Senior Ml Operations Engineer Jobs

Elastic, the Search AI Company, enables everyone to find the answers they need in real time, using all their data, at scale — unleashing the potential of businesses and people. The Elastic Search AI Platform, used by more than 50% of the Fortune 500, brings together the precision of search and the intelligence of AI to enable everyone to accelerate the results that matter. By taking advantage of all structured and unstructured data — securing and protecting private information more effectively — Elastic’s complete, cloud-based solutions for search, security, and observability help organizations deliver on the promise of AI. What is The Role We are looking for a hands-on Full-Stack Senior Software Engineer to join our IT Software Engineering team reporting directly to the Director, IT Software Engineering. Inside IT Software Engineering, we are shifting from a traditional "SaaS-only" procurement model to an agile Build and Buy strategy. Powered by the Elastic Stack—Elasticsearch, Kibana, Elastic ML, and native AI capabilities—our team builds custom, enterprise-grade applications and AI-powered bots for internal teams (IT, HR, Finance, Ops), replacing expensive third-party software contracts with faster, native solutions. This is a 100% individual contributor (IC) engineering role . You will lead the technical design, development, and deployment of full-stack internal web applications and conversational AI bots. You will write clean React and Node.js code, integrate backend services natively with Elasticsearch, and leverage modern AI development tools (Cursor, Claude, Copilot) to accelerate delivery—all while taking complete operational ownership of production systems. What You Will Be Doing Build Enterprise Applications: Write clean, maintainable code across React, TypeScript, and Node.js to design and ship full-stack applications that replace legacy SaaS tools. Develop AI Bots & Workflows: Architect and deploy intelligent internal bots using LLMs, Retrieval-Augm

The Software Systems Infrastructure team is responsible for aiding the Software Systems organization with all its internal tooling needs, development processes and ensuring that all safety-critical software meets a high safety bar for production vehicles. We are a fullstack high-paced software engineering team. In this role, you will help establish infrastructure, design and develop software tools that will automate, track or audit processes and methods needed to build safety-critical software that will revolutionize transportation in the 21st century. Borrowing principles from avionics, automotive, and other safety-critical industries, you will work with software, systems and firmware engineers across the Zoox software organization, ranging from state-of-the-art AI Perception, all the way to highly-critical RTOS and middleware components. In this role, you will: Lead the design and development of Geofencer across the stack: a React/TypeScript single-page application with rich interactive map editing (deck.gl, Mapbox), and Python backend services (FastAPI, SQLAlchemy/GeoAlchemy2, Celery) on PostgreSQL/PostGIS. Own core geospatial data workflows: versioned geofence editing, road-network annotation, drivable-area computation and connectivity validation, and the Plan of Record release process that publishes operational constraints to the fleet. Integrate with the broader Zoox ecosystem: the Zoox Road Network (ZRN) map graph, S3 artifact pipelines, downstream fleet and mission systems, and ML-assisted annotation workflows. Operate production infrastructure: B

Who We Are At Justworks, you’ll enjoy a welcoming and casual environment, great benefits, wellness program offerings, company retreats, and the ability to interact with and learn from leaders in the startup community. We work hard and care about our most prized asset - our people. We’re helping businesses get off the ground by enabling them to focus on running their business. We solve HR issues. We’re data-driven and never stop iterating. If you’d like to work in a supportive, entrepreneurial environment, are interested in building something meaningful and having fun while doing it, we’d love to hear from you. We're united by shared goals and shared motivations at Justworks. These are best summed up in our company values, which are reflected in our product and in our team. Our Values If this sounds like you, you’ll fit right in. Department Technology, Data & AI About The Team The Data & Analytics team is Justworks' data and AI center of excellence — a connected set of functions spanning business intelligence, data science, analytics engineering, platform data/ML engineering and AI builders. We've grown significantly over the last several years and continue to build momentum across the business, leveling up maturity in how we deliver insights, govern data, and enable the company to make better decisions. You'll find a highly collaborative & diverse group motivated by craft, shared learning, and a genuine commitment to growing our impact for customers while evolving how we work together for our people. Come build with us! The Data Platform Engineering team sits at the core of this foundation, owning the pipelines, platform infrastructure, and reliability that every other DNA function depends on. Who You Are You're a hands-on engineering leader with deep experience building and operating modern data platforms. You've led teams through scale — evolving architecture, improving reliability, and making hard tradeoffs between speed, cost, and quality. You're as

P - 1381 At Databricks, we are passionate about enabling Data & AI teams to solve the world's toughest problems — from making the next mode of transportation a reality to accelerating the development of medical breakthroughs. We do this by building and running the world's best data and AI infrastructure platform so our customers can use deep data insights to improve their business. Founded by engineers, we leap at every opportunity to tackle technical challenges, from designing next-gen UI/UX for data interaction to scaling our services and infrastructure across millions of virtual machines. And we're only getting started. About the Team The Backline Engineering Team serves as the critical bridge between Frontline Support and Engineering. We handle complex technical issues and escalations across the Data and AI ecosystem. With a strong focus on customer success, we are committed to delivering exceptional customer satisfaction by providing deep technical expertise, proactive issue resolution, and continuous platform improvements. We emphasise automation and tooling to enhance troubleshooting efficiency, reduce manual efforts, and improve the overall supportability of the platform and the health of our products. By developing smart solutions and streamlining workflows, we drive operational excellence and ensure a delightful experience for both customers and internal teams. As a Staff Backline Engineer, you will be a technical expert and escalation point for some of the most complex ML/AI issues. You will work across Support, Engineering, Product, and customers to troubleshoot difficult problems, reproduce issues, identify root causes, and drive them to resolution. What You'll Do Serve as a senior escalation point for complex ML/AI issues involving model training, inference, Model Serving, MLflow, Feature Engineering, with knowledge of Spark, Delta Lake, and distributed workloads. Perform deep technical investigations using logs, traces, metrics, profiling, configu

Reddit is a community of communities. It’s built on shared interests, passion, and trust, and is home to the most open and authentic conversations on the internet. Every day, Reddit users submit, vote, and comment on the topics they care most about. With 100,000+ active communities and approximately 130 million daily active unique visitors, Reddit is one of the internet’s largest sources of information. For more information, visit www.redditinc.com . We're looking for an experienced Senior Engineering Manager to lead our Media Ranking teams. In this role, you’ll lead a high-impact team of Machine Learning Engineers dedicated to improving relevance, personalization, and discovery of the core product. Reddit is investing big on making media (video and audio) as the core components of our experience around which conversations happen and users connect with the community. Your work will directly shape the personalized experiences of over 120M+ daily users. Responsibilities: Define Technical Vision & Strategy: Define the technical vision and long-term roadmap for Feed Relevance, aligning cutting-edge ML solutions with Reddit’s product and business objectives. Team Leadership & Development: Coach and support the development of your team, constantly seeking opportunities to grow their skills and impact. Cross-Functional Partnership: Work with cross-functional partners from across the company to identify key areas of opportunity, set expectations, and communicate your team’s work. Technical Execution & Delivery: Oversee the design, development, and optimization of sophisticated relevance, discovery, and content distribution systems. Platform & Infrastructure Collaboration: Collaborate with platform and infrastructure teams to chart a path to the next generation of AI-powered solutions for Reddit. Operational Excellence: Maintain high standards for system performance, reliability, and efficiency in alignment with customer needs and responsible AI princip

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. Observe by Snowflake is an AI-powered observability platform built on the Snowflake AI Data Cloud and engineered for scale. We ingest and store logs, metrics, traces, and events on an open, scalable data lakehouse, using open formats like Apache Iceberg, at dramatically lower cost. A dynamic Context Graph and chat-based AI SRE provide rich context and automated workflows so teams can move from detection to root cause of production issue and resolution 10x faster. Leading engineering teams at companies like Capital One, Topgolf, and Dialpad rely on Observe to troubleshoot hundreds of terabytes of telemetry daily while maintaining reliability at enterprise scale. As part of Snowflake, Observe combines startup-style ownership and velocity with the global reach, operational excellence, and ecosystem of one of the world’s leading data platforms. The Team On the AI Backend team at Observe by Snowflake you'll be at the forefront of how AI is reshaping the way engineering teams operate; building the platform that powers intelligent, automated workflows across observability and beyond. Our team is small, and moves fast, with real ownership over hard problems that span agentic APIs, real-time pipelines, and AI quality. You'll work alongside talented engineers across ML, product, and

The Ontology Engineer is a foundational technical hire on the AI, ML and Data Science team specializing in Knowledge Representation. This role is responsible for designing and maintaining the formal ontological architecture that makes cross-organizational data alignment. This is not a taxonomy or metadata management role. It requires genuine formal depth in description logics, upper ontology theory, and the ability to reason about what an ontology commits to and what it leaves open. Our platform sits between hospitals, distributors, GPOs, manufacturers, and regulators, enabling transactional execution, clinical data alignment, and analytics optimization across organizational boundaries. Each party maintains its own implicit ontology encoded in its schemas, workflows, and data. The Ontology Engineer will define the formal structures and processes that make alignment across them possible. These structures should be auditable, compositionally sound, and maintainable over a multi-year lifecycle as all parties' systems evolve. This Engineer will work directly with teammates that are familiar with ontology formalisms and with domain experts who understand the operational realities of HCSC data. They will be expected to make and defend design decisions at the level of formal correctness, not just practical convenience, and to direct and evaluate LLM-assisted ontology discovery and enrichment pipelines with the rigor that formal alignment demands. Essential Duties: Design and maintain the ontology, covering the canonical structural layer (organizations, items, contracts, transaction), source data ontologies (supporting the canonical) and the process layer (data curation, ontology matching, workflows). Establish the rules for when two records from different systems refer to the same thing, and when they don't — recognizing the answer can differ by use case. Establish mappings from trading partner source data to the canonical ontology, with documented proven

We are looking for an enthusiastic software engineer to join our AI networking acceleration team, to work on a groundbreaking open-source library, using hardware offloads, GPU Kernels and RDMA network cards. Our product is a performance-oriented low-level infrastructure, crafted to change the way inference works. We thrive as a team in a deeply strong environment, and we're passionate about innovation. The rewards are sweet and include working with some of the brightest people in the industry, an aggressive compensation plan that rewards top performers, and the opportunity to collaborate on products that transform daily the way people work and play. What you'll be doing: Developing a highly optimized inference framework Running on the world’s largest supercomputers and data centers. The work environment is dynamic and challenging as our employees work on innovative, next-generation products at the forefront of technology in terms of performance, scalability, and features. What we need to see: B.Sc. or equivalent experience in Computer Science or Software Engineering 6+ years of experience in modern C++ / C / Rust development 3 years of experience in Linux environment and familiarity with development tools Deep knowledge of the TCP/IP network stack Understanding of computer architecture and operating systems concepts Ways to stand out from the crowd: Background in Linux internals and low-level software optimizations (benchmarking, bottleneck research, performance tuning) Experience in programming CUDA kernels is an advantage Familiarity with ML frameworks and LLMs Background in parallel programming / high-performance computing / RDMA t

About BlockTech BlockTech is an algorithmic trading firm operating at the frontier of global crypto derivatives and spot markets. We trade 24/7 across some of the fastest-moving, most data-rich venues in finance. Crypto remains one of the few markets where a researcher can still meaningfully move the edge: abundant data, novel microstructure, and the shortest possible loop between a research idea and live PnL. We're looking for an experienced Quantitative Researcher to take ownership of that edge and push it further. The role This is a senior, hands-on research seat on our trading floor. You'll own a research agenda end-to-end from hypothesis, dataset construction, feature engineering, model training, backtesting, live deployment, monitoring, and iteration. You'll be trusted to set its direction. You'll work shoulder-to-shoulder with fellow researchers, traders and analysts, shape how we price and trade, and help raise the bar for research across the floor, including mentoring less experienced researchers and influencing the tools and standards the team relies on. You will: Own price-prediction, signal, execution, and anomaly-detection models across crypto derivatives and spot markets from idea to live PnL using state-of-the-art ML Shape our research, backtesting, and trading infrastructure together with engineers, so good ideas reach production quickly and safely Own models in production: monitor live performance, diagnose decay, and iterate on what you ship Set research direction alongside traders, deciding which trades are worth making and why Raise the research bar by mentoring colleagues, reviewing work, and setting standards for rigour What we're looking for 4+ years of hands-on quantitative research and/or applied ML experience, with a track record of models you've taken into production trading live A strong academic foundation in a quantitative discipline (mathematics, physics, statistics, computer science, ML/AI, or similar) Fluency in Python and the modern

About BlockTech BlockTech is an algorithmic trading firm operating at the frontier of global crypto derivatives and spot markets. We trade 24/7 across some of the fastest-moving, most data-rich venues in finance. Crypto remains one of the few markets where a researcher can still meaningfully move the edge: abundant data, novel microstructure, and the shortest possible loop between a research idea and live PnL. We're looking for an experienced Quantitative Researcher to take ownership of that edge and push it further. The role This is a senior, hands-on research seat on our trading floor. You'll own a research agenda end-to-end from hypothesis, dataset construction, feature engineering, model training, backtesting, live deployment, monitoring, and iteration. You'll be trusted to set its direction. You'll work shoulder-to-shoulder with fellow researchers, traders and analysts, shape how we price and trade, and help raise the bar for research across the floor, including mentoring less experienced researchers and influencing the tools and standards the team relies on. What you'll do Own price-prediction, signal, execution, and anomaly-detection models across crypto derivatives and spot markets from idea to live PnL using state-of-the-art ML Shape our research, backtesting, and trading infrastructure together with engineers, so good ideas reach production quickly and safely Own models in production: monitor live performance, diagnose decay, and iterate on what you ship Set research direction alongside traders, deciding which trades are worth making and why Raise the research bar by mentoring colleagues, reviewing work, and setting standards for rigour What we're looking for 4+ years of hands-on quantitative research and/or applied ML experience, with a track record of models you've taken into production trading live A strong academic foundation in a quantitative discipline (mathematics, physics, statistics, computer science, ML/AI, or similar) Fluency in Python and the m

Machine Learning Engineer – (Computer Vision) We’re looking for a Staff/Senior Machine Learning Engineer with deep expertise in computer vision and biometrics to lead the design and scaling of face recognition systems in production. You’ll build and train models, and own ML systems end-to-end on AWS. The final job level for this role will be determined following the interview process. What You’ll Do Lead the design and development of computer vision systems for biometrics (face attributes, detection, quality, and recognition) Rigorous fairness analysis and benchmarking of biometric models across various datasets and operating conditions. Architect, train, and optimize models using PyTorch, Tensorflow, and/or JAX Own and evolve end-to-end ML pipelines, from data ingestion to deployment. Design automated pipelines (Airflow) for data ingestion and cleaning. You will be responsible for curating balanced training sets and generating synthetic data to address both quality and diversity gaps. Production Engineering: Own the path to production. Optimize models for low-latency inference (quantization, distillation, TensorRT/ONNX) and manage deployment on AWS. Mentor ML engineers, conduct code/design reviews, and drive technical best practices across the Computer Vision team. What We’re Looking For Experience: 5+ years of industry experience in Machine Learning, with at least 3 years dedicated to Biometrics or Face Analysis. Deep expertise in computer vision and biometrics, especially face recognition. Fairness & Ethics: You understand the sources of algorithmic bias in Computer Vision and have practical experience measuring and mitigating disparate impact. Strong Engineering: Expert proficiency in Python (both machine learning and vision libraries such as Pillow, OpenCV, PyTorch, etc). You write clean, modular, production-ready code. Systems Architecture: Experience designing end-to-end ML pipelines (Data to Train to Deploy) and working with workflow orchestrators like A

About BlockTech BlockTech is an algorithmic trading firm operating at the frontier of global crypto derivatives and spot markets. We trade 24/7 across some of the fastest-moving, most data-rich venues in finance. Crypto remains one of the few markets where a researcher can still meaningfully move the edge: abundant data, novel microstructure, and the shortest possible loop between a research idea and live PnL. We're looking for an experienced Quantitative Researcher to take ownership of that edge and push it further. The role This is a senior, hands-on research seat on our trading floor. You'll own a research agenda end-to-end from hypothesis, dataset construction, feature engineering, model training, backtesting, live deployment, monitoring, and iteration. You'll be trusted to set its direction. You'll work shoulder-to-shoulder with fellow researchers, traders and analysts, shape how we price and trade, and help raise the bar for research across the floor, including mentoring less experienced researchers and influencing the tools and standards the team relies on. You will: Own price-prediction, signal, execution, and anomaly-detection models across crypto derivatives and spot markets from idea to live PnL Shape our research, backtesting, and trading infrastructure together with engineers, so good ideas reach production quickly and safely Own models in production: monitor live performance, diagnose decay, and iterate on what you ship Set research direction alongside traders, deciding which trades are worth making and why Raise the research bar by mentoring colleagues, reviewing work, and setting standards for rigour What we're looking for 4+ years of hands-on quantitative research and/or applied ML experience, with a track record of models you've taken into production trading live A strong academic foundation in a quantitative discipline (mathematics, physics, statistics, computer science, ML/AI, or similar) Fluency in Python and the modern ML stack (PyTorch and/or

About BlockTech BlockTech is an algorithmic trading firm operating at the frontier of global crypto derivatives and spot markets. We trade 24/7 across some of the fastest-moving, most data-rich venues in finance. Crypto remains one of the few markets where a researcher can still meaningfully move the edge: abundant data, novel microstructure, and the shortest possible loop between a research idea and live PnL. We're looking for an experienced Quantitative Researcher to take ownership of that edge and push it further. The role This is a senior, hands-on research seat on our trading floor. You'll own a research agenda end-to-end from hypothesis, dataset construction, feature engineering, model training, backtesting, live deployment, monitoring, and iteration. You'll be trusted to set its direction. You'll work shoulder-to-shoulder with fellow researchers, traders and analysts, shape how we price and trade, and help raise the bar for research across the floor, including mentoring less experienced researchers and influencing the tools and standards the team relies on. What you'll do Own price-prediction, signal, execution, and anomaly-detection models across crypto derivatives and spot markets from idea to live PnL using state-of-the-art ML Shape our research, backtesting, and trading infrastructure together with engineers, so good ideas reach production quickly and safely Own models in production: monitor live performance, diagnose decay, and iterate on what you ship Set research direction alongside traders, deciding which trades are worth making and why Raise the research bar by mentoring colleagues, reviewing work, and setting standards for rigour What we're looking for 4+ years of hands-on quantitative research and/or applied ML experience, with a track record of models you've taken into production trading live A strong academic foundation in a quantitative discipline (mathematics, physics, statistics, computer science, ML/AI, or similar) Fluency in Python and the m

Role Purpose We’re looking for a Staff/Senior Machine Learning Engineer with deep expertise in computer vision and biometrics to lead the design and scaling of face recognition systems in production. You’ll build and train models, and own ML systems end-to-end on AWS. The final job level for this role will be determined following the interview process. What You’ll Do Lead the design and development of computer vision systems for biometrics (face attributes, detection, quality, and recognition) Rigorous fairness analysis and benchmarking of biometric models across various datasets and operating conditions. Architect, train, and optimize models using PyTorch, Tensorflow, and/or JAX Own and evolve end-to-end ML pipelines, from data ingestion to deployment. Design automated pipelines (Airflow) for data ingestion and cleaning. You will be responsible for curating balanced training sets and generating synthetic data to address both quality and diversity gaps. Production Engineering: Own the path to production. Optimize models for low-latency inference (quantization, distillation, TensorRT/ONNX) and manage deployment on AWS. Mentor ML engineers, conduct code/design reviews, and drive technical best practices across the Computer Vision team. What We’re Looking For Experience: 5+ years of industry experience in Machine Learning, with at least 3 years dedicated to Biometrics or Face Analysis. Deep expertise in computer vision and biometrics, especially face recognition. Fairness & Ethics: You understand the sources of algorithmic bias in Computer Vision and have practical experience measuring and mitigating disparate impact. Strong Engineering: Expert proficiency in Python (both machine learning and vision libraries such as Pillow, OpenCV, PyTorch, etc). You write clean, modular, production-ready code. Systems Architecture: Experience designing end-to-end ML pipelines (Data to Train to Deploy) and working with workflow orchestrators like Airflow. Cloud Native: Hands-on ex

Machine Learning Engineer IV – (Computer Vision) We’re looking for a Staff/Senior Machine Learning Engineer with deep expertise in computer vision and biometrics to lead the design and scaling of face recognition systems in production. You’ll build and train models, and own ML systems end-to-end on AWS. The final job level for this role will be determined following the interview process. What You’ll Do Lead the design and development of computer vision systems for biometrics (face attributes, detection, quality, and recognition) Rigorous fairness analysis and benchmarking of biometric models across various datasets and operating conditions. Architect, train, and optimize models using PyTorch, Tensorflow, and/or JAX Own and evolve end-to-end ML pipelines, from data ingestion to deployment. Design automated pipelines (Airflow) for data ingestion and cleaning. You will be responsible for curating balanced training sets and generating synthetic data to address both quality and diversity gaps. Production Engineering: Own the path to production. Optimize models for low-latency inference (quantization, distillation, TensorRT/ONNX) and manage deployment on AWS. Mentor ML engineers, conduct code/design reviews, and drive technical best practices across the Computer Vision team. What We’re Looking For Strong industry experience in Machine Learning, dedicated to Biometrics or Face Analysis. Deep expertise in computer vision and biometrics, especially face recognition. Fairness & Ethics: You understand the sources of algorithmic bias in Computer Vision and have practical experience measuring and mitigating disparate impact. Strong Engineering: Expert proficiency in Python (both machine learning and vision libraries such as Pillow, OpenCV, PyTorch, etc). You write clean, modular, production-ready code. Systems Architecture: Experience designing end-to-end ML pipelines (Data to Train to Deploy) and working with workflow orchestrators like Airflow. Cloud Native: Hands-on exper