product managers, designers, and engineers to define requirements and implement innovative features.

• Design and Development of AI Agents: Build modular, task-specific AI agents capable of natural language understanding, dialogue management, and action execution using frameworks such as LangChain or Rasa.
• Debug and troubleshoot technical issues to ensure platform stability.
• Stay current with emerging technologies and best practices in software engineering and AI.

Required Qualifications

• Bachelor’s degree in Computer Science, Engineering, or related field.
• Strong foundation in programming languages (e.g., Python, JavaScript, or TypeScript).
• Familiarity with AI/ML concepts and frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).
• Basic understanding of cloud platforms like Google Cloud Platform and AWS.
• Excellent communication skills and ability to work in a collaborative, in-person environment.

Nice-to-Have Skills

• Strong understanding of machine learning principles, including model development, evaluation, and deployment for real-world applications. 
• Retrieval-Augmented Generation (RAG) Pipelines: Design and deploy RAG pipelines by integrating retrievers with generative models to enable contextual, real-time responses using vectorized knowledge bases.
• Continuous Learning: Incorporate user feedback loops and data augmentation techniques to iteratively improve the performance and relevance of AI agents and RAG pipelines.

What We Offer

• Compensation: Expected salary range is $100K–$120K annually, based on experience and location.
• Health Benefits: 100% coverage for employee premiums and 80% for dependents.
• Wellness: Flexible PTO, mental wellness days, and reimbursements for wellness initiatives.
• Parental Leave: Competitive policies eligible after six months of employment.
• Work Environment: 3-5 Days in our SF Office in Downtown in SF with lunch covered.

Wonderschool’s mission is to ensure every child has access to child care that helps them realize their potential. We are doing this by building a suite of tools to support passionate early care and education professionals to start and run quality and sustainable child care programs. Our tools allow providers to easily manage their students, parents, and school all from one dashboard. Our community allows providers to seek advice, support, and a sense of belonging with a group of like-minded peers.

For parents, our marketplace allows parents to easily search for schools, schedule visits, enroll their children, and make payments.

The first five years of a child’s life are critical for nurturing brain development for success in school and beyond. All children deserve access to a care situation that will allow them to grow into healthy, lifelong learners. By leading a revolution where all children are able to get access to an environment to develop these skills, we can create drastic changes to neighborhoods and societies as a whole.

We are hiring an Applied Scientist to help advance personalization across the Crunchyroll ecosystem. In this role, you will lead the scientific development of recommendation, ranking, and decisioning solutions that improve how fans discover and engage with anime series/movies, manga, merchandise, games, and other areas in the anime fandom. You will partner closely with Machine Learning Engineers, Product, Engineering, Marketing, and Content stakeholders to make Crunchyroll the ultimate destination for anime experience.

About the role

In the role of Senior Applied Scientist for Recommendation and Personalization, you will report to the Director of Data Science and Machine Learning in our Center for Data and Insights. You will own the research and applied science agenda for personalization, from problem framing and data exploration through model development, evaluation, experimentation, and iteration. This role is ideal for someone who enjoys combining strong scientific rigor with product thinking to improve user discovery, engagement, retention, and long-term fan value.

You will work across multiple user touchpoints, including app and web interfaces, lifecycle and promotional email campaigns, and flywheels that connect video, ecommerce, manga, and adjacent experiences. You will help define what great personalization looks like at Crunchyroll, build the evidence to prove impact, and collaborate with engineering partners to ensure the resulting solutions can be productionized effectively.

This position is based in our Los Angeles office, but we will consider our San Francisco office as a secondary location. We work a hybrid schedule, in-office three days a week: Tuesday, Wednesday, and Thursday. 

Core areas of responsibility

• Lead the research and development of recommendation, ranking, retrieval, and personalization methods tailored to Crunchyroll use cases across streaming, manga, ecommerce, and lifecycle marketing surfaces.
• Frame ambiguous business and product questions into clear scientific problems, hypotheses, success metrics, and experimentation plans.
• Design and run robust offline evaluation frameworks for recommender systems, including relevance, diversity, novelty, coverage, calibration, and long-term value metrics.
• Partner with Product, Analytics, and Engineering to define online experiments, interpret results, and turn learnings into roadmap decisions and model improvements.
• Develop user, content, and contextual understanding through feature design, representation learning, segmentation, and behavioral analysis.
• Prototype and evaluate a range of approaches, including collaborative filtering, content-based methods, sequence modeling, deep learning, bandits, causal or uplift methods, and LLM-enabled recommendation techniques where appropriate.
• Analyze user feedback loops and cross-domain interactions to improve discovery across video, merchandise, manga, and other ecosystem experiences.
• Work closely with Machine Learning Engineers to translate promising research into production-ready solutions on our in-house recommendation platform.
• Communicate scientific findings, model tradeoffs, and business implications clearly to technical and non-technical stakeholders.
• Help establish best practices for experimentation, reproducibility, model governance, and scientific documentation within the personalization and recommendations function.

About you

We get excited about candidates, like you, because you have:

Experience: You bring 5+ years of experience in applied machine learning, recommendation systems, search/ranking, experimentation, or a closely related area, with a track record of driving measurable product impact.

Scientific Depth: You have strong foundations in machine learning, statistics, experimental design, and causal thinking, and you know how to choose the right level of modeling complexity for the problem at hand.

Recommendation Expertise: You have hands-on experience with at least some of the following: collaborative filtering, retrieval and ranking systems, representation learning, sequence / generative models, bandits, graph methods, or personalization for consumer products.

Technical Skills: You are highly proficient in Python and comfortable working with common ML libraries such as PyTorch, TensorFlow, Scikit-learn, XGBoost, or similar tooling. Experience working with SQL, distributed data processing, and cloud-based ML workflows is strongly preferred.

Experimentation Mindset: You know how to design offline and online evaluations, reason carefully about metrics, and connect experimental findings to user and business outcomes.

Cross-Functional Collaboration: You have experience partnering effectively with engineers, product managers, analysts, marketers, and business stakeholders to move from idea to execution.

Communication Skills: You can explain sophisticated modeling decisions and ambiguous findings in a clear, decision-oriented way to diverse audiences.

Educational Background: You hold an MS or PhD in Computer Science, Machine Learning, Statistics, Operations Research, Economics, or a related quantitative discipline, or you bring equivalent applied industry experience.

Nice to have

• Experience personalizing content, commerce, media, entertainment, gaming, or subscription products at scale.
• Familiarity with recommender-system failure modes such as popularity bias, cold start, sparse feedback, and feedback loop effects.
• Experience with multi-objective optimization, constrained ranking, or balancing short-term engagement with long-term user value.
• Exposure to generative AI, representation learning, or LLM applications that support recommendation and personalization workflows.
• Published research, patents, or open-source contributions in recommendation systems, personalization, applied machine learning, or experimentation.

About the team

Our centralized DS/ML team serves stakeholders across Finance, Product, Engineering, Marketing, Creatives, and Content Operations with data-driven and ML/AI-powered solutions. Within that broader organization, the Personalization and Recommendation group is building the next generation capabilities to power tailored fan experiences across every major user interface and lifecycle touchpoint. Today, the team includes engineers focused on operationalizing our recommendation platform with strong engineering excellence. This Applied Scientist role complements that foundation by bringing deeper scientific ownership to modeling strategy, evaluation, and experimentation, while partnering closely with an additional MLE hire to accelerate production impact.

Why this role is exciting

• You will help define personalization strategy across multiple surfaces instead of optimizing a single narrow funnel.
• You will work on problems that span content discovery, user engagement, retention, and cross-domain ecosystem value.
• You will influence both what we build and how we measure success, with strong exposure to partners across the business.
• You will join at a formative moment, with the opportunity to shape team standards, technical direction, and long-term roadmap.

Why you will love working at Crunchyroll

In addition to getting to work with fun, passionate and inspired colleagues, you will also enjoy the following benefits and perks:

#LifeAtCrunchyroll #LI-Hybrid