Production Dx Engineer Jobs

Location : Remote (U.S.) Other Considerations: U.S. Citizen or Permanent Resident required with the ability to obtain a Public Trust LTS is seeking a Cyber Security Analyst to support the Department of Veterans Affairs (VA) Health Portfolio. The Health PMO support contract provides IT program management, project management, technical management, financial management, functional planning, and documentation analysis in support of the Enterprise Program Management Office (EPMO) Enterprise Program Management Division (EPMD) Health Portfolio The VA Health Portfolio is focused on development and execution of quality IT projects that address the needs of Veterans and health care providers through management of health care information and sustainment of production software to keep the mission going. What You'll Do: This individual will support cybersecurity efforts across multiple projects within a portfolio setting and proactively monitor upcoming cyber priorities across the VA Health portfolio. Communicate and provide consultative support on the VA on matters related to system security certification & accreditation and Authority to Operate (ATO). Coordinate and lead security and privacy activities within project teams and develop security and privacy related artifacts. Review and identified system/applications security controls in accordance with NIST SP-800 53 Rev 4 guidance and VA Handbook 6500. Responsible for on time deliverables of assigned and related security and privacy artifacts. Must be able to identify and mitigate risks to the program. Demonstrated experience in a remote work environment. Ability to proactively communicate and coordinate with various internal and external project stakeholders, depending on needs. Identifying and communicate symptoms for process improvement. Ability to work independently with minimal guidance and supervision Support a team of other cyber security professionals Support Application development/maintenance and IT

Our vision is to transform how the world uses information to enrich life for all. Join an inclusive team passionate about one thing: using their expertise in the relentless pursuit of innovation for customers and partners. The solutions we build help make everything from virtual reality experiences to breakthroughs in neural networks possible. We do it all while committing to integrity, sustainability, and giving back to our communities. Because doing so can fuel the very innovation we are pursuing. Key Responsibilities Capacity Optimization & Advanced Analytics Develop optimization models to improve factory capacity utilization, throughput, cycle time, tool loading, and bottleneck management. Build mathematical models for capacity planning, production allocation, constraint identification, and investment prioritization. Apply operations research techniques such as linear programming, mixed-integer programming, constraint programming, stochastic optimization, and simulation-based optimization. Design algorithms to support factory maxout strategies and identify opportunities to unlock additional capacity without unnecessary capital investment. Semiconductor Manufacturing Problem Solving Partner with manufacturing, industrial engineering, planning, equipment, process, and business teams to understand capacity constraints and operational challenges. Analyze tool capability, process flows, product mix, WIP movement, cycle time, dispatching rules, and factory constraints to recommend optimization opportunities. Support scenario analysis for capacity expansion, product mix changes, technology transitions, and capital planning decisions. Develop data-driven recommendations that improve decision quality

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. Role Overview The ASIC Test Engineer is responsible for test program development, silicon validation, yield analysis, and production test optimization for advanced ASIC/SSD controller products. This role supports both NPI and HVM phases, driving fast ramp, robust quality, and cost-effective test solutions across wafer sort and final test. Key Responsibilities Develop and debug ATE test programs for wafer sort and final test (probe to package) to support silicon bring-up and production ramp. Drive yield analysis, failure characterization, and root cause investigation to accelerate yield improvement and ensure product quality. Collaborate with design, product engineering, reliability, and OSAT teams to resolve test and silicon issues across the product lifecycle. Optimize test coverage, test time (TTR), and cost through test content development, guardband tuning, and screening strategy improvements. Support hardware bring-up including probe cards, load boards, and tester configurations to ensure stable manufacturing operations. Enable efficient NPI execution by developing scalable test solutions and improving cycle time from bring-up to HVM. AI/Advanced Data Capability Apply AI/ML and data-driven methodologies to enhance yield analysis, anomaly detection, and failure classification. Leverage AI-assisted tools (e.g., code generation, debug automation, data analytics platforms) to improve engineering productivity and debug cycle time.<br

About the Team DoorDash’s GenAI Platform team sits within Machine Learning Platform and builds the shared infrastructure that helps DoorDash, Wolt, and Deliveroo teams safely bring GenAI-powered products, agents, automation, and personalization to production. Our mission is to increase the velocity of business impact from GenAI. A central pillar of that work is running frontier open-weight LLMs and VLMs (such as GLM, Qwen, Kimi, and DeepSeek) ourselves — real-time GPU serving, high-throughput batch inference, and fine-tuning on autoscaling GPUs — delivering large cost and latency wins (for example, a billion embeddings produced roughly 20× cheaper and visual models served roughly 72% cheaper). We also own core platform surfaces including the LLM Gateway, Agent Gateway, evals infrastructure, guardrails, and cost attribution. About the Role You will join a small, high-leverage team building production infrastructure for Generative AI at DoorDash, leading the design and architecture of our open-weights model platform spanning inference and fine-tuning: real-time GPU serving, high-throughput batch inference, and model fine-tuning. You’ll set technical direction across model serving and inference engines, fine-tuning and training pipelines, GPU autoscaling and utilization, batch pipelines, backend services, and observability, and mentor engineers as you go. This role is ideal for a senior engineer who enjoys owning ambiguous, high-impact systems and pushing the cost/performance frontier of GPU inference and fine-tuning in a fast-moving technical area where product needs, model capabilities, vendor ecosystems, and cost/performance tradeoffs are evolving quickly. You’re excited about this opportunity because you will… Lead the design of infrastructure that helps DoorDash teams move GenAI ideas from prototype to production, increasing the velocity of business impact from AI across the company. Own and evolve our open-weights serving stack — real-time GPU endpoints, high-thr

Data Semantics is Datadog’s authority on semantic knowledge, providing shared infrastructure that powers both Datadog’s product experiences and AI capabilities. As Datadog continues its investment in OpenTelemetry-native observability, semantic interoperability, and AI-powered workflows, this team sits at the center of some of the company’s most strategic platform initiatives. As a Staff Engineer, you will serve as a technical leader for the team, balancing stewardship of critical production systems with the exploration of new platform capabilities that improve how telemetry is modeled, understood, and consumed across Datadog. You will work closely with engineering and product partners to define standards, drive technical direction, and deliver solutions that scale across Datadog’s observability platform. At Datadog, we place value in our office culture - the relationships and collaboration it builds and the creativity it brings to the table. We operate as a hybrid workplace to ensure our Datadogs can create a work-life harmony that best fits them. What You’ll Do: Lead the technical direction of the Data Semantics team while remaining deeply hands-on in design, implementation, and delivery. Build and scale semantic infrastructure that bridges OpenTelemetry, Datadog-native telemetry, cloud-provider telemetry, and customer-defined data models. Drive platform initiatives focused on schema evolution, telemetry standardization, data insights, and semantic interoperability across Datadog products. Partner with engineering and product teams across the platform to define standards, align stakeholders, and deliver high-leverage platform capabilities. Mentor engineers through design reviews, technical guidance, operational excellence, and long-term career development. Participate in on-call rotations and lead investigation and resolution efforts for complex production incidents affecting critical platform services. Who You Are: You have significant experience designing, opera

About Us Paytm is India's payment Super App offering consumers and merchants the most comprehensive payment services. As the pioneer of the mobile QR payments revolution in India, today, Paytm stands as India’s largest payment company by Users, Merchants, Payment Transactions, and Revenue. Paytm’s mission is to drive financial inclusion in India and bring half a billion Indians into the mainstream economy through technology-led financial Services. Paytm enables commerce for small merchants and distributes various financial services offerings to its consumers and merchants in partnership with financial institutions. Paytm has been a pioneer in the merchant space by introducing innovative solutions like QR codes to accept payments and Sound-box to reconcile payments via voice alerts. We are also distributing loans to these partners via our ‘Paytm for Business’ App. About The Team Paytm Intelligence is building next-generation AI platforms across two key areas: AI inference infrastructure and agentic AI solutions. Our focus is on enabling enterprises to move from AI experimentation to production-grade deployment with measurable business outcomes. In the agentic AI space, we are building solutions such as Outreach Manager, CLM agents, and domain- specific AI agents that automate complex customer journeys across sales, service, and operations. These systems go beyond traditional chatbots, orchestrating workflows, decisions, and actions across enterprise systems. About the Role We are hiring an AI Agentic Solutions Architect to drive enterprise adoption of AI-powered workflow automation. This role sits at the intersection of business process transformation, AI systems, and enterprise sales. You will work directly with enterprise customers to understand their workflows, identify automation opportunities, and design agentic solutions that deliver measurable outcomes such as cost reduction, conversion uplift, and improved customer experience. Key Responsibilities Customer

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 US Expansion Planning Engineer, you will be a key contact in preparing for production in a new high volume memory manufacturing facility in Boise, Idaho, calculating and presenting capital impacts of processes that will be introduced at the site to senior leaders in manufacturing and R&D. Innovation is highly valued at Micron. In this role, you will have the opportunity to develop new visualization and data driven tools to expedite, optimize and refine the fab planning and reporting processes used to prepare, ramp, and operate the site. Successfully partnering with supply chain, finance, engineering, procurement, production, and R&D, you will connect the business in driving execution of manufacturing, including long term capacity, capital planning and cost savings, for the operation. The ability to understand and efficiently communicate capital impacts of manufacturing changes is essential. Every day, our team has the opportunity to provide recommendations which will transform the future of memory manufacturing through data analysis and a growth mindset. Are you ready to meet the challenge? Your responsibilities will include, but are not limited to: Collaborate with partners to drive future tool and process roadmaps, and calculate network capacity impacts of process changes Complete financial analyses before new capital equipment is purchased including metrics optimization, process flexibility, capacity sharing and equipment repurpose Handle site tool utiliza

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’s Release Engineering team builds and operates the systems that safely deliver infrastructure, platform, and product changes to production at global scale. We own the release platforms, rollout orchestration, and safety mechanisms that allow engineering teams across Snowflake to ship quickly while minimizing operational risk. Our mission is to make production deployments fast, safe, self-service, increasingly autonomous, and augmented by AI-driven intelligence and automation. This role sits at the intersection of developer productivity, distributed systems reliability, and large-scale multi-cloud infrastructure orchestration. At Snowflake, Release Engineering is a platform engineering function focused on building the systems, abstractions, and automation that make software delivery safe, scalable, and efficient across the company. In this role, you will Design and build continuous deployment and rollout infrastructure that safely ships changes across Snowflake’s large-scale, multi-cloud production environment. Build and evolve platform capabilities for progressive delivery, including staged rollouts, canarying, automated health checks, rollback controls, and guardrails that reduce blast radius during production change events. Improve engineering velocity by removi

About the role: We are seeking a Senior Backend Engineer with deep backend engineering expertise and proficiency in one or more major programming languages (e.g., Python, Java, Go, Rust, or Kotlin), along with a strong understanding of AI models and agents. As a core member of our AI Engineering team, you will collaborate with data scientists, ML engineers, and product managers to build scalable, production-ready infrastructure and APIs that power intelligent systems. What you'll be doing: As a Senior Backend Engineer in the AI Engineering team, you will: Build and maintain reliable, scalable backend services to support AI agent execution and orchestration. Develop AI agent systems for complex operational workflows using LangChain, LangGraph, LiteLLM, and Langfuse. Orchestrate a hybrid model stack that includes OpenAI and Google Gemini alongside self-hosted and fine-tuned LLMs like Gemma and Llama. Build and maintain integrations with clinical systems (FHIR, EMR). Drive observability and reliability using OpenTelemetry, Datadog, and Langfuse. Design APIs (GraphQL, REST), background workers, and event-driven systems that interface with AI inference engines and agent runtimes. Collaborate with Data Science, ML, and engineering teams to deploy AI features and improve the performance, scalability, and reliability of backend systems. Participate in code reviews, knowledge sharing, and mentoring to elevate the team’s technical capabilities. What we're looking for: 6+ years of backend engineering experience, with strong proficiency in more than one major programming language (such as Python, Java, Go, Rust, or Kotlin). Solid understanding of AI systems architecture and experience working in environments involving AI agents, LLMs, or inference pipelines. Proven experience in building and scaling backend APIs, microservices, and background jobs. Strong experience with relational and NoSQL databases (e.g., PostgreSQL, MySQL, MongoDB, Redis), including schema des

FEQ327R502 The Manager, Specialist Solutions Architect will lead a team of Specialist Solution Architects and leaders in the Life Science vertical, who provide Specialist coverage for both our Solutions Architect (SA) teams and Delivery Solutions Architect (DSA) team. Your experience when partnering with the pre-sales technical team and leaders will help drive technical wins and accelerate Databricks DBU consumption. You will be responsible for guiding and empowering your team to communicate complex, outcome-based results that drive consumption and help our customers achieve their desired outcomes. The impact you will have: Hiring and building the Specialist Solutions Architect team, scaling the designated field engineering segment organization without sacrificing quality Build a collaborative culture within a hyper-growth team to embody and ensure Databricks' company values Drive Databricks product adoption and accelerate use cases to production within the Life Sciences vertical. Collaborate closely with cross-functional teams, including Sales, Marketing, Support, Product Development, Operations, and other FE teams, to strategize and align efforts towards achieving HLS vertical's incremental consumption target and commit target. What we look for: 2+ years leadership experience building and guiding teams Demonstrated expertise in designing and implementing complex, large-scale solutions within the healthcare and life science industries, with a solid understanding of the specific challenges and data requirements in the HLS vertical. Strong knowledge of HLS-specific regulations, such as HIPAA, GDPR, and industry standards like HL7, FHIR, and DICOM. Familiarity with life science-related use cases and topics, including genomics, bioinformatics, clinical trials, drug discovery, real-world evidence, and precision medicine. In-depth understanding of healthcare-related use cases and topics, such as electronic health records (EHR), telemedicine, medical ima

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. Responsibilities and Tasks: • Establish and improve process condition and technology. • Upgrade process capability and reduce production cost. • Establish and modify process management projects. • Set up process parameters for a variety of semiconductor equipment. • Evaluation, promotion and planning of new equipment / materials. • Abnormal analysis and improvement. AI Relevant Job Responsibilities: - Integrates AI-assisted tools and insights into daily work to improve efficiency, quality, or effectiveness, exercising sound judgment and complying with organizational standards and legal requirements. - Contributes to a culture of continuous improvement by identifying, testing, and sharing AI-enabled enhancements within one’s scope of work. - Ability to apply baseline digital fluency and role‑appropriate AI literacy to use AI‑enabled tools responsibly and effectively for research, analysis, content creation, problem‑solving, operational tasks, and achieving business About Micron Technology, Inc. We are an industry leader in innovative memory and storage solutions transforming how the world uses information to enrich life for all . With a relentless focus on our customers, technology leadership, and manufacturing and op

What you’ll do Own day-to-day operations for the scanner program and scanner builds in the spa: purchasing/procurement, vendor management, receiving, inventory, and logistics. Stand up lightweight production operations as we move from prototypes to repeatable builds: build planning, kitting, work instructions, and readiness checklists. Partner with Quality to ensure the operational system supports compliance: traceability, document control, training records, NCR/CAPA workflows, and audit readiness. Drive cross-functional execution for the physical spa build-out and scanner integration: schedules, dependencies, risk register, and weekly coordination with vendors and internal teams. Own the “integration glue” across facilities + device ops: commissioning plans, acceptance criteria, and operational handoff (runbooks, maintenance, spares, escalation paths). Build and track operational metrics: cost, budget, lead times, vendor performance, build throughput, and reliability of critical subsystems. Audit of import/export documentation, management of contract renewals, regulatory compliance. What we’re looking for Proven operations leadership in hardware/medical/robotics (or similarly complex electromechanical products), including procurement and vendor management. Strong program management instincts: can run schedules, unblock cross-functional dependencies, and keep priorities clear under ambiguity. Comfort operating in quality/regulatory environments and building processes that are rigorous without slowing a small team. High ownership and bias to action: can jump between spreadsheets, docks, and the lab/site to keep the program moving. Useful experience Experience running prototype-to-production transitions (NPI, EVT/DVT/PVT-style builds, CM/EMS collaboration). Facilities / construction operations experience (GC coordination, MEP commissioning, site readiness). Familiarity with inventory systems and procurement tooling (even “simple but disciplined”).

What you’ll do Partner with medical image reconstruction scientists / engineers to build ML components that improve reconstruction quality, speed, robustness, or quantitative accuracy. Define training/evaluation pipelines, datasets, and metrics that map to user needs and design requirements. Productionize models: inference performance, reproducibility, monitoring for drift/regressions, and safe fallbacks. Collaborate on hybrid algorithms, incorporating physics and learned priors, denoisers, learned regularizers, and quality estimation. Help build tooling for rapid experimentation as well as rigorous verification of algorithm changes. What we’re looking for Strong applied ML experience plus comfort with signal processing / imaging or adjacent domains. Ability to move fluidly between research prototypes and production-quality systems. Strong evaluation discipline: metrics, ablations, data leakage avoidance, and reproducibility. A demonstrated track record of applying ML to physics-based or inverse problems (i.e., shipped projects, a portfolio, or publications.) Useful experience ML for imaging/inverse problems (or adjacent) with strong evaluation discipline and comfort with GPU performance constraints. Pragmatic production mindset: reproducible training/inference, regression testing, and safe deployment in high-stakes contexts. A background in computational physics or scientific computing. Leverage ML-based methods such as PiNNs and Neural Operators to solve partial differential equations arising in ultrasound simulation and imaging. Experience in Agentic-SciML is a plus. Hands-on experience with data curation for ML: building datasets from messy, real-world sources, defining ground truth, and managing labeling or simulation pipelines. Background in data assimilation: combining observations with physics-based models (Kalman filtering, variational methods, ensemble approaches, or learned variants).

The Perception team is pioneering the development of a multi-modality foundation model to drive the next generation of autonomous system intelligence. As a Machine Learning and System Optimization Engineer, you will orchestrate and allocate overall system capacity to various core perception models running on-bot, as well as drive large initiatives that allow for more efficient inference by sharing various parts of the perception stack with one another. You will focus on bringing highly efficient, production-ready large-scale models to our on-vehicle stack. We are looking for experts with hands-on experience compressing, accelerating, and deploying complex models, including LLMs, VLMs, or foundation models, for power- and thermal-constrained vehicle SoCs. In addition, you will optimize ML models, write custom CUDA kernels, and build highly concurrent inference code to ensure real-time, deterministic execution on edge devices. In this role, you will: Allocate and distribute system resources (CPU/GPU/interconnect) to various models and inference engines running on the robot. Spearhead cross-cutting initiatives that allow for better compute utilization through sharing/fusing models and better scheduling strategies. Optimize large-scale models (Multi-Modal Sensor Fusion models, LLMs, VLMs) using advanced quantization (PTQ, QAT), pruning, mixed-precision inference frameworks, and parameter-efficient fine-tuning (LoRA, QLoRA). Architect and implement model conversion and compilation pipelines using TensorRT for edge deployment. Write production-level, low-latency, and memory-safe C++ and CUDA code for real-time inference on vehicle systems. Qualifications: Deep experience in system and performance optimization in CPU/GPU systems designed for low latency or high throughput. Deep expertise in working with real-time systems & required

What you’ll do Design and implement secure cloud pipelines that ingest very large scan datasets (multi-terabyte), reliably and resumably. Build orchestration for GPU-accelerated reconstruction and analysis with strong retry semantics, idempotency, and cost controls. Define end-to-end data lifecycle for medical imaging: raw vs intermediate vs derived artifacts, retention policies, and reproducibility. Implement security + compliance primitives appropriate for HIPAA/PHI: encryption in transit/at rest, key management, least privilege, audit logs, and access reviews. Build operational tooling: monitoring, alerting, runbooks, and incident-driven improvements for a growing device fleet. What we’re looking for Strong experience with cloud batch/queueing/orchestration, storage systems, and data pipeline reliability. Experience shipping production systems that handle large data volumes and failure-prone networks. Practical security mindset (least privilege, secrets, audit logging) and comfort operating in compliance-constrained environments. Useful experience Building reliable data pipelines at scale (queues/orchestration, resumable uploads, GPU batch execution) with strong observability. Security + privacy by default: encryption, least-privilege access, auditing, and practical HIPAA/PHI guardrails. Owning the “boring” backend details that keep a lean team moving: schemas/migrations, cost controls, retries, and runbooks. Understanding compute tradeoffs across hardware options, and specifying appropriate cloud resources.