Machine Learning Scientist Jobs

RDQ427R542 At Databricks, we are passionate about enabling data teams to solve the world's most challenging problems — from making the next mode of transportation a reality to accelerating medical breakthroughs. We do this by building and running the world's best data and AI platform, enabling our customers to use deep data insights to improve their businesses. Our ability to execute this mission depends on building trust and recognition within an ever-growing community of data engineers, analysts, scientists, machine learning practitioners, and AI practitioners. Are you the person who is already at every AI meetup in the Bay Area — not to be seen, but because that's where the interesting conversations are happening? Are you a hands-on practitioner in generative AI, agents, and MLOps who can hold your own in a room full of AI engineers and leave them with something they can actually use? As a Senior Developer Advocate for AI and Machine Learning, you'll be the voice of Databricks in the Bay Area AI community and a crucial link between our engineering teams and the global community of data scientists and AI engineers. You'll focus on how practitioners build, evaluate, govern, and ship agentic systems on the Databricks Data + AI Platform — with particular depth in Genie Agents, Genie Code, Genie One, MLflow, and Databricks Unity AI Gateway. You'll translate what you learn from customers and the community into reference implementations, demos, and content that are production-ready rather than merely impressive on stage. This is a role for someone who wants to be in the room. San Francisco and the wider Bay Area are the densest concentration of AI practitioners in the world, and we want Databricks to be present and useful in that community — at meetups, hack nights, demo nights, founder dinners, and the hallway conversations around them. You'll build genuine relationships with the people building the future of AI here, and you'll bring what you hear back to our pro

RDQ427R542 At Databricks, we are passionate about enabling data teams to solve the world's most challenging problems — from making the next mode of transportation a reality to accelerating medical breakthroughs. We do this by building and running the world's best data and AI platform, enabling our customers to use deep data insights to improve their businesses. Our ability to execute this mission depends on building trust and recognition within an ever-growing community of data engineers, analysts, scientists, machine learning practitioners, and AI practitioners. Are you the person who is already at every AI meetup in the Bay Area — not to be seen, but because that's where the interesting conversations are happening? Are you a hands-on practitioner in generative AI, agents, and MLOps who can hold your own in a room full of AI engineers and leave them with something they can actually use? As a Senior Developer Advocate for AI and Machine Learning, you'll be the voice of Databricks in the Bay Area AI community and a crucial link between our engineering teams and the global community of data scientists and AI engineers. You'll focus on how practitioners build, evaluate, govern, and ship agentic systems on the Databricks Data + AI Platform — with particular depth in Genie Agents, Genie Code, Genie One, MLflow, and Databricks Unity AI Gateway. You'll translate what you learn from customers and the community into reference implementations, demos, and content that are production-ready rather than merely impressive on stage. This is a role for someone who wants to be in the room. San Francisco and the wider Bay Area are the densest concentration of AI practitioners in the world, and we want Databricks to be present and useful in that community — at meetups, hack nights, demo nights, founder dinners, and the hallway conversations around them. You'll build genuine relationships with the people building the future of AI here, and you'll bring what you hear back to our pro

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. With over half a billion rides and counting, Lyft is solving hard problems in a flourishing domain with a lot of data and creative solutions in Marketplace, Mapping, Fraud, Growth and beyond. We're actively building the next-generation Machine Learning (ML) platform for low-cost, ultra-immersive transportation to improve people’s lives using modern ML with peta-byte scale data. Our Machine Learning Engineers are excited to work on these challenging problems and redefine solutions to directly impact various aspects of Lyft's primary business. If you are a student with experience in machine learning workflows, passionate about solving challenging problems using data and working in a dynamic, creative, and collaborative environment, this opportunity is for you! Responsibilities: Contribute to the design, build, train and test of Machine Learning models Write production-level code to convert ML models into working pipelines Partner with Product Managers, Data Scientists, and fellow ML Engineers to frame Machine Learning problems within the business context Analyze experimental and observational data, communicate findings to support decisions Participate in code and spec reviews to ensure code quality and distribute knowledge Experience: Currently pursuing a Bachelor's, Master's, or PhD degree in Computer Science or a related technical field from a university in Canada (required) , with a graduation date between December 2027 and Summer 2028 (required). For any candidates who are master's students who worked between their bachelor's and master's programs: candidates should also have less than 2 years of relevant full-time work experience Available during Summer 2027 for the internship in Toronto Good understanding and knowledge of ML libraries like scikit-learn, Tensorflow, PyTorch, Keras, MXNet, et

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. With over half a billion rides and counting, Lyft is solving hard problems in a flourishing domain with a lot of data and creative solutions in Marketplace, Mapping, Fraud, Growth and beyond. We're actively building the next-generation Machine Learning (ML) platform for low-cost, ultra-immersive transportation to improve people’s lives using modern ML with peta-byte scale data. Our Machine Learning Engineers are excited to work on these challenging problems and redefine solutions to directly impact various aspects of Lyft's primary business. If you are a student with experience in machine learning workflows, passionate about solving challenging problems using data and working in a dynamic, creative, and collaborative environment, this opportunity is for you! Responsibilities: Contribute to the design, build, train and test of Machine Learning models Write production-level code to convert ML models into working pipelines Partner with Product Managers, Data Scientists, and fellow ML Engineers to frame Machine Learning problems within the business context Analyze experimental and observational data, communicate findings to support decisions Participate in code and spec reviews to ensure code quality and distribute knowledge Experience: Currently pursuing a Bachelor's, Master's, or PhD degree in Computer Science or a related technical field from a university in Canada (required) , with a graduation date between December 2027 and Summer 2028 (required). For any candidates who are master's students who worked between their bachelor's and master's programs: candidates should also have less than 2 years of relevant full-time work experience Available during Summer 2027 for the internship in Toronto Good understanding and knowledge of ML libraries like scikit-learn, Tensorflow, PyTorch, Keras, MXNet, et

Who we are About Stripe Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world’s largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone’s reach while doing the most important work of your career. About the team Stripe processes over $1T in payments volume per year, which is roughly 1% of the world’s GDP. The tremendous amount of data makes Stripe one of the best places to do machine learning. The ML Infra team builds services and tools that power every step in the ML lifecycle, including data exploration, feature generation, experimentation, training, deploying, serving ML models, and building LLM applications. With the phenomenal developments happening in the field of AI, we are positioned to accelerate the adoption of AI/ML across all parts of the company by building highly scalable and reliable foundational infrastructure. What you’ll do You will work closely with machine learning engineers, data scientists, and product engineering teams to enable seamless end-to-end experience in building solutions across data, analytics, and AI/ML platforms. You will build the next generation of ML Infra services and major new capabilities that substantially improve ML development velocity and MLOps maturity across the company. Responsibilities Designing and building scalable, reliable, and secure services for notebooks, ML model training, experimentation, serving, and LLM applications across multiple regions. Creating services and libraries that enable ML engineers at Stripe to seamlessly transition from experimentation to production across Stripe’s systems. Working directly with product teams and ML engineers to improve their day-to-day pr

Who we are About Stripe Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world's largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone's reach while doing the most important work of your career. About the team Stripe processes over $1.9T in payments volume per year, which is roughly 1.6% of the world's GDP, for millions of customers from startups to enterprises. The tremendous amount of data makes Stripe one of the best places to do machine learning. While being an integral part of almost every product line at Stripe (e.g., Payments, Radar, Capital, Billing, etc.), we have lots of exciting opportunities to innovate in ML Platform at Stripe. The ML Platform team builds the platforms and services that enable ML engineers and data scientists across Stripe to take data and build features and models from prototype to production—reliably, at low latency, and at scale. Our scope spans ML training infrastructure, model serving and deployment, feature computation and online serving, observability and monitoring, and agentic AI capabilities. We work closely with product teams, data scientists, and platform infrastructure teams to build powerful, flexible, and user-friendly systems that substantially increase ML velocity across the company. What you'll do You'll serve as a technical lead across the ML Platform space and a key contributor to the evolution of the platforms that power Stripe's ML-driven products. As a Staff Engineer, you'll make decisions with a large impact on Stripe. You'll influence our investments and strategy while making our systems more reliable, secure, and a delight to use. You'll work cross-functionally with other technical staff, dat

About Appier Appier is an AI-native Agentic AI as a Service (AaaS) company that uses artificial intelligence (AI) to power business decision-making. Founded in 2012 with a vision of democratizing AI, Appier’s mission is turning AI into ROI by making software intelligent. Appier now has 17 offices across APAC, Europe and U.S., and is listed on the Tokyo Stock Exchange (Ticker number: 4180). Visit www.appier.com for more information. The Impact You’ll Make at Appier We’re looking for a Staff/Senior Machine Learning Platform Engineer to join our Machine Learning Platform Team, which powers end-to-end infrastructure for model training, evaluation, deployment, and monitoring at scale. Our platform supports daily execution of hundreds of ML models and processes billions of data records across batch and streaming pipelines. In this role, you’ll shape the architecture and core components of our ML platform—covering batch (Spark), streaming (Flink), job orchestration (Argo on Kubernetes), and infrastructure tools—while ensuring the platform remains robust, scalable, and developer-friendly. You’ll also champion best practices and modern development tools including LLM-based programming assistants. What You’ll Work On Architect, implement, and scale batch (Spark) and streaming (Flink) pipelines that process billions of records daily for ML training and evaluation. Design and operate robust ML job execution frameworks for training, inference, and post-processing. Build and maintain internal API servers and developer tools to orchestrate ML jobs on Kubernetes (via Argo Workflows, Helm, Terraform). Design and monitor data infrastructure using ClickHouse and PostgreSQL. Ensure high availability and observability through monitoring tools like Prometheus and Grafana. Collaborate with data scientists, product managers, and engineers to deliver reliable and efficient ML platform capabilities. Actively adopt and promote the use of LLM-based tools (e.g.,

Who we are About Stripe Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world's largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone's reach while doing the most important work of your career. About the team Stripe processes over $1.9T in payments volume per year, which is roughly 1.6% of the world's GDP, for millions of customers from startups to enterprises. The tremendous amount of data makes Stripe one of the best places to do machine learning. While being an integral part of almost every product line at Stripe (e.g., Payments, Radar, Capital, Billing, etc.), we have lots of exciting opportunities to innovate in ML Platform at Stripe. The ML Platform team builds the platforms and services that enable ML engineers and data scientists across Stripe to take data and build features and models from prototype to production—reliably, at low latency, and at scale. Our scope spans ML training infrastructure, model serving and deployment, feature computation and online serving, observability and monitoring, and agentic AI capabilities. We work closely with product teams, data scientists, and platform infrastructure teams to build powerful, flexible, and user-friendly systems that substantially increase ML velocity across the company. What you'll do You'll serve as a technical lead across the ML Platform space and a key contributor to the evolution of the platforms that power Stripe's ML-driven products. As a Staff Engineer, you'll make decisions with a large impact on Stripe. You'll influence our investments and strategy while making our systems more reliable, secure, and a delight to use. You'll work cross-functionally with other technical staff, dat

Opportunity Overview: As a Staff Applied Scientist, you will play a critical technical leadership role on Cohere Health’s AS team, with a primary focus on powering and scaling machine learning capabilities across our products. You will apply state-of-the-art machine learning, large language models, and agentic architectures to complex clinical and operational intake workflows, helping automate decision-making, improve data quality, and reduce administrative burden for clinical teams. In this role, you will partner closely with clinical operations, product, and engineering teams to uncover hidden drivers, inform strategic decisions, and design and evaluate ML systems that directly impact how members and providers experience Cohere Health at the front door of care. This role blends deep hands-on technical work with strategic influence, mentorship, and ownership across multiple work streams, while contributing to broader ML initiatives. What you’ll do: Design, develop and evaluate advanced machine learning systems for retrieval, classification, prediction, and generative use cases. Apply advanced statistical and ML techniques to extract insights from large-scale structured and unstructured healthcare datasets. Lead model development across the ML lifecycle, including experimentation, training, evaluation, partnering with AI engineering to support deployment, monitoring, and iteration. Write and maintain reusable codebases to perform data preprocessing, model training, evaluation, and deployment Collaborate cross-functionally with product managers, clinicians, data engineers, BI engineers, and design teams to translate business and clinical needs into robust ML solutions. Drive experimentation by defining problem statements, forming falsifiable hypotheses, and designing rigorous evaluation frameworks tied to business outcomes. Review, communicate, and present ML insights and results to technical and non-technical stakeholders, including executive leadership. Serve as a

Opportunity Overview: As a Staff Data Scientist at Cohere Health, you will serve as a technical leader across high-priority initiatives, shaping how data science is applied to some of the most complex challenges in healthcare. You’ll drive the design of advanced analytical and modeling solutions, influence strategic direction, and partner deeply across Product, Clinical, and Engineering to deliver scalable, high-impact outcomes. This role goes beyond execution. You’ll define approaches, set standards, and guide others in solving ambiguous, high-leverage problems. You’ll play a critical role in advancing the maturity of data science at Cohere while contributing directly to improving clinical and operational decision-making. What you’ll do: Lead the design and execution of complex, high-impact data science initiatives across multiple domains Define analytical frameworks and modeling approaches for ambiguous, strategic problem spaces Partner with senior stakeholders to shape problem definition, prioritize opportunities, and influence decision-making Develop and deploy advanced models and scalable analytical solutions that drive measurable outcomes Establish best practices for experimentation, model development, and analytical rigor across the team Mentor and guide other data scientists, providing technical leadership and elevating team capabilities Drive cross-functional alignment to ensure solutions are practical, scalable, and integrated into workflows ISMS roles and responsibilities: Good knowledge of Information security Oversee specific business processes within the ISMS. Responsible to manage the ISMS documentation, conduct risk assessments, and implement risk treatment plans. Risk Owners are responsible for identifying, assessing, and managing risks within their areas of responsibility. They are also responsible for implementing risk treatment plans. Conduct the BCP and other test related to information security continuity along with CISO Responsible for monitor

Opportunity Overview: As a Lead Applied Scientist, you’ll join our growing team of world-class applied scientists and clinical experts to deploy machine learning models that help automate burdensome administrative clinical practices. Our AS team’s work is focused on designing, prototyping, and partnering with AI Engineering to deploy and monitor production models which extract or predict relevant clinical findings from structured and unstructured data sources. What you’ll do: Design and develop advanced machine learning algorithms for retrieval, classification and generative use cases. Write and maintain reusable codebases to perform data preprocessing, model training, and model evaluation Provide scientific and machine learning best practice guidance to junior engineers Work cross-functionally with diverse stakeholders, including product managers, clinicians, and technical leadership to align ML solutions with Cohere’s core objectives. What you’ll need: Must-haves Minimum 7+ years of experience in applied ML with a masters degree or 5+ years with a PhD, ideally in a senior or lead role in a large technology or healthcare company Clear understanding of model building, model maintenance and the measures that optimize models for production use Understand experimental design and can independently perform collection, measurement, and interpretation of results Expert in Python with experience in deep learning frameworks (e.g., PyTorch) Hands on experience building deep learning models (e.g., transformers) for NLP tasks MS or PhD required Nice-to-haves Experience with generative AI or Retrieval Augmented Generation (RAG) Experience working with unstructured healthcare data (e.g., clinical notes) Experience with OCR or image-based document understanding techniques Hands on experience using AWS tools such as SageMaker Studio ISMS roles and responsibilities Good knowledge of Information practices. Assist the manager in all the information security activitie

We're transforming the grocery industry At Instacart, we invite the world to share love through food because we believe everyone should have access to the food they love and more time to enjoy it together. Where others see a simple need for grocery delivery, we see exciting complexity and endless opportunity to serve the varied needs of our community. We work to deliver an essential service that customers rely on to get their groceries and household goods, while also offering safe and flexible earnings opportunities to Instacart Personal Shoppers. Instacart has become a lifeline for millions of people, and we’re building the team to help push our shopping cart forward. If you’re ready to do the best work of your life, come join our table. Instacart is a Flex First team There’s no one-size fits all approach to how we do our best work. Our employees have the flexibility to choose where they do their best work—whether it’s from home, an office, or your favorite coffee shop—while staying connected and building community through regular in-person events. Learn more about our flexible approach to where we work. Overview The Advertiser Optimization team is the decision-making engine of Instacart's $1B+ ads business. We own the systems responsible for Bidding, Pacing, Budgeting, and Targeting: converting stated advertiser goals into real-time auction actions. Our mission is to maximize realized Advertiser Value by deciding when to participate, how much to bid, and how fast to spend, all while balancing User Experience and Platform Revenue. We are hiring a Senior Applied Scientist II to lead the algorithmic direction of these systems. This is a role for someone who thinks in terms of control theory, constrained optimization, and auction economics, and who can translate those frameworks into production code that makes millions of decisions per day. You will formulate problems from first principles, shape the technical roadmap, and own systems end-to-end from mathematic

Every day, tens of millions of people come to Roblox to explore, create, play, learn, and connect with friends in 3D immersive digital experiences– all created by our global community of developers and creators. At Roblox, we’re building the tools and platform that empower our community to bring any experience that they can imagine to life. Our vision is to reimagine the way people come together, from anywhere in the world, and on any device. We’re on a mission to connect a billion people with optimism and civility, and looking for amazing talent to help us get there. A career at Roblox means you’ll be working to shape the future of human interaction, solving unique technical challenges at scale, and helping to create safer, more civil shared experiences for everyone. Why Data Science And Engineering The Data Science & Engineering organization's mission is to increase our speed, frequency, and acumen in making decisions at scale by instilling a data-influenced approach to building products. We cover a wide area of the data spectrum, including analytical data engineering, product analytics, experimentation, causal inference, statistical modeling, and machine learning. Aligned and partnering with product verticals, we use this extensive tool belt to discover new opportunities and unmet use cases, influence and shape the product roadmap and prioritization, build data products, and measure impact on our community of players and developers. In Data Science & Engineering, you will build the macroeconomic models, simulation frameworks, and policy mechanics that scale Roblox’s virtual economy. By analyzing Robux currency dynamics, global pricing strategies, and developer exchange policies, your work will directly inform strategic decision-making, financial policy, and executive roadmaps on our road to connecting one billion users. You will: Develop, build, and refine macroeconomic models to forecast and monitor Robux money supply, velocity, inflati

As a Staff Research Scientist within Datadog AI Research (DAIR), you will drive research in post-training and autonomous agents as a hands-on individual contributor. You will advance reinforcement learning, simulated environments, synthetic data generation, and evaluation methods for agents operating across complex real-world systems. You will set the technical direction for ambitious research programs, raise the technical bar for the researchers and research engineers around you, and collaborate with Datadog's product and engineering teams to translate research advances into products. What You'll Do: Drive research in agent post-training and reinforcement learning, shaping the technical direction of ambitious research programs grounded in observability and security Own research problems end to end, from framing the question through experimentation, model development, and evaluation Design and advance reinforcement learning approaches, post-training methods, and training loops for agents operating in complex environments Build simulated environments, synthetic data generation approaches, and evaluation frameworks that enable scalable agent training and rigorous measurement Raise the technical bar across the team by reviewing research directions, mentoring researchers and research engineers, and setting standards for experimental rigor Collaborate with cross-functional teams across Research, Product, and Engineering to translate advances in autonomous agents into scalable Datadog capabilities Contribute to research publications, present at top-tier conferences such as NeurIPS, ICLR, and ICML, and help open-source key model artifacts and benchmarks Who You Are: You hold a PhD in Computer Science, Machine Learning, or a related field, or have equivalent experience, with deep expertise in areas such as reinforcement learning, AI agents, post-training, or generative modeling You have driven technically ambitious research at meaningful scale as

As a Staff Research Scientist within Datadog AI Research (DAIR), you will drive research in foundation models and world models as a hands-on individual contributor. You will advance large-scale pre-training and multimodal learning across the diverse signals generated by distributed systems, including metrics, traces, logs, topology, and events. You will set the technical direction for ambitious research programs, raise the technical bar for the researchers and research engineers around you, and collaborate with Datadog's product and engineering teams to translate research advances into products. What You'll Do: Drive research in foundation models, world models, and multimodal learning, shaping the technical direction of ambitious research programs grounded in observability Own research problems end to end, from framing the question through experimentation, model development, and evaluation Train large-scale multimodal models on diverse telemetry data, including metrics, logs, traces, topology, events, and other non-text modalities Advance approaches to pre-training, representation learning, world modeling, scaling, and evaluation for models that learn the dynamics of complex distributed systems Raise the technical bar across the team by reviewing research directions, mentoring researchers and research engineers, and setting standards for experimental rigor Collaborate with cross-functional teams across Research, Product, and Engineering to translate research advances into scalable Datadog capabilities Contribute to research publications, present at top-tier conferences such as NeurIPS, ICLR, and ICML, and help open-source key model artifacts and benchmarks Who You Are: You hold a PhD in Computer Science, Machine Learning, or a related field, or have equivalent experience, with deep expertise in areas such as foundation models, world models, multimodal learning, or generative modeling You have driven technically ambitious research at meanin