Production Supervisor Jobs

The following description is not intended as an exhaustive list of all responsibilities, duties and skills required of personnel so classified. Management retains the discretion of adding or changing the duties of the position at any time. Key Responsibilities Lead end-to-end project management for (Client) and other Tier-1 OEM customers, from project initiation through FATP ramp-up and mass production. Act as the primary customer interface, managing project timelines, deliverables, and communication to ensure alignment with client expectations. Collaborate with cross-functional teams (engineering, operations, supply chain, quality, procurement, and finance) to drive successful program execution. Manage project schedules, risks, and resource allocation to ensure on-time delivery and cost targets are met. Support contract negotiations and business terms with (Client) customers, ensuring alignment with company goals and profitability. Provide technical and business support during customer meetings, audits, and on-site visits. Drive NPI (New Product Introduction) readiness, FATP line setup, and factory ramp-up by coordinating between client teams and internal operations. Monitor material readiness, demand planning, and capacity to support business continuity and minimize production risks. Conduct regular project reviews with internal stakeholders and clients, escalating risks and driving corrective actions. Support business development initiatives by analyzing (Client)/OEM roadmaps, identifying growth opportunities, and contributing to proposals and pricing strategies. Benchmark industry practices and client requirements to optimize cost structure, quality, and efficiency across FATP programs. Build and maintain strong client relationships, serving as the liaison to ensure clear communication between client, internal teams, and contract manufacturers. Qualifications Bachelor’s degree in Business, Supply Chain Management, Engineering, or related field. 2+ years of exper

About Snorkel Snorkel AI is the frontier AI data lab, helping teams build the data and environments behind high-performing frontier and agentic AI. We combine technology with research-driven AI data development to create datasets, benchmarks, evals, and custom solutions for real-world AI systems. Founded out of the Stanford AI Lab in 2019, Snorkel works with leading AI labs and enterprises to move from better data to better outcomes. Excited to help us redefine how AI is built? Apply to be the newest Snorkeler! About the Role Snorkel AI is hiring a Manager, Forward Deployed Engineering to build and lead our Forward Deployed Engineering (FDE) function within the Data-as-a-Service (DaaS) organization. This team is responsible for the technical execution layer of DaaS delivery — owning the systems, workflows, and quality frameworks that power high-quality dataset production at scale. You will lead a team of engineers who operate across the full AI data lifecycle: designing evaluation systems, building ML-assisted workflows, and developing the technical foundations that make human-in-the-loop (HITL) data generation faster, more reliable, and scalable. This is a hands-on, player coach role where you’ll both help define the strategy and architecture of our data pipelines while staying close to implementation. As a founding leader, you will establish how FDE partners with Delivery, Engineering, and Research to ensure consistent, high-quality execution across all DaaS projects. You’ll operate at the intersection of data engineering, ML engineering, and production operations, driving the evolution of our technical delivery capabilities and enabling Snorkel to scale its impact across increasingly complex AI workloads. Main Responsibilities Build and lead the Forward Deployed Engineering function, defining the team’s operating model, technical roadmap, and standards for execution Hire, mentor, and develop a high-performing team, fostering strong ownership, velocity, and

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. About the role: We are seeking an experienced Optical Network Engineer to lead Laser related work within our optical interconnect efforts for large-scale compute systems. The role also requires broad, hands-on optical validation experience across IM/DD-based interconnects, working from lab characterization through production readiness and scaled deployment. In this role you will: Drive laser-focused requirements and technical direction within the broader optical interconnect roadmap. Lead evaluation and validation of optical components and subsystems, including laser-based elements, in lab and production-representative environments. Support end-to-end optical testing for IM/DD interconnects (e.g., module/system bring-up, characterization, debug, and readiness for scale). Work with external partners to align on development milestones, performance targets, and quality expectations. Own technical issue triage and resolution across performance, reliability, and manufacturability topics. Collaborate across internal teams to support integration, rollout, and operational success at scale. You might thrive in this role if you have: Strong experience in laser-focused optical engineering (development, validation, manufacturing readiness, or field support). Broad hands-on background with IM/DD optical technologies and optical test/debug workflows. Experience working with external suppliers/manufacturing partners and production-oriented execution. Demonstrated ability to debug complex t

ABOUT BASETEN Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma, and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $1.5B Series F , led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn to ship AI products. THE ROLE We’re hiring a Data Engineer to build and scale Baseten’s internal data platform. This role sits at the intersection of data engineering, analytics, and data science, transforming raw product and business data into reliable datasets that power decision-making. You’ll design the data models, pipelines, and analytics infrastructure that enable teams across Product, Engineering, Finance, Marketing, and Sales to understand usage and performance. This includes working with AI inference, infrastructure, and observability data to generate insights about the product, business operations and platform economics. You’ll partner closely with stakeholders to build robust, scalable pipelines, define company-wide metrics that inform strategy and planning. RESPONSIBILITIES Design and maintain core data models and semantic layers Develop and orchestrate batch and streaming data pipelines using technologies such as Apache Beam, Kafka, Airflow, or similar frameworks Analyze inference and infrastructure telemetry , including data from OpenTelemetry, Grafana, and other observability tools Define and maintain company-wide metrics across product usage, performance, and customer lifecycle Enable self-service analytics through agents and tools, with well-structured semantic layers and context Ensure data reliability and quality through testing, documentation, and governance PREFERRED QUALIFICATIONS Understanding of inference metrics suc

About the Team DoorDash Labs is a team within DoorDash building autonomous delivery robots and other autonomy solutions from the ground up for DoorDash's core delivery platform. If you have a passion for applying autonomy and robotics in a service used by millions of people daily, then we want to talk to you! About the Role We are seeking a hands-on Electrical Engineer to review the design, drawings, and schematics of the electrical systems for our 4-wheeled autonomous delivery robot. This role is responsible for delivering robust, production-ready hardware across high-speed digital design, PCBAs, RF integration, harnessing, and board-level packaging. This is a deeply technical position for someone who can move seamlessly between system-level thinking and detailed circuit execution. You're excited about this opportunity because... Develop electrical systems for a complex mobile robotic platform from concept through production. Lead high-speed digital design efforts (PCIe, MIPI, Ethernet, DDR, multi-gigabit SERDES, etc.). Own PCBA development including schematic capture, stack-up definition, layout guidance, impedance control, and design-for-manufacture. Drive RF integration (Wi-Fi, LTE/5G, GNSS, Bluetooth, RADAR, and LiDAR), antenna placement, layout constraints, and signal integrity. Develop and review cable harness designs, connector selection, grounding strategy, and environmental robustness. Collaborate closely with mechanical engineering on board packaging, thermal management, vibration resistance, and environmental sealing. Define and implement power distribution architecture, battery interfaces, protection circuits, and system monitoring. Ensure compliance with EMI/EMC and regulatory requirements (FCC/CE) (CISPR-25 and FCC Part 15 Class A). Support prototype builds and bring-up, debug hardware/firmware interface issues, root-cause failures, and drive corrective actions quickly. Establish design standards and perform detailed design reviews across the EE team.

At Vanta, our mission is to help businesses earn and prove trust. We believe that security should be monitored and verified continuously, and we empower companies to practice better security and prove it with ease. Vanta has a kind and talented team, and while some have prior security experience, many have been successful at Vanta without it. Vanta’s Developer Experience team builds the tools engineers use every day to bring ideas to production rapidly and reliably. You’ll empower other Vanta engineers to leverage cutting-edge technologies and best practices to make Vanta more performant and scalable on a platform level. Example projects include modernizing our CI/CD pipelines, introducing new test frameworks, launching AI-powered dev tools, and scaling developer environments to support a growing engineering team. This team has a wide breadth of impact across all of product engineering. The work we do compounds in value by making it easier for engineers to diagnose and solve bugs, streamline workflows, and ship value to our customers quickly and safely. Vanta engineers design and develop new product functionality and infrastructure leveraging modern frameworks and tooling, including TypeScript, React, Node.js, MongoDB, Github Actions, and various AWS services such as Fargate and ECS. Vanta has a kind and talented team, and while some have prior security experience, many have been successful at Vanta without it. We’d love for you to join us! You will: Set direction for critical dev infrastructure, enabling us to stay ahead of continued rapid growth Design and build CI and build systems that ensure Vanta engineers can develop and ship robust products quickly and confidently Improve the efficiency and reliability of our deployment workflows, including tools for hotfixes, rollbacks, and incident mitigation Lead development of tools that accelerate feedback loops — from typechecking and linting to running tests and deploying changes Build and maintain scalable developmen

Agents have changed the game for software delivery and efficacy. Diligent is the leading GRC platform in the world, and we are racing ahead to take the agents show on the road and work with the customers where they work . The FDE function will lead the change on how we embed AI agents into some of the world’s most complex governance, risk and compliance environments. This is not a support or consultancy role. It is a builder role, for someone who is equally comfortable reading a failing agent trace, running a discovery workshop with a bank’s internal audit team, and translating what they find into a production-grade agentic solution. You will be building AI agents for GRC professionals , not assistants that surface suggestions, but agents that own complex, multi-step workflows end to end . Agents that customers can hand a task to and trust it will come back done. Closing the gap between a promising prototype and something a company Board and ELT depends on is a completely . Here’s a breakdown of what you’ll do Embed directly with major enterprise customers (global banks, regulated corporates) across EU and US ; sitting wi th internal audit teams, risk functions, compliance and governance professionals to understand their real workflows and devise agentic solutions to intelligently automate them creating tremendous efficacy and efficiencies for our customers. Run agent-focused discovery workshops, rapidly prototype agentic solutions, and test them with practitioners; distinguishing between workflows that need an agent and those that need a button. Source, integrate, and move data between enterprise systems as part of live customer implementations — understanding the real data landscape customers operate in and building reliable pipelines to support it . Take agents from prototype t

About the Team The Statsig team within OpenAI owns the experimentation, rollout, dynamic configuration, and analytics infrastructure that sits on the launch path for OpenAI products. Our systems help teams ship safely, evaluate product and model changes in production, and make high-confidence decisions from real-world usage. Statsig began as an independent company built around experimentation, feature management, and product analytics at scale. After Statsig joined OpenAI, the team began the next chapter: bringing that platform expertise and infrastructure into OpenAI as the experimentation and rollout foundation for every product we ship. This is infrastructure with a very direct product consequence. Teams working on ChatGPT, Codex, model measurement, consumer experiences including ads, business subscriptions, developer products, and shared platform systems depend on Statsig to evaluate configurations, move traffic safely, ingest experiment data, serve analytics, and roll changes forward or back when production reality demands it. We are at a critical point in the platform journey. Adoption is accelerating quickly across OpenAI, and the systems that were already important are becoming load-bearing for how the company launches. The infrastructure needs to stay fast under sharply increasing evaluation volume, reliable when more services depend on it, observable enough to debug quickly, and efficient enough to support OpenAI-wide scale. Recent SDK and server-side infrastructure work has already produced measurable wins in latency, reliability, memory usage, and compute efficiency across important services. The next phase is to make those gains systematic: a platform that can absorb rapidly growing product velocity while preserving low latency, data quality, operational safety, and developer trust. Based out of OpenAI's Bellevue office, we are a close-knit team that values in-person collaboration, technical depth, operational ownership, and building infrastructure that

NK Securities Research is a leading financial firm that leverages cutting-edge technology and sophisticated algorithms to trade the financial markets. Founded in 2011, we have gained invaluable experience in the field of High-Frequency Trading (HFT) across different asset classes. Role Overview We’re looking for engineers who can take AI work beyond experiments and make it hold up in production. You’ll work closely with quant researchers and infra engineers to build AI systems that actually get used improving research speed and internal tooling without slowing down the core stack. We value engineers who think about trade-offs, test what they build, and care about how things run in production. What You’ll Build Production AI Ship models that meet defined latency and reliability expectation Add monitoring, rollback, and guardrails before anything goes live Optimise inference across CPU/GPU environments when it matters Integration into Real Systems Plug AI into data-heavy workflows without hurting performance Work within existing low-latency architecture instead of fighting it Profile and remove bottlenecks rather than guessing AI for Engineers & Researchers Build tools that genuinely speed up research and development Improve code understanding, review workflows, and internal knowledge retrieval Keep systems auditable and predictable LLM & Retrieval Systems Implement structured RAG and embedding pipelines with validation in place Create safe integration layers between models and internal systems Performance & Standards Track latency, drift, and stability — not just accuracy Build observability into everything you ship Help raise the bar for how AI is engineered here What We’re Looking For Strong Python fundamentals Clear thinking around system design and performance trade-offs Experience deploying AI systems in production (1–5 years is typical) Familiarity with transformers, embeddings, or LLM deployment Nice to have: Exposure to C++ / Rust / Go E

About the Team The compute infrastructure team runs the GPU fleet and large-scale compute clusters that serve the models backing ChatGPT and the API, while also supporting training workloads for our next generation models. We operate a large, modern GPU fleet and provide a unified platform for other OpenAI teams to seamlessly run production Applied AI and Research training workloads. We seek to learn from deployment and distribute the benefits of AI, while ensuring that this powerful tool is used responsibly and safely. Safety is more important to us than unfettered growth. About the Role You will be part of an engineer-first TPM team as a Technical Program Manager for Compute Infrastructure who owns the end-to-end delivery of large-scale GPU clusters, partnering with engineers to bring clusters online across external providers and partners. You’ll run a broad, parallel portfolio spanning hardware, networking, power, and cooling—driving execution, risk management, and crisp alignment from working teams through leadership to deliver production-ready capacity at scale. 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: Lead end-to-end delivery of both New Compute SKUs and large-scale GPU clusters across an external partner ecosystem while supporting capacity planning for training and inference. Ability to contextually drive multi-threaded bring-up programs spanning hardware, networking, power, and cooling—owning plans, dependencies, and critical paths. Interface with chip providers to derisk long-term onboarding to new hardware platforms by working across kernels, comms, hardware, and scheduling engineering teams. Build and operationalize program mechanisms (roadmaps, milestones, risk registers, runbooks) that make delivery predictable at massive scale. Partner with engineering to improve cluster turn-up reliability, repeatability, and automation

About the Role At FourKites we have the opportunity to tackle complex challenges with real-world impacts. Whether it’s medical supplies from Cardinal Health or groceries for Walmart, the FourKites platform helps customers operate global supply chains that are efficient, agile and sustainable. Join a team of curious problem solvers that celebrates differences, leads with empathy and values inclusivity. As a Senior Customer Engineer, you own the technical customer relationship end to end. You run discovery independently, design integration and agentic workflow architectures for complex enterprise problems, and deploy solutions live with customers — often before they know exactly how to articulate what they need. You write optimized, production-grade code at speed, grounded in strong data structures and algorithms fundamentals, because compressing the time from customer problem to working solution is how FDE delivers its value. You bring genuine innovation to hard problems — your solutions are technically sound, elegant, and often non-obvious. You are the primary technical contact for 2–4 major enterprise accounts, you mentor FDEs on the team, and you are building the skills that will take you into Staff-level technical leadership. What You'll Do Own complex integration and AI agent implementations end-to-end — from technical discovery through go-live and post-launch enhancement — as the primary technical contact for 2–4 enterprise accounts Design integration architectures with explicit attention to error handling, retry logic, observability, failure recovery, and multi-system authentication Design and deploy agentic AI workflows that orchestrate supply chain operations — from requirements through production, including regression testing and validation before each customer deployment Deploy AI agent workflows live with customers present — configuring and troubleshooting in the room during customer calls, not gathering requirements to build later Run customer discovery

Role Purpose At Jumio, we’re building trusted identity solutions powered by machine learning and biometrics. As a Data Engineer II , you will take ownership of designing, building, and scaling data pipelines that power our ML systems. You’ll work closely with ML engineers, platform teams, and product stakeholders to enable robust dataset creation, benchmarking, and production-grade data workflows on AWS. What You’ll Do Design, build, and maintain scalable and reliable data pipelines for dataset creation, transformation, and benchmarking Own and optimize Airflow pipelines on AWS for data processing, orchestration, and evaluation workflows Write efficient, production-grade SQL and Python code for large-scale data processing and analysis Partner closely with ML engineers to enable model training, evaluation, and benchmarking pipelines Improve pipeline performance, reliability, and observability, ensuring high data quality in production Build and maintain systems to support model performance tracking and data drift monitoring Troubleshoot and resolve data issues across pipelines, ensuring minimal impact on ML workflows Contribute to data architecture decisions and best practices across the platform Collaborate cross-functionally with ML, platform, and data teams to support scalable ML infrastructure What We’re Looking For 3–5 years of experience in Data Engineering, Data Platforms, or related roles Strong proficiency in Python and SQL with experience in production systems Hands-on experience with AWS services (S3, EC2, SageMaker or similar) Solid experience building and managing Airflow (or similar orchestration tools) Strong understanding of data engineering fundamentals (ETL/ELT, data modeling, pipeline design) Experience working with large-scale datasets and distributed data systems Experience supporting ML workflows, datasets, or evaluation pipelines Strong problem-solving skills and ability to work independently in a fast-paced environment Nice to Have Experience w

The following description is not intended as an exhaustive list of all responsibilities, duties and skills required of personnel so classified. Management retains the discretion of adding or changing the duties of the position at any time. Position Overview BYD Electronics is seeking a Technical Program Manager (TPM) to support the development and delivery of thermal solutions for data center and server platforms. As an OEM supplier, this role will act as the technical interface between our engineering teams and our data center/server customers, ensuring successful product development, integration, and deployment. The TPM will coordinate cross-functional teams across thermal engineering, mechanical design, manufacturing, and customer engineering teams to drive programs from concept through mass production while meeting customer performance, quality, and schedule requirements. Key Responsibilities Customer Technical Interface Serve as the primary technical and program contact for data center/server customers. Work closely with customer engineering teams to understand system thermal requirements, server architecture, and integration constraints. Support technical discussions, design reviews, and issue resolution throughout the product lifecycle. Program Management Manage end-to-end programs for thermal solutions including heat sinks, heat pipes, vapor chambers, liquid cooling components, and related assemblies. Develop and maintain program schedules, milestones, and deliverables. Track risks, issues, and dependencies to ensure successful program execution. Cross-Functional Coordination Coordinate internal teams including thermal engineering, mechanical design, manufacturing, quality, and supply chain. Drive alignment between product design, testing, and manufacturing readiness. Facilitate cross-functional communication to resolve technical and program challenges. Product Development & NPI Support product development phases including design validation, prototy

About the Team Training Runtime designs the core distributed runtime that powers everything from early research experiments to frontier-scale model runs. We build robust, scalable, high performance components to support our distributed training workloads. Our priorities are to maximize the productivity of our researchers and our hardware, accelerating progress towards AGI. Within Training Runtime, the Runtime Foundations team develops the distributed OS responsible for orchestrating the large numbers of processes that make up modern training workloads and providing all necessary observability and resilience to operate in frontier scale environments. Our runtime sits beneath training frameworks and on top of research infrastructure, ensuring jobs run reliably across massive clusters while maintaining performance, stability, and observability. Success for us is measured by both system reliability and researcher velocity - enabling ideas to scale from experiments to production training runs. About the Role As a Training Runtime: Runtime Foundations Engineer , you will work on the software that ties thousands of computers together and exposes them as a unified system. This system has to serve individual researchers running multiple parallel experiments, as well as our largest training runs spanning 100’s of thousands and even millions of machines and accelerators. This requires easy to use, introspectable systems that can promote a fast debugging and development cycle, as well as relentless optimization for scale while maintaining stability and performance throughout. You will work primarily in Codex + Rust , building high-performance asynchronous systems with a strong emphasis on performance, correctness, resilience, and scalability. Working at this scale and at the frontier of AI development poses novel challenges. We support many different types of research, and the problems you will be working on are highly ambiguous and require a superb ability to jump into novel d

You will lead a small, hands-on engineering team building the secure, scalable Core Analytics Data Access Platform that accelerates Datadog’s Applied AI and analytics capabilities. The team owns the Data Access Platform — a unified interface that lets AI and analytics teams discover and self-serve production-ready datasets while abstracting underlying systems and embedding required legal and compliance guardrails. In this role you’ll own technical direction, contribute to design and code, and partner closely with Applied AI, Product Analytics, and internal platform teams to provide reliable datasets and APIs for model training and analysis. This role balances day-to-day engineering leadership with long-term platform planning. 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 a Hands-On Engineering Team: Manage, mentor, and grow a small team of 2–4 data engineers (mix of senior and junior) across Paris and NYC, fostering technical excellence and career development. Own Technical Direction and Delivery: Define architecture, engineering priorities, and the team roadmap for the Data Access Platform, driving implementation of scalable, secure data pipelines and platform services. Contribute to Design and Code: Spend substantial time coding, reviewing, and shipping critical platform components to ensure performance, reliability, and operational excellence. Partner with Internal Stakeholders: Work closely with Applied AI, Internal Product Analytics, product managers, and platform teams to define data contracts, APIs, SLAs, observability, and curated analytical datasets. Ensure Data Security, Governance, and Reliability: Implement access controls, lineage, monitoring, and compliance guardrails to support safe model training and repeatable analytics workflows.