Ml Platform Engineer Jobs

Sigmoid Analytics is a leading Data solutions company backed by Sequoia Capital. We offer best in- end-to-end data value chain spanning across Data Science, Data Engineering and Data Ops. With data and technology at the core of our solutions, we are solving some of the toughest problems out there. Our culture is modelled around expertise and mutual respect with a team first mindset. You’ll work with teams that push the boundaries of what-is-possible and build solutions that energize and inspire. Offices : New York | Dallas | San Francisco | Lima | Bengaluru The below role is for our Bengaluru office. About the Role : You will work on a broad range of cutting-edge data science and machine learning problems across a variety of industries. You will be engaging with clients to understand their business context. If you are passionate to work on complex unstructured business problems that can be solved using data science and machine learning, we would like to talk to you. Role: ALDS Mandate Skills & Competencies: Experience in Programming (Python, R, SQL, NoSQL, Spark) with ML tools & Cloud Technology (AWS, Azure, GCP) Experience in Python libraries such as NumPy, pandas, scikit-learn, tensor-flow, scapy, scrapy, BERT etc. Good understanding in statistics, and ability to design statistical hypothesis testing to aid formal decision making. Develops predictive models using Machine Learning algorithms (SVM, Random Forest, Neural Network, Decision Tree, Logistic Regression, K-mean Clustering, linear regression, PCA etc.) Engaging with clients, understanding complex problem statements, and offering solutions in the domains of Retail, Pharma, Banking, Insurance, etc. Contribute to internal product development initiatives related to data science. Develop data science roadmap, and guide data scientist to meet their deliverables. Handling end-to-end client AI & analytics programs. Your role will be a combination of hands-on contribution, technical team management, and c

Who are we? Cohere is the leading security-first enterprise AI company. We build cutting-edge foundation AI models and end-to-end products that are designed to solve real-world business problems. We’re training and deploying frontier models for enterprises who are building AI systems. We believe that our work is instrumental to the widespread adoption of AI and we are looking for folks that want to be part of that. We obsess over what we build. Each one of us is responsible for contributing to increasing the capabilities of our models and the value they drive for our customers. Cohere is a team of researchers, engineers, designers, and more, who are all passionate about their craft. We are a global technology company headquartered in Toronto with key offices in London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul. Join us! Why this role? This role is for people who love building tools for their coworkers. The Internal Applications team creates tools that help us create better models. In this role you will collaborate with internal stakeholders, which include annotators, ML researchers, product managers and more. Join our team of builders who create tooling that will pave the way for the next generation of large language models! As a Full-Stack Software Engineer on the Internal Applications team, you will: Work with a small talented and enthusiastic team of software engineers Contribute to delightful experiences for our user-facing products, meticulously crafting code for browsers and servers Collaborate and grow with your engineering colleagues of all levels through direct pairing sessions, architectural designs, documentation and talks Identify and remove roadblocks to enable your team to increase its engineering velocity. Build resilient systems that are mission-critical Keep up with the cutting edge and adopt new technologies to improve performance and reliability You may be a good fit if: You have experience shipping products with a large numb

Airbnb was born in 2007 when two hosts welcomed three guests to their San Francisco home, and has since grown to over 5 million hosts who have welcomed over 2 billion guest arrivals in almost every country across the globe. Every day, hosts offer unique stays and experiences that make it possible for guests to connect with communities in a more authentic way. The Community You Will Join: The Total Rewards Compensation team serves as a strategic advisor to business leaders, managers, and employees across Airbnb. We are compensation experts with a deep understanding of our stakeholders, problem solvers who use data and insights to drive value, and partners who build the tools, models, and frameworks that support sound compensation decision-making across the company. This role sits within the Compensation function and will work closely with the Technology organization and cross-functional partners including Talent Directors, Recruiting, People Analytics, Finance, and Legal. The Difference You Will Make: We are looking for a Senior Compensation Partner to own Airbnb's compensation strategy for the Technology organization, covering functions such as Engineering, Infrastructure, ML/AI and Data Science. You will be the primary Total Rewards advisor to Technology leadership and the compensation point of contact for Talent Directors and Recruiting, defining how Airbnb competes for AI, ML, and frontier engineering talent. Working alongside our regional Total Rewards Partners, you will hold a global view of how we pay technical talent across all markets. Beyond your client group, you will contribute to the design and strategy of complex global programs, lead core Total Rewards priorities, and build the models and tools the broader Compensation team relies on. You are equally credible advising our most senior technical leaders and in the data behind the recommendation. A Typical Day: Serve as the senior compensation advisor to Technology leadership across func

We believe that the way people interact with their finances will drastically improve in the next few years. We’re dedicated to empowering this transformation by building the tools and experiences that thousands of developers use to create their own products. Plaid powers the tools millions of people rely on to live a healthier financial life. We work with thousands of companies like Venmo, SoFi, several of the Fortune 500, and many of the largest banks to make it easy for people to connect their financial accounts to the apps and services they want to use. Plaid’s network covers 12,000 financial institutions across the US, Canada, UK and Europe. Founded in 2013, the company is headquartered in San Francisco with offices in New York, Seattle, Washington D.C., Raleigh, London, and Amsterdam. Fraud Data is the data science and machine learning team within Plaid’s Fraud organization, responsible for using data and ML to improve and scale Plaid’s fraud products. Within Fraud Data, the Customer & Product Intelligence team focuses on understanding product performance, uncovering customer insights, and enabling go-to-market teams with data-driven solutions. The team partners closely with customers and GTM teams on fraud analyses and proofs of concept, turning customer learnings into scalable, reusable product capabilities. We also build the metrics, analytics, and data foundations that measure product health, identify opportunities for improvement, and guide product decisions across Plaid’s Fraud portfolio. As a Data Science Manager, you will lead a team responsible for customer-facing data science and Fraud product analytics. You will set the team's roadmap, develop its data scientists, and remain involved in analytical methods, technical reviews, and customer investigations. You will: Set a 6–12-month roadmap with Product, Engineering, and GTM, and assign priorities and responsibilities across the team. Define product metrics, their underlying data, and reporting and

We believe that the way people interact with their finances will drastically improve in the next few years. We’re dedicated to empowering this transformation by building the tools and experiences that thousands of developers use to create their own products. Plaid powers the tools millions of people rely on to live a healthier financial life. We work with thousands of companies like Venmo, SoFi, several of the Fortune 500, and many of the largest banks to make it easy for people to connect their financial accounts to the apps and services they want to use. Plaid’s network covers 12,000 financial institutions across the US, Canada, UK and Europe. Founded in 2013, the company is headquartered in San Francisco with offices in New York, Seattle, Washington D.C., Raleigh, London, and Amsterdam. About the Team The Embedded Insights team supports Plaid’s mission to build a world-class suite of intelligence products. We identify the best opportunities to use machine learning in Plaid products, prove out those opportunities, and collaborate with cross-functional partners to turn them into real-world production systems. About the Role As a Senior Machine Learning Engineer on Embedded Insights, you will help shape Plaid’s future by building machine learning-powered products and features. You will initially support the Plaid App, working on a 0-to-1 consumer-facing product and helping establish product-market fit for a new business line. You will work closely with product managers, data scientists, engineers, customers, and other machine learning engineers to translate ambiguous opportunities into effective ML systems that create measurable customer value. In supporting the Plaid App, you will: Build machine learning-based features for a 0-to-1 consumer-facing product. Partner with product managers to translate ambiguous business requirements into machine learning problems and influence product strategy and roadmap decisions. Rapidly iterate and experiment to help drive product

About the Team The Agent Safety team works to ensure that increasingly capable AI agents act safely, exercise sound judgment, and remain aligned with user intent. Our mission is to reduce the probability of severe unintended outcomes from increasingly capable AI agents while preserving their ability to act effectively and autonomously. Our work spans three areas: Training: Create training methods, environments and data that teach agents to make better decisions in consequential situations. We turn real-world failures into training signals that prevent similar incidents, and identify precursor behaviors and mitigations to address emerging risks. Measurements: Build evaluations and production metrics that identify emerging risks and measure whether our interventions work. Oversight: Develop oversight and system mitigation mechanisms that reduce harmful actions while preserving useful autonomy (for example future versions of auto-review ). About the Role We’re looking for strong executors with excellent judgment, comfort with ambiguity, and an understanding of frontier model research. You don’t need prior safety or alignment experience, we also welcome people that recently realized that alignment and safety is a critical area to contribute to. This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees. In this role, you will: Train and evaluate frontier models to reduce harmful or misaligned agent actions, forming clear hypotheses and executing independently through ambiguity. Mine incidents and build scalable measurement, data-processing, and evaluation systems that turn real failures into repeatable safety signals. Collaborate closely with post-training, capabilities, oversight, and pre-training partners to ship research-backed mitigations into large-scale training and agent systems. You might thrive in this role if you: Have demonstrated strength in research engineering, ML en

We're hiring an AI Support Engineer to work directly with the founder and build the systems that power customer support at Bolna. This isn't a traditional support role — you'll use AI to make support scale, and you'll partner closely with the business team on the customer conversations that matter most. What you'll do - Work directly with the founder to design and continuously improve how customer support runs at Bolna - Pull and collate data from Intercom to spot patterns, recurring issues, and gaps in how customers are being helped - Build AI-powered workflows that triage, answer, and resolve customer support queries with less manual effort - Design the systems and processes behind a streamlined, scalable support flow — from triage to escalation to resolution - Step in directly on critical customer support situations alongside the business team when it matters - Turn recurring support themes into feedback for product and engineering What we're looking for - 1–3 years of experience in a support, ops, or technical customer-facing role — ideally somewhere that rewarded building your own tools and process, not just following a playbook - Hands-on comfort with AI tools/workflows (prompting, automations, agent builders) — you don't need to be an ML engineer, but you should be someone who reaches for AI to solve a workflow problem - Experience with Intercom or a similar support/helpdesk tool - Sharp, structured communicator — equally comfortable writing to customers and to the founder - Comfortable with ambiguity — this role is being built as you build it Nice to have - Experience setting up support automations, chatbots, or AI agents in a real product company - Familiarity with SQL or basic scripting to pull/analyze support data - Startup experience, especially in a 0-to-1 function

NVIDIA has continuously reinvented itself over two decades. Our invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing. NVIDIA is a “learning machine” that constantly evolves by adapting to new opportunities that are hard to solve, that only we can address, and that matter to the world. This is our life’s work, to amplify human creativity and intelligence. Make the choice to join us today. Design-for-X Engineering at NVIDIA works on groundbreaking innovations involving crafting creative solutions in AI for Chip Design and AI for Predictions in various use cases in manufacturing testing on some of the industry's most complex semiconductor chips. What you'll be doing: As a senior member in our team, you will work on innovating in the DFT Power, Thermal & Voltage Noise Methodology areas. This will include working on groundbreaking low power & thermal solutions for our manufacturing tests to be enabled at conditions that push the boundaries for our datacenter GPUs. You will work with multi-functional teams including Product Development & Power Architecture, implementing brand-new methodologies on hard-to-solve problems for improving our outgoing quality of chips. You will work on post-silicon data analysis for power to architect the next-gen solutions. In addition, you will help develop and deploy DFT methodologies for our next generation products using Applied ML & Gen AI solutions. You will also help mentor junior engineers on test designs and trade-offs including cost and quality. What we need to see: BSEE (or equ

About the Role Adobe is seeking a Machine Learning Engineer to join the Adobe Genuine Engineering team. This group protects Adobe's ecosystem from fraud, abuse, and misuse using intelligent systems worldwide. In this position, you will build and develop machine learning models from scratch, including custom transformer-based frameworks, to identify fraudulent actions, stop account sharing, and protect the experience of hundreds of millions of users. You will manage the entire model lifecycle: raw behavioral data and feature engineering, architecture development, large-scale GPU training, deployment, and monitoring. The team is actively building in-house behavioral foundation models that learn identity-preserving representations from long sequences of user activity. This is a role for an engineer who wants to own deep learning systems end-to-end — not consume pre-built ones. Key Responsibilities Build and train deep learning models from scratch, including custom transformer and attention-based architectures for long behavioral event sequences. Own the full training stack: event tokenization, temporal and positional embeddings, self-supervised pretraining (e.g., masked modeling, contrastive learning), and downstream fine-tuning. Train large models efficiently on GPU infrastructure using mixed-precision training, gradient accumulation/checkpointing, efficient attention, and distributed strategies (DDP, FSDP, or equivalent). Build and optimize feature pipelines on Databricks and Spark, transforming raw behavioral events into high-quality model inputs. Translate prototypes into production ML systems — scalable, reliable, and observable — and drive inference performance through architectural and serving-side optimization. Contribute to MLOps practices: experiment tracking, model versioning, CI/CD, automated retraining, and

About the Role Adobe is seeking a Machine Learning Engineer to join the Adobe Genuine Engineering team. This group protects Adobe's ecosystem from fraud, abuse, and misuse using intelligent systems worldwide. In this position, you will build and develop machine learning models from scratch, including custom transformer-based frameworks, to identify fraudulent actions, stop account sharing, and protect the experience of hundreds of millions of users. You will manage the entire model lifecycle: raw behavioral data and feature engineering, architecture development, large-scale GPU training, deployment, and monitoring. The team is actively building in-house behavioral foundation models that learn identity-preserving representations from long sequences of user activity. This is a role for an engineer who wants to own deep learning systems end-to-end — not consume pre-built ones. Key Responsibilities Build and train deep learning models from scratch, including custom transformer and attention-based architectures for long behavioral event sequences. Own the full training stack: event tokenization, temporal and positional embeddings, self-supervised pretraining (e.g., masked modeling, contrastive learning), and downstream fine-tuning. Train large models efficiently on GPU infrastructure using mixed-precision training, gradient accumulation/checkpointing, efficient attention, and distributed strategies (DDP, FSDP, or equivalent). Build and optimize feature pipelines on Databricks and Spark, transforming raw behavioral events into high-quality model inputs. Translate prototypes into production ML systems — scalable, reliable, and observable — and drive inference performance through architectural and serving-side optimization. Contribute to MLOps practices: experiment tracking, model versioning, CI/CD, automated retraining, and

About the Role Adobe is seeking a Machine Learning Engineer to join the Adobe Genuine Engineering team. This group protects Adobe's ecosystem from fraud, abuse, and misuse using intelligent systems worldwide. In this position, you will build and develop machine learning models from scratch, including custom transformer-based frameworks, to identify fraudulent actions, stop account sharing, and protect the experience of hundreds of millions of users. You will manage the entire model lifecycle: raw behavioral data and feature engineering, architecture development, large-scale GPU training, deployment, and monitoring. The team is actively building in-house behavioral foundation models that learn identity-preserving representations from long sequences of user activity. This is a role for an engineer who wants to own deep learning systems end-to-end — not consume pre-built ones. Key Responsibilities Build and train deep learning models from scratch, including custom transformer and attention-based architectures for long behavioral event sequences. Own the full training stack: event tokenization, temporal and positional embeddings, self-supervised pretraining (e.g., masked modeling, contrastive learning), and downstream fine-tuning. Train large models efficiently on GPU infrastructure using mixed-precision training, gradient accumulation/checkpointing, efficient attention, and distributed strategies (DDP, FSDP, or equivalent). Build and optimize feature pipelines on Databricks and Spark, transforming raw behavioral events into high-quality model inputs. Translate prototypes into production ML systems — scalable, reliable, and observable — and drive inference performance through architectural and serving-side optimization. Contribute to MLOps practices: experiment tracking, model versioning, CI/CD, automated retraining, and

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 Instacart Catalog team is the product data infrastructure powering everything customers see, find, and trust across the marketplace. We ingest data from retailers and third-party sources and use AI/ML and human operations to validate, enrich, and publish product information at scale. At the heart of this infrastructure are product attributes — the structured descriptors that power search filters, item card badges, dietary safety signals, health personalization, brand advertising, and agentic commerce. They touch every major surface and every major stakeholder. We're looking for a Senior Product Manager to own the product data layer that determines how customers find what they need, how brands show up on shelf, and how Instacart earns trust as the definitive source of grocery product data. In this role, you'll set the end-to-end strategy for catalog att

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 Instacart Catalog team is the product data infrastructure powering everything customers see, find, and trust across the marketplace. We ingest data from retailers and third-party sources and use AI/ML and human operations to validate, enrich, and publish product information at scale. At the heart of this infrastructure are product attributes — the structured descriptors that power search filters, item card badges, dietary safety signals, health personalization, brand advertising, and agentic commerce. They touch every major surface and every major stakeholder. We're looking for a Senior Product Manager to own the product data layer that determines how customers find what they need, how brands show up on shelf, and how Instacart earns trust as the definitive source of grocery product data. In this role, you'll set the end-to-end strategy for catalog att

At Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are energized by the opportunity to reinvent how they work. You don’t just use tools; you possess an innate curiosity, treating AI as a high-trust collaborator that is core to how you solve problems and accelerate your impact. We look for low-ego individuals who thrive in dynamic and fast-moving environments and move with an experimental mindset — who rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results. At Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done. Are you passionate about helping organizations unlock the power of AI through modern data architecture? Snowflake is looking for a customer-facing Solution Engineer who combines strong technical depth with executive presence and a passion for innovation. In this role, you’ll partner with Sales to guide customers from raw data to real AI impact — architecting scalable solutions, delivering compelling demos, and influencing complex buying decisions across executive and technical audiences. Location: Candidate MUST be located in or near Atlanta. What You’ll Do Lead customer conversations on AI strategy and data modernization. Design and demonstrate scalable data and AI solutions using SQL and Python. Support enterprise Proof of Concepts from concept to value realization. Translate complex technical concepts into measurable business outcomes. Navigate objections and competitive dynamics in high-stakes sales cycles. Leverage AI tools to personalize demos, accelerate preparation, and enhance delivery. What You Bring Strong SQL proficiency and experience with modern data warehousing. Experience using Python for analytics or ML workflows. Familiarity with AI/ML concepts, including Generative AI and LLMs. Exceptional communication skills across technical and executive audiences. Abi

Who are we? Cohere is the leading security-first enterprise AI company. We build cutting-edge foundation AI models and end-to-end products that are designed to solve real-world business problems. We’re training and deploying frontier models for enterprises who are building AI systems. We believe that our work is instrumental to the widespread adoption of AI and we are looking for folks that want to be part of that. We obsess over what we build. Each one of us is responsible for contributing to increasing the capabilities of our models and the value they drive for our customers. Cohere is a team of researchers, engineers, designers, and more, who are all passionate about their craft. We are a global technology company headquartered in Toronto with key offices in London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul. Join us! Why this role? As a Machine Learning Engineer on our Applied ML team, you will work directly with customers to quickly understand their greatest problems and design and implement solutions using Large Language Models. You’ll apply your problem-solving ability, creativity, and technical skills to close the last-mile gap in Enterprise AI adoption. You’ll be able to deliver products like early startup CTOs/CEOs do and disrupt some of the most important industries and institutions globally! As a Machine Learning Engineer (Applied ML), you will: Plan and execute large-group projects that carry through from ideation to production. Bring cross-functional alignment across engineering, product and other disciplines. Mentor a distributed team of engineers in subject matter expertise. Identify opportunities and gaps in existing models and strategize what to work on. Work closely with product teams to develop solutions. Engage in collaborations with our partner organizations. Assist our legal teams with preparation of patents on developed IP. Join us at a pivotal moment, shape what we build and wear multiple hats! You may be a good fit if y