Identity is the key to unlocking the potential of AI. Okta secures AI by building the trusted, neutral infrastructure that enables organisations to safely embrace this new era. This work requires a relentless drive to solve complex challenges with real-world stakes. We are looking for builders and owners who operate with speed and urgency and execute with excellence.

This is an opportunity to do career-defining work. We're all in on this mission. If you are too, let's talk.

AI Infrastructure Architect

About the Role

We are looking for a smart and versatile AI Infrastructure Architect to build and evolve the AI infrastructure and platform that powers our identity security solutions. Your work will enable internal teams and product groups to integrate AI capabilities safely, securely, and at scale,empowering Okta’s mission to protect millions of digital identities worldwide. While your primary focus will be to architect scalable, secure, and resilient infrastructure supporting AI-driven tools, frameworks, and identity services, we value someone who isn’t afraid to get hands-on when needed to help solve complex challenges and drive projects forward.

Key Responsibilities

• Lead AI enablement initiatives, including proof-of-concepts for emerging AI infrastructure technologies and integration approaches.

• Collaborate cross-functionally with engineering, security, data science, and product teams to align AI platform architecture with business and security goals.

• Architect scalable, resilient, and secure AI infrastructure that supports AI-powered tools and features across Okta’s Identity Platform.

• Lead infrastructure decisions across AWS, GCP, or hybrid environments with a focus on secure identity data handling

• Develop and maintain infrastructure-as-code frameworks (e.g., Terraform, Helm) to ensure consistent, reproducible deployment of AI services

• Champion security and compliance by embedding data privacy and identity protection standards directly into the AI platform and infrastructure design.

• Serve as the key advocate and strategist for AI-driven efficiency initiatives across infrastructure platform teams and pre-production systems.

• Implement robust MLOps practices, such as model evaluation, rollback strategies, and A/B testing, to guarantee the reliability and governance of AI in production.

• Drive continuous innovation by staying current with AI and cloud infrastructure trends and evangelizing best practices internally.

Desired Qualifications

• 10+ years in infrastructure or software engineering, with ≥ 2 years building AI/ML systems

• Exceptional systems level thinking and a track record in architecting and building enterprise grade infrastructure

• Deep expertise in cloud platforms (AWS, GCP), distributed systems, and container orchestration (Kubernetes)

• Expected to be very hands-on in order to create, review, and contribute large chunks of quality code

Preferred

• Experience in identity, security, fraud, or risk analytics domains.

• Experience operationalizing large language models or foundation models in production environments.

• Contributions to MLOps or infrastructure open-source projects.

What You’ll Gain

• Opportunity to lead infrastructure shaping AI systems that protect millions of identity transactions.

• Be at the core of building efficient and AI powered enterprise grade solutions that touch internal and external customers alike.

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The design space of biomolecules is unimaginably vast , far beyond what can be explored experimentally. Yet within this space lie molecules with properties essential for new medicines. Our machine learning models learn to design therapeutic biomolecules with specific, desirable functions.

We advance the state of the art in molecular design by training large-scale foundation models and developing cutting-edge generative approaches. The models learn from diverse heterogeneous datasets and are refined through focused fine-tuning and feedback from experiments. Key to progress is a team that combines exceptional machine learning expertise with thorough domain understanding.

You will collaborate closely with other machine learning researchers and engineers, as well as computational and experimental biologists, to advance these models and translate their capabilities into real therapeutic designs.

Responsibilities

• Embody our vision of an interdisciplinary environment and embrace learning about areas outside of your traditional area of expertise

• Develop, implement, train, and iteratively improve state-of-the-art models for biomolecule design

• Analyze, visualize, and communicate results to support team efforts in improving models and data

• Create, deploy, and refine tools for efficient, reliable machine learning experimentation and production

• Work with biologists to collect data for the training and evaluation of generative models of biomolecules

• Provide mentorship and technical direction to team members as appropriate

Qualifications

• 3+ years of hands-on experience developing ML models

• Demonstrated track record of implementing, training, improving advanced machine learning models

• Highly capable programmer fluent in Python ecosystem and PyTorch or similar deep learning framework

• Availability to work with team members across US and Europe, with meetings starting at 8am PT and ending at 7pm CET

• Readiness to travel several times a year for company retreats and business events

Compensation

$200K – $275K + Bonus + Equity

Benefits

• A competitive compensation package

• 30 days paid vacation per year

• Comprehensive health insurance for US based employees

• 401K with company match for US based employees and Direktversicherung for German employees

• Quarterly company-wide retreats

• Monthly wellness benefit

• Budget for multiple visits per year to our offices in Berlin, Palo Alto or Switzerland

• Learning & Development budget to attend conferences, take courses, or otherwise invest in your professional growth, as well as access to the Learning & Development platform EdX and Hone

• A buddy to help you get settled

At Inceptive, we are creating tools to develop increasingly powerful biological software for the rational design of novel, broadly accessible medicines and biotechnologies previously out of reach. Our team brings together vast expertise in molecular biology, machine learning, and software engineering, and we are all working towards becoming interdisciplinary, meaning we deepen the knowledge we have in our area of expertise while also expanding our knowledge of completely new fields.

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As a machine learning engineer on our team, you'll develop advanced ML solutions that directly impact Stripe's payment products and core business metrics.

About the team

The Payments ML Accelerator team is developing foundational ML capabilities that drive innovation across Stripe's payment products. We build deep learning models that tackle Stripe's most complex payment challenges - from fraud detection to authorization optimization - and deliver measurable business impact.

Responsibilities

• Design and deploy deep learning architectures and foundation models to address problems across key payment entities such as merchants, issuers, or customers
• Identify high-impact opportunities, and drive the long-term ML roadmap through well-scoped high-leverage initiatives
• Architect generalizable ML workflows to enable rapid scaling and optimized online performance
• Deploy ML models online and ensure operational stability
• Experiment with advanced ML solutions in the industry and ideate on product applications
• Explore cutting-edge ML techniques and evaluate their potential to solve business problems
• Work closely with ML infrastructure teams to shape new platform capabilities

Who you are

We are looking for ML Engineers who are passionate about using ML to improve products and delight customers. You have experience developing streaming feature pipelines, building ML models, and deploying them to production, even if it involves making substantial changes to backend code. You are comfortable with ambiguity, love to take initiative, and have a bias towards action.

Minimum requirements

• Minimum 7 years of industry experience doing end-to-end ML development on a machine learning team and bringing ML models to production
• Proficient in Python, Scala, and Spark
• Proficient in deep learning and LLM/foundation models

Preferred qualifications

• MS/PhD degree in quantitative field or ML/AI (e.g. computer science, math, physics, statistics)
• Knowledge about how to manipulate data to perform analysis, including querying data, defining metrics, or slicing and dicing data to evaluate a hypothesis
• Experience evaluating niche and upcoming ML solutions

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This role sits at the intersection of AI/ML, autonomy, model lifecycle, infrastructure, and product strategy. The PM partners closely with engineering, AI research, Hivemind Solutions, and field teams to deliver the tooling that enables sovereign autonomy, AI Factories at the edge, and continuous learning,capabilities that are central to Shield AI's strategic direction.

This is a high-impact role for an experienced product leader excited to define how foundation models are trained, validated, governed, and deployed across thousands of autonomous systems in highly contested environments.

Responsibilities:

• AI Model Development & Training Platform

Own the roadmap for foundation model training workflows, including dataset ingestion, curation, labeling, synthetic data generation, domain model training, and distillation pipelines. Define requirements for world models, robotics models, and VLA-based training, evaluation, and specialization. Lead the evolution of MLOps capabilities in Forge, including data lineage, experiment tracking, model versioning, and scalable evaluation suites.

• Data, Simulation & Synthetic Data Factory

Define product requirements for synthetic data generation, simulation-integrated data flywheels, and automated scenario generation. Partner with Digital Twin, Simulation, and autonomy teams to convert natural-language mission inputs into data needs, training procedures, and model variants.

• Safe Deployment & Model Governance

Lead the development of model governance and auditability tooling, including model cards, dataset rights, lineage tracking, safety gates, and compliance evidence. Build guardrails and workflows to safely deploy models onto edge hardware in disconnected, GPS- or comms-denied environments. Partner with Safety, Certification, Cyber, and Engineering teams to ensure traceability and evaluation pipelines meet operational and accreditation requirements.

• Edge Deployment & AI Factory Integration

Partner with Pilot, EdgeOS, and hardware teams to integrate foundation-model-based perception and reasoning into autonomy behaviors. Define requirements for distillation, quantization, and inference tooling as part of the “three-computer” development and deployment model. Ensure closed-loop workflows between cloud model training and edge-native execution.

• Cross-Functional Leadership

Collaborate with Engineering, Research, Product, Customer Engagement, and Solutions teams to ensure model outputs meet mission and platform constraints. Translate advanced AI capabilities into intuitive workflows that platform OEMs and partner nations can use to build sovereign AI factories. Sequence foundational capabilities that unblock autonomy, simulation, and customer-facing product teams.

• User & Customer Impact

Develop deep empathy for ML engineers, autonomy developers, and Solutions engineers who rely on the platform. Capture operational data gaps, mission-driven model needs, and domain-specific specialization requirements. Lead demos and onboarding for model-development capabilities across internal and external teams.

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Job Description:

Stripe's mission is to accelerate global economic and technological development. We handle over $1.4T in payments volume per year, which is roughly 1.3% of the world's GDP.

As an AI/ML Engineering Manager, you will oversee three critical teams within PayIntel, driving the strategic direction and execution for how Payments, and Stripe more generally, adopts AI, and how our suite of performance products leverage ML/AI to provide a consistent experience that generates revenue for Stripe.

Responsibilities:

• Lead the development of our decisioning platform, ensuring that ML/AI decisions are consistent across the lifecycle of a payment regardless of which product makes those decisions.
• Collaborate with the ML Foundations team to develop and deploy Stripe's Foundation Model to risk, conversion and growth opportunities in Payments.
• Extend our performance analytics, observability, and risk management capabilities across Stripe so that users have a consistently high quality performance experience for cost, revenue, and risk across Stripe.
• Expand our Payments Analytics solution to ensure that users are fully aware of performance opportunities and can take full advantage of our suite of products to automate improvements.

Requirements:

• 10+ years of experience building and shipping ML models that power AI/ML product features, with a strong emphasis on modern technologies such as DNNs, Transformers, and Foundation Models.
• 5+ years of experience managing and developing a team of managers, fostering their growth and ensuring alignment with strategic objectives.
• A strong builder mentality, with the ability to define a team's charter and lead the development of complex systems from scratch.
• Proven ability to shepherd large, complex projects and drive transformational change in an organization and with partners that depend on your team's platform services.
• Deep passion for solving really interesting problems, willingness to experiment, engage with customers directly to understand how well our solutions are working, and to build deep knowledge about performance that drives impact across the company.

Preferred Qualifications:

• Experience with a large-scale, data-rich product in a domain such as payments, commerce, search, or social media.
• Knowledge of the challenges and opportunities in applying ML to fraud prevention, consumer intelligence, or financial services.
• Experience building platforms that accelerate service adoption outside your organization with little maintainability overhead.

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This is a unique opportunity to shape the future of intelligent robotics and push the frontier of our capabilities and end product offerings.

Artificial Intelligence could be one of humanity's most useful inventions. At Google DeepMind, we're a team of scientists, engineers, machine learning experts and more, working together to advance the state of the art in artificial intelligence.

As the Director of Product Management for Robotics, you'll provide comprehensive product leadership, guiding the entire lifecycle of our robotic systems from concept to launch and beyond. You'll work closely with our Robotics Research and our Model teams to define and deliver cutting-edge products that integrate artificial intelligence and Gemini model capabilities to achieve enhanced functionality and autonomy.

Key Responsibilities:

• Product Vision & Strategy: Define and evangelize the long-term product vision and strategy for our robotics portfolio.
• Roadmap Development: Own the end-to-end product roadmap for robotics, prioritising features, capabilities, and new product introductions based on market analysis, customer needs/use cases, and technological advancements.
• AI Integration Leadership: Help drive the integration of Gemini models and their capabilities into our robotic systems, identifying opportunities to enhance autonomy, perception, decision-making, and user interaction through intelligent algorithms and machine learning.
• Ecosystem Development: Build and nurture strategic partnerships with key technology providers, academic institutions, and industry players to expand our robotics ecosystem and unlock new capabilities.
• Market & Customer Insight: Identify unmet needs, use cases, emerging trends, and new business opportunities in the robotics space.
• Cross-Functional Leadership: Collaborate closely with Research, Program Management, and Marketing teams to ensure seamless product development, successful launches, and effective go-to-market strategies.
• Performance Measurement: Define and track key product metrics, utilising data-driven insights to evaluate product performance, identify areas for improvement, and inform future product decisions.

Skills & Qualifications:

• Graduate degree in Robotics or Customer Science
• Bachelor's degree in Engineering, Computer Science, Robotics, or a related technical field and a Master's degree in Robotics or Computer Science.
• 10+ years of experience in product management, with at least 5 years in a leadership role focused on AI/Foundation models, robotics, or highly technical hardware/software products.
• At least 5 years of management experience, including managing senior PMs

In addition, the following would be an advantage:

• Demonstrated experience with robotic systems that leverage AI for enhanced functionality, autonomy, or intelligent behaviour.
• Strong understanding of AI/ML concepts, frameworks, and their application in real-world robotic systems.
• Proven ability to define and execute product strategies, build detailed roadmaps, and successfully launch innovative products.
• Exceptional leadership and communication skills, with the ability to manage strategic partnership, influence cross-functional teams, and present complex technical information clearly to diverse audiences.
• Strong analytical and problem-solving skills, with a data-driven approach to product decisions.

The US base salary range for this full-time position is between $272,000 - $383,000 + bonus + benefits.

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We are seeking 2 Research Engineers for the Frontier Safety Risk Assessment team within the AGI Safety and Alignment Team.

As a Research Engineer, you will contribute novel research towards our ability to measure and assess risk from frontier models. This might include:

• Identifying new risk pathways within current areas (loss of control, ML R&D, cyber, CBRN, harmful manipulation) or in new ones;
• Conceiving of, designing, and developing new ways to measure pre-mitigation and post-mitigation risk;
• Forecasting and scenario planning for future risks which are not yet material.

Your work will involve complex conceptual thinking as well as engineering. You should be comfortable with research that is uncertain, under-constrained, and which does not have an achievable “right answer”. You should also be skilled at engineering, especially using Python, and able to rapidly familiarise yourself with internal and external codebases. Lastly, you should be able to adapt to pragmatic constraints around compute and researcher time that require us to prioritise effort based on the value of information.

Although this job description is written for a Research Engineer, all members of this team are better thought of as members of technical staff. We expect everyone to contribute to the research as well as the engineering and to be strong in both areas.

The role will mostly depend on your general ability to assess and manage future risks, rather than from specialist knowledge within the risk domains, but insofar as specialist knowledge is helpful, knowledge in ML R&D and loss of control as risk domains are likely the most valuable.

About You

In order to set you up for success as a Research Engineer at Google DeepMind, we look for the following skills and experience:

• You have extensive research experience with deep learning and/or foundation models (for example, but not necessarily, a PhD in machine learning).
• You are adept at generating ideas and designing experiments, and implementing these in Python with real AI systems.
• You are keen to address risks from foundation models, and have thought about how to do so. You plan for your research to impact production systems on a timescale between “immediately” and “a few years”.
• You are excited to work with strong contributors to make progress towards a shared ambitious goal.
• With strong, clear communication skills, you are confident engaging technical stakeholders to share research insights tailored to their background.

In addition, any of the following would be an advantage:

• Experience in areas such as frontier risk assessment and/or mitigations, safety, and alignment.
• Engineering experience with LLM training and inference.
• PhD in Computer Science or Machine Learning related field.
• A track record of publications at venues such as NeurIPS, ICLR, ICML, RL/DL, EMNLP, AAAI and UAI.
• Experience with collaborating or leading an applied research project.

At Google DeepMind, we value diversity of experience, knowledge, backgrounds and perspectives and harness these qualities to create extraordinary impact. We are committed to equal employment opportunity regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, pregnancy, or related condition (including breastfeeding) or any other basis as protected by applicable law. If you have a disability or additional need that requires accommodation, please do not hesitate to let us know.

At Google DeepMind, we want employees and their families to live happier and healthier lives, both in and out of work, and our benefits reflect that. Some select benefits we offer: enhanced maternity, paternity, adoption, and shared parental leave, private medical and dental insurance for yourself and any dependents, and flexible working options. We strive to continually improve our working environment, and provide you with excellent facilities such as healthy food, an on-site gym, faith rooms, terraces etc.

We are also open to relocating candidates and offer a bespoke service and immigration support to make it as easy as possible (depending on eligibility).

The US base salary range for this full-time position is between $136,000 - $245,000 + bonus + equity + benefits. Your recruiter can share more about the specific salary range for your targeted location during the hiring process.

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Microsoft AI are looking for a talented Member of Technical Staff - Multimodal - MAI Superintelligence Team at their New York office. This role sits at the heart of strategic decision-making, turning market data into actionable insights for a company that's revolutionising AI systems. You'll work directly with leadership to shape the company's direction in the AI market.

About the Role

As a Member of Technical Staff - Multimodal - MAI Superintelligence Team, you will develop algorithms, design model architectures, conduct experiments, champion measurement and evaluation, innovate datasets and data pipelines. You will improve training and deployment efficiency, paying careful attention to detail, persevering, and learning from everyone’s attempts whether successful or not. You will follow a rigorous data-driven approach grounded in meticulous ablation studies and scientific analysis. You will innovate and iterate over ideas, prototypes, and product. You will collaborate closely with teams on infrastructure, data engineering, pre-training, post-training, and product feedback. You will advance the AI frontier responsibly. You will embody our culture and values.

Accountabilities

• Develop algorithms, design model architectures, conduct experiments, champion measurement and evaluation, innovate datasets and data pipelines.
• Improve training and deployment efficiency, paying careful attention to detail, persevering, and learning from everyone’s attempts whether successful or not.

The Candidate we're looking for

Experience:

• Bachelor’s Degree in AI, Computer Science, Data Science, Statistics, Physics, Engineering, or related technical discipline AND 4+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience.

Technical skills:

• Experience with large-scale AI systems — design and deployment of distributed architectures, multimodal or conversational models; proficiency with ML frameworks (e.g., PyTorch, TensorFlow) and cloud/HPC environments (e.g., Azure).
• Expertise in data engineering for foundation models — multimodal dataset design, curation, annotation pipelines, quality evaluation, bias detection, and understanding of privacy, compliance, and Responsible AI principles.
• Background in LLM interaction and deployment — practical work in prompt engineering, safety-aligned evaluation, and integration of conversational AI into production systems.

Personal attributes:

• Cross-functional collaboration and communication — ability to produce clear technical documentation, partner with engineering, product, and design teams, and contribute to knowledge sharing; demonstrated application of emerging AI technologies and best practices.

Benefits

• Software Engineering IC4 – The typical base pay range for this role across the U.S. is USD $119,800 – $234,700 per year.
• There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $158,400 – $258,000 per year.

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