Performance Modeling Engineer Jobs

About Sentry Software runs the world and the pace is faster than ever. Sentry helps developers fix errors and performance issues before users notice, so teams can spend less time firefighting and more time building. Trusted by 200,000+ organizations, Sentry is today’s application monitoring standard and our team is building its AI-native future. About The Role The Security Team is responsible for securing all things Sentry: our customers, our code, and everything in between. We are a small but growing team with broad scope, high trust, and the autonomy to tackle hard security problems with creativity and an engineering mindset. We work at a company with a strong developer culture, building a product that millions of developers genuinely love and rely on. That context shapes everything about how we operate. As a Security Engineer on this team, you'll work across application and platform security domains. You'll contribute to the practices that keep Sentry secure as we grow: security reviews, threat modeling, vulnerability management, and embedding secure coding practices into an engineering organization that cares about doing things right. You'll partner closely with product and engineering teams to influence how features are designed and built from the start. You will work as a technical collaborator who helps make the secure path the obvious one. As Sentry expands our agentic product capabilities and development practices, you'll also find yourself at the frontier of a new set of security challenges. In this role, you will Support and help mature Sentry's security review program. From secure code review, to architecture review, and threat modeling. You'll help build the processes, tooling, and culture which make security a natural part of how we ship and operate. Contribute to mature vulnerability management practices. Intake, triage, prioritization, remediation tracking, and support of our bug bounty and responsible disclosure program. Advocate for secure-by-desig

Interactive Design Engineer - HPIQ Description - About The Role As a Product Design Engineer at HP IQ, you’ll work at the intersection of creativity, engineering, and design to develop groundbreaking devices that redefine how people interact with technology. You’ll collaborate closely with industrial designers, hardware engineers, and interaction designers to transform ideas into functional prototypes and production-ready designs. From 3D CAD modeling and hands-on prototyping to engineering analysis and design validation, you’ll help solve complex mechanical challenges and drive designs from early concepts toward production. You’ll have the opportunity to take ownership of meaningful engineering work while learning from an experienced, multidisciplinary team and contributing to products that push the boundaries of what’s possible. What You Might Do Design mechanical parts, components, and assemblies for new consumer devices using 3D CAD (NX) Create detailed 2D engineering drawings, define specifications and tolerances, and work closely with overseas vendor partners to ensure accuracy and quality Design and run experiments, applying engineering analysis and test results to guide design decisions Evaluate materials, manufacturing processes, and design tradeoffs to develop robust and scalable solutions Work cross-functionally with hardware, software, industrial design, and other engineering teams to bring concepts from ideation through development Conceptualize, design, and build prototypes that quickly validate ideas Participate in design reviews, clearly communicating design decisions, technical analysis, and tradeoffs to both technical and non-technical audiences Build, test, troubleshoot, and iterate prototypes to identify issues and improve product performance, reliability, a

What you’ll do Act as the technical lead for large parts of the scanner platform: system architecture, codebase structure, and long-term maintainability. Own core runtime foundations: distributed control, state management, fault handling, and reliability. Drive engineering rigor: testability, code quality, review standards, performance regression prevention, and release processes. Build robust observability: logs, metrics, traces, and replayable diagnostics (with privacy constraints). Collaborate with hardware and recon/ML teams to define interfaces, data contracts, timing/synchronization, and failure modes. Lead complex refactors (e.g., message passing / RPC boundaries, modularization, concurrency model) without halting forward progress. What we’re looking for Deep software architecture experience for real-world systems: robotics, instrumentation, medical devices, or other complex distributed products. Strong Python and concurrency background (asyncio, multiprocessing, profiling, performance engineering). Track record of shipping systems that are observable, debuggable, and resilient. Strong technical leadership: clarity, pragmatic trade-offs, and mentoring. Useful experience Building but rock-solid systems: clear interfaces (gRPC/protobuf or equivalent), strong state modeling, and failure handling. High-leverage engineering habits on a lean team: good tests, CI, reproducible dev environments, and fast code review. Practical performance + concurrency work in Python (asyncio, profiling, multiprocessing) and comfort debugging distributed behavior. Security-minded device software: safe defaults, encrypted data paths, and disciplined handling of PII/PHI. Operational thinking: remote updates/management, excellent logging, and diagnostics that make real hardware debuggable.

We're looking for a Senior Engineer with a strong background in computer science fundamentals, systems design, experience in the Java ecosystem, streaming systems, and data-intensive applications to join our engineering team. In this role, you will be instrumental in designing, building, and optimizing the underlying data structures, algorithms, and database interactions that power our generative AI platform, code generation and migration tools. This involves crafting sophisticated orchestration layers, robust integration points, and high-performance data systems that seamlessly connect and leverage advanced AI capabilities for code generation and building a sophisticated data migration suite using a modern technology stack, which includes Java, Spring Boot, Kafka, Debezium, and React.You will work on critical components that ensure the scalability, efficiency, and reliability of our services, collaborating closely with AI researchers, product management and other engineers to design and implement cutting-edge products that solve complex customer challenges.. We are looking to speak to candidates who are based in Sydney for our hybrid working model. The ideal candidate for this role will have 6+ years of engineering experience in backend systems, distributed systems, or core platform development. Proficiency in one or several of Java, Rust, C/C++, and/or Python, with a strong understanding of systems-level programming, memory management, and performance tuning. Extensive experience with streaming data platforms such as Apache Kafka and Change Data Capture (CDC) tools like Debezium Extensive experience with relational data modeling and hands-on experience with at least one SQL database (Postgres, MySQL, etc) Exposure to client-side technologies such as JavaScript and React is a plus Good understanding of algorithms, data structures and their time and space complexity Curiosity, a positive attitude, and a drive to continue learning Excellent verbal and wri

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. The Safety and Customer Care (SCC) team at Lyft manages over 1.7 million monthly human and AI interactions and serves as Lyft's primary direct touchpoint with riders and drivers. We handle critical infrastructure that powers both human associates and AI agents to make riders and drivers feel safe and comfortable while riding or driving with Lyft, transforming every support interaction into a moment of genuine connection. As a Data Engineer on the SCC team, you will have ownership over the data modeling and pipelines that power SCC’s Associate and AI Agent Platform . Your efforts will be critical to the reliability of our pipelines, execution of third party data integrations, accurate reporting of agents performance, and efficiency improvements that can save millions of dollars / year. You will work cross-functionally to bridge Lyft's business goals with data engineering. Your efforts will allow access to business and user behavior insights, using huge amounts of Lyft data to fuel several teams such as Analytics, Data Science, Engineering, and many others. Responsibilities: Owner of the core data pipeline, responsible for scaling up data processing flow to meet the rapid data growth at Lyft Evolve data model and data schema based on business and engineering needs Implement systems tracking data quality and consistency Develop tools supporting self-service data pipeline management (ETL) SQL and MapReduce job tuning to improve data processing performance Write well-crafted, well-tested, readable, maintainable code Participate in code reviews to ensure code quality and distribute knowledge Collaborate cross-functionally with product, engineering, data science, and marketing teams to understand business problems and align on prioritization and solutions Experience: Bachelor's degree in Compute

About the Team: OpenAI’s Hardware organization develops AI-native silicon and system-level solutions for the unique demands of advanced AI workloads. Building on efforts like Jalapeño, the team is developing future generations of AI-native silicon and tightly integrated systems to power the next generation of frontier models. By co-designing chips, systems, tools, and methodologies, the team helps deliver faster, more efficient, and production-ready hardware for OpenAI’s supercomputing platform. Role Overview We are seeking a Package Reliability Engineer to lead reliability engineering for advanced packages used in high-performance AI and computing systems. The primary focus of this role is to assess package level mechanical and thermal reliability risks and apply thermal and mechanical modeling to optimize package design, material selection, and assembly processes. The engineer will also develop reliability test plans with external partners, identify failure mechanisms, perform root-cause analysis, and recommend practical corrective actions. In this role, you will assess package reliability risks from early architecture development through product qualification and high-volume manufacturing. You will work closely with package design, silicon design, system engineering, manufacturing, and ASIC partners to predict package behavior, develop qualification strategies, resolve reliability issues, and improve overall package robustness and lifetime. In this role you will: Lead reliability test plan and assessments for advanced HPC packages, including risk identification, potential failure-mechanism analysis, root-cause investigation, mitigation planning, and corrective-action development. Drive reliability-focused package design optimization based on thermo-mechanical modeling to improve package reliability, power integrity, thermal performance, mechanical robustness, and platform scalability. Develop, validate, and apply package reliability models and lifetime-prediction

Our vision is to transform how the world uses information to enrich life for all . Micron Technology is a world leader in innovating memory and storage solutions that accelerate the transformation of information into intelligence, inspiring the world to learn, communicate and advance faster than ever. As a Metrology Application Engineer at Micron Technology, you will: 1. Develop, analyze and implement metrology measurement techniques in films with a primary focus on lithography overlay, CD-SEM. Also need to cover ellipsometry/ scatterometry characterization, modeling; XRF, and surface profiling techniques. 2. Perform SWR (Special Work Request) lots to assist Engineers to complete the evaluations for set-up metrology of recipe of newly introduced processes, new process equipment, for process improvement, optimization or characterization 3. Be responsible for driving supplier tool CIP activity, assisting in NPI (New Product Introduction) transfer activity, defining quality metrics, achieving metrology performance, and providing weekly summary updates. 4. Be constantly engaged in the Fab operation and performing tasks to improve quality, reduce cost, reduce cycle time, and improve yield. Detailed analysis of next generation measurement capabilities as well as coordinating evolutionary programs. 5. Deliver reliable and value-adding metrology applications to reinforce Micron's technical competitiveness. Standardize BKM, knowledge and way of working to keep the growth of metrology team. Early engage with TD and MCT for cutting-edge tech introduction. 6. In addition, we are looking for engineers who embrace AI-enabled ways of working, leveraging AI, automation, and data-driven insights to accelerate problem-solving, enhance decision-making, improve operational effi

Work Flexibility: Onsite It's Time to Join Stryker! Stryker is seeking a Staff Electrical Engineer to support the design and development of electrical systems and subsystems for complex medical devices. In this role, you will apply your electrical engineering expertise across design, prototyping, testing, troubleshooting, and documentation. You will translate product and customer needs into engineering requirements and robust electrical designs while collaborating closely with cross-functional teams throughout the product development lifecycle. What You Will Do Electrical Design and Development Design and develop electrical components, circuits, and subsystems for medical devices. Translate design inputs and system requirements into electrical engineering specifications and detailed designs. Develop, evaluate, and refine design concepts through analysis, prototyping, simulation, and bench testing. Apply advanced electrical test methods to evaluate component and subsystem performance. Investigate complex technical problems, identify potential solutions, and evaluate design alternatives against product and subsystem requirements. Conduct technical research, analysis, and engineering studies to support design decisions and product development. Assess system-level impacts and dependencies associated with electrical design choices. Verification and Technical Execution Develop and execute test methods to evaluate electrical designs, components, and subsystems. Analyze test results, troubleshoot design issues, and drive problems to root cause and resolution. Apply engineering analysis, modeling, simulation, and statistical methods as appropriate to support design decisions. Partner with other engineering dis

At Affirm, we exist for the moments that matter—giving people a clear, predictable way to pay over time, with no hidden fees, no surprises, and no tradeoffs on what matters most. Financial Systems owns the data and reporting foundation for Accounting, and the operational reliability of the pipelines that power reporting, reconciliations, and automation. We are building a single source of truth for financial information using dbt and Snowflake to enable scalable BI and process automation across the org. We are hiring an Analytics Engineer focused on maintaining and optimizing our finance data platform , improving reliability, efficiency, and performance of our pipelines and core datasets. This role is ideal for someone who enjoys operational ownership, building strong foundations, and making data systems easier and safer to run at scale. What You’ll Do Build and maintain dbt models and core datasets that support Accounting reporting and downstream automation use cases. Improve platform reliability through strong testing patterns, alerting, and runbooks. Own and document data pipelines and lineage, ensuring changes are understandable and auditable. Identify, troubleshoot, and resolve production data issues, and drive root-cause fixes. Optimize performance and cost in Snowflake and dbt to support scaling needs. Partner with engineering and business stakeholders to translate requirements into durable, well-tested data assets. Contribute to the team’s best practices in version control, code review, documentation, and release hygiene (GitHub-based workflows). What We Look For 3+ years of experience in analytics engineering, data engineering, or similar roles working with production data systems. Strong SQL skills and hands-on experience building in dbt (modeling, testing, documentation). Experience operating in a modern development workflow: Git and pull-request based collaboration (GitHub preferred). Familiarity with standard IDEs and collaborative debugging practices. E

Sigma is growing rapidly, and our Technical Support Engineering team is scaling alongside it to meet the needs of an expanding global user base. As a Technical Support Engineer at Sigma, you will be part of an award-winning team recognized with the 2024 Stevie Gold Award for Customer Service, helping customers solve technical, business, and data challenges using the Sigma platform. You'll work closely with Product, Engineering, and Go-to-Market teams to diagnose complex issues, drive solutions, and contribute to the continuous improvement of our product and support operations. Minimum Education Requirement This position requires a U.S. Bachelor's degree (or foreign equivalent) in Computer Science, Software Engineering, Information Systems, Data Science, or a closely related technical field. This requirement is a minimum and cannot be substituted by work experience alone. What You Will Be Doing You will work with Sigma's customers and the pre-sales team to assist with the diagnosis and resolution of complex technical issues. Working closely with the development team, you will develop best practices and tools for diagnosing issues and optimizing the service for performance. Collaborate with cross-functional groups — backend, frontend, DevOps, design, product, and the go-to-market teams to create a first-class experience for users of our product. Participate in quarterly projects and perform periodic on-call duties to improve automation and processes. Qualifications We Are Looking For 2+ years of experience in a customer-facing technical role (Technical Support, Solutions Engineering, or Software Engineering) at a cloud or SaaS provider. SQL proficiency — strong grasp of JOINs, Partitions, Window Functions, Aggregations, CTEs, and sub-queries. SQL query performance troubleshooting and query plan analysis. Proficient in data modeling concepts. Ability to chart data into logical visualizations. A proven track record of building trust with customers and brin

Opportunity Overview: We are seeking a Senior Business Intelligence Engineer to join our Intelligence team. In this role, you will design, build, and optimize scalable data models and analytical assets that power reporting and self-service analytics across the organization. You will play a key role in strengthening our BI ecosystem by partnering closely with data platforms, and product teams to deliver high-quality, governed, and performant datasets and dashboards, while upholding best practices and standards.. This is an ideal opportunity for an experienced BI engineer who thrives in a hands-on technical role, can independently support complex initiatives, and is passionate about building clean, reliable, and scalable data foundations. This position offers the chance to have a substantial influence on the company, with the potential for rapid career advancement in a highly visible environment. What you’ll do: Collaborate with business stakeholders to understand reporting needs and translate them into functional requirements. Design, develop, and maintain BI reports, dashboards, and data visualizations using BI tools (e.g., Tableau, Power BI, etc.). Build and optimize Tableau dashboards and KPI frameworks with a focus on performance, usability, governance, and row-level security. Ensure data consistency and quality in reports by working closely with the data management team. Apply best practices in dbt modeling, testing, version control, and release management to ensure high-quality, maintainable pipelines. Perform data analysis and ad-hoc queries to support business decision-making. Stay current with industry best practices and emerging BI technologies. ISMS roles and responsibilities: Good knowledge of Information practices. Assist the manager in all the information security activities implementation and maintenance process. Ensuring the team and imparted with Competence related to Information security Responsible for implementation of secu

Role Overview We are seeking a Staff Simulation Engineer to build an end-to-end aerial autonomy simulation stack at DoorDash Labs. This is a highly technical, hands-on leadership role focused on defining and implementing the simulation architecture that underpins autonomy development, validation, CI/CD testing, and pilot training. You will operate as the technical authority for simulation: owning core architecture decisions, developing key components yourself, and setting engineering standards. You will build and mentor a small, high-caliber simulation team while remaining deeply involved in implementation and system design. This role is ideal for someone who has built simulation systems from first principles, understands simulator internals deeply, and is excited to create a world-class platform from scratch. Key Responsibilities Architect and implement an end-to-end simulation stack for aerial autonomy at DoorDash Labs.. Develop high-fidelity simulation capabilities, including: Flight dynamics modeling Contact modeling and constraint handling Sensor and perception simulation Autonomy software-in-the-loop (SITL) integration Design and implement scalable simulation infrastructure to support: Regression testing in CI/CD pipelines Continuous validation of flight autonomy and autopilot software stack Mission-level testing and scenario generation Build cloud-deployed simulation systems to enable large-scale parallel testing and pilot training. Partner closely with autonomy, controls, and aircraft teams to ensure simulation fidelity and validation alignment. Establish technical direction, architecture standards, and performance benchmarks for simulation. Mentor and grow a small team of simulation engineers while remaining deeply hands-on. Required Qualifications Master’s or PhD in Computer Science, Electrical Engineering, Mechanical Engineering, Robotics, Aerospace Engineering, or a related field. 10+ years of experience in robotics or physics-based simulation. Deep expe

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. Snowflake runs large scale cloud infrastructure to deliver its own service — production and internal deployments, Kubernetes fleets, CI/CD, etc. Our cloud spend is in billions of dollars per year. The Cloud Efficiency team builds a unified, self-serve cloud efficiency platform along with AI skills and agents that makes spend observable, attributable, governable while driving recommendations and optimization of our cloud spend. AS A SOFTWARE ENGINEER AT SNOWFLAKE YOU WILL: Design, develop, and maintain scalable platform for resource ownership registry, usage attribution, utilization measurement, and cost modeling. Build AI agents, tools and automation to enhance system monitoring, alerting, and root cause analysis. Improve and optimize data ingestion, storage, and query efficiency for cloud utilization, cost and efficiency data at scale. Collaborate with teams across Snowflake to understand attribution and observability needs and implement solutions that improve operational visibility. Contribute to open-source and industry best practices in monitoring and distributed systems monitoring. Ensure high availability, reliability, and performance of team-managed platforms by participating in on-call rotations and incident management. Partner with Finance, Product and Engineering

We are looking for an experienced Staff Software Engineer with a strong background in building software frameworks, LLM eval frameworks and team leadership. You will lead a talented team building a complex product suite using a modern stack. The ideal candidate is a hands-on technical leader who can drive architectural decisions, mentor engineers, and collaborate closely with product management to deliver a product that solves our customers' most challenging modernization validations problems, ensuring that new applications have the same functionality and performance as their legacy counterparts. This role will be based in our India office in Gurgaon and offers a hybrid working model. The ideal candidate for this role will have 8+ years of commercial software development experience with at least one JVM language such as Java, preferably using the Spring ecosystem and paired with strong Python experience 2+ years of experience leading, coaching, and mentoring a team of software engineers to achieve high-impact results Solid experience in software architecture and development Familiarity with evaluation frameworks (such as DeepEval, Ragas, and Promptfoo) and harnesses, alongside benchmark datasets, scoring/grading approaches, and LLM-as-a-judge methodologies Experience in application/database modernization with proven ability to design and implement frameworks that ensure database state equivalence across different implementations Must understand change data capture, event-driven database interception (MongoDB listeners, RDBMS triggers), and state comparison algorithms with pattern-based exclusions Extensive experience with relational and document data modeling and hands-on experience with at least one SQL database (Postgres, MySQL, etc) and at least one document database (e.g. MongoDB) Good understanding of algorithms, data structures and their time and space complexity Curiosity, a positive attitude, and a drive to continue learning Excellent verbal and written comm

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