Production Scheduler Enhancement Engineer Jobs

About the role The Forward Deployed Solutions Engineer will work directly within a business domain (e.g., Commercial, Clinical Operations, Lab Operations, Sales & Marketing, Customer Experience etc.). In your role, you’ll find opportunities for enhancing efficiency and productivity by looking for workflows which can be executed 10–100x faster or more often than a human team could using AI agents, integrations and other patterns. You will build, deploy, and run them in production. You report into the central AI & Automation team, partner directly with domain leadership on priorities, and bring patterns back so the whole company compounds. Find the leverage in your domain Map the workflows in your domain — the ones running today, and the ones that don’t exist yet because they weren’t feasible without agents or automation tools. Identify the step-change opportunities: where AI, ML, or automation unlock throughput, coverage, or speed. Build the business case, quantify projected impact, and align with domain leadership on priorities. Design the future-state workflow Map structured and unstructured data flows across the systems involved (CRM, ERP, ticketing, document stores, internal tools, external SaaS). Define the target workflow: what the agent does, what the human does, and where they hand off. Figure out what context the agent or model needs to do the work well — and how to get it there reliably (retrieval, grounding, tool access, memory). Design human-in-the-loop checkpoints so review adds value without becoming the bottleneck. Build and connect the systems Stand up agents and automation pipelines using the organization’s approved AI platforms and frameworks. Connect agents to business systems — via MCP servers, APIs, webhooks, CLIs, and skills — within the guardrails set by IT and security. Configure tools, prompts, context, and retrieval pipelines so agents perform reliably on real work, not just in demos. Handle integration gnarliness: auth, schema

About the Team OpenAI’s mission is to ensure that artificial general intelligence (AGI) benefits all of humanity. A key part of achieving that mission is training models that deeply understand and reflect human preferences — the Human Data team is at the heart of that effort. The Human Data engineering team creates the systems that enable scalable, high-quality human feedback. These systems are essential to how OpenAI trains and improves its most advanced models. Engineers on this team collaborate closely with world-class researchers to bring alignment techniques to life — from experimental ideas to production-ready feedback loops. About the Role We’re looking for software engineers to join the Human Data team and build the platforms, prototypes, tools, and infrastructure that power how our AI models are trained, aligned, and evaluated. You’ll partner with researchers and cross-functional teams to bring alignment ideas to life, influence future model training, and shape how models interact with the real world. We’re looking for people who are excited by technical ownership, enjoy working across the stack, and are eager to solve ambiguous problems in a high-impact, fast-paced environment. 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: Build and maintain robust full-stack systems for feedback collection, data labeling, and evaluation pipelines, while maintaining high levels of security. Translate experimental alignment research into scalable production infrastructure, including inference and model training stacks. Design and iterate on user-facing tools and backend services to support high-quality data workflows Partner with researchers, engineers, and program leads to shape feedback loops and model interaction paradigms Drive infrastructure improvements that enable faster iteration and scaling across OpenAI’s frontier models, from internal r

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 looking for a customer-obsessed software engineer to come ship with us. You’ll own features like multi-node training and products like serverless reinforcement learning (RL) from conception to MVP (and from MVP to GA!). You’ll work through the stack, architecting solutions from API and UI down to our infrastructure layer. You’ll fine tune models yourself to develop an understanding of user workflows. You’ll work closely with research engineers leveraging state-of-the-art training techniques to build experiences that accelerate model development and solve for real pain points. If you’re excited to dive deep into the training, let’s talk! THE PRODUCT Take a look at what we’ve built so far: Overview of the product so far Training docs overview Story of the Training product Research we've done EXAMPLE INITIATIVES Checkpointing Pipeline: Our checkpointing pipeline starts with automated checkpointing, a feature that ensures that versions of models created during training are automatically backed up to the cloud. Users are able to then deploy checkpoints seamlessly into inference servers, providing point-and-click integrations into inference frameworks like vLLM and Baseten’s Inference Stack. This enables customers to quickly evaluate the performance of their checkpoints with real traffic. Multinode training: Multinode training enables customers to easily run training jobs across multiple compute nodes, enabling

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. Job Summary: A project engineer in process engineering department will be in charge of monitoring and controlling all BYD North America manufacturing processes involved in pilot transit bus/school bus/truck of each order to ensure the project goal is met. Duties/Responsibilities: Join Pre Production Meeting (PPM) review readiness of staring a new production order in terms of production document, material list, special customer requirements in technical spec, etc. Monitor progress of each project on production floor, record how production plan was impacted by the major issues. Collaborating with other departments and stakeholders to ensure quick solutions are provided to fix production issues, record owner and progress of solving the issues. At the end of each production project create a project closure report, summarize root causes of all issues that can be avoided for future projects. Other tasks assigned by superior. Required Skills/Abilities: Excellent verbal and written communication skills. Excellent interpersonal and customer service skills. Excellent organizational skills and attention to detail. Excellent time management skills with a proven ability to meet deadlines. Strong analytical and problem-solving skills. Strong supervisory and leadership skills. Ability to prioritize tasks and to delegate them when appropriate. Ability to function well in a high-paced and at times stressful environment. Proficient with Microsoft Office Suite or related software . Education and Experience: Bachelor’s degree in mechanical/electrical engineering with 1 year engineering job experience or high school degree with 3 years’ engineering job experience. Physical Requirements: Prolonged periods of sitting at a desk and working

About the Team The AI Architect team partners with organizations to turn OpenAI's most capable models into meaningful, real-world impact. We work with customers across industries and digital-native businesses to identify where AI can create value, design secure and scalable solutions, and help those solutions move from early exploration into sustained production adoption. The team brings together technical strategy, customer partnership, and practical deployment expertise, working closely with Sales, Product, Engineering, Research, and specialist delivery teams. About the Role As an AI Architect, you will be the senior technical owner for a named portfolio of customers and the primary technical counterpart to their leadership teams. You will act as the “CTO of your book of business”, shaping each customer's AI strategy and guiding their journey from pre-sales discovery and solution evaluation through deployment, adoption, and measurable business impact. You will own the technical account plan across ChatGPT Enterprise, the OpenAI API, Codex, and other agentic AI solutions. In partnership with the Account Director, you will translate business priorities into a focused use-case portfolio, an actionable adoption roadmap, and a clear path to durable customer value and growth. The Account Director owns commercial strategy; you own the technical strategy, customer journey, and path to production value. You will remain accountable for the technical outcome while bringing in the right specialists across deployment, implementation, enablement, security, product, and partners to provide deeper expertise and execute work where needed. This role calls for strong industry fluency, sound architectural judgment, and the ability to move confidently between executive strategy and hands-on technical conversations. In this role, you will: Serve as the primary technical advisor and long-term technical relationship owner for a named portfolio of existing customers and pre-sales prospect

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’re looking for signal integrity (SI) system design engineers who have a deep expertise in the SI area, and hold strong system level design knowledge 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 system signal integrity (SI) design for AI supercomputer product in the data center application. Collaborate with chip, package, boards, rack and system engineers, design partners to drive system SI design and develop innovative interconnect and high-speed technologies Identify and evaluate new technologies and methodologies to improve signal and power integrity in product design, and contribute to the development of new products and technology by providing expertise in signal integrity Perform simulation and modeling to identify and troubleshoot signal integrity issues Lead system interconnect design, bring up and qualification As the scope of the role and team grows, understand and influence roadmaps for hardware partners for our datacenter networks, racks, and buildings. You might thrive in this role if you: Have at least 10 years of industry experience, including experience design hardware system and SerDes testing for data center applications Have a strong bias toward action, and won’t take no for an answer. Have experience and good knowledge of system design experience in the SI areas, from chip, SerDes, board, rack level Have ex

About the Team: A well-established quantitative portfolio management team at Point72 is looking for an experienced quantitative professional to develop and trade systematic macro strategies, with a focus on market microstructure. The candidate will be given the resources and support to drive the build out and expansion of the quantitative macro business. Role/Responsibilities: Perform rigorous and innovative research to develop systematic signals for global macro (futures, FX, etc.) markets, with a focus on market microstructure signals Perform feature engineering with order book tick data at intraday to daily horizons Perform feature combination using various modeling techniques ranging from linear to machine learning models Participate in the research pipeline end-to-end, including signal idea generation, data processing, modeling, strategy backtesting, and production implementation Help drive the growth of the investment process and research capabilities of the team Work in a team of highly qualified and motivated individuals with access to a cutting-edge research and trading infrastructure and clean datasets Assist in building, maintenance, and continual improvement of production and trading environments Requirements: MS or PhD in physics, engineering, statistics, applied math, quantitative finance, or other quantitative fields with a strong foundation in statistics 4+ years of experience in quantitative research, building statistical models for intraday to daily trading, as part of a successful proprietary trading team with a track record Knowledge of market microstructure for futures and/or FX Prior experience with tick data based feature generation, modelling, and monetization Demonstrated proficiency in Python, R, or C/C++. Familiarly with data science toolkits, such as scikit-learn, Pandas Collaborative mindset with strong independent research abilities Commitment to the highest ethical standards

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the Role We are looking for a Threat Intelligence Engineer to join our Threat Intelligence team. In this role, you will build the infrastructure that powers our threat discovery capabilities—integrating external data sources, developing detection systems for automated lead generation, and creating internal tooling that scales our investigators' impact. This is a foundational engineering role on a small, high-impact team. You will take projects from proof-of-concept to production, work closely with investigators to understand their needs, and help scale what may become a multi-person collections function. Responsibilities: Build automated detection systems that use disparate signals to identify abusive behavior. Take systems from idea to proof-of-concept to production-grade with appropriate monitoring, documentation, and maintenance processes Develop and maintain YARA rule infrastructure, including tools for writing, validating, and testing rules against real data Create integrations with external threat intelligence platforms (e.g. VirusTotal, Censys, Urlscan) via MCP servers to enable multi-source correlation during investigations Build data pipelines that ingest intelligence from RSS feeds, CTI news sources, and partner sharing, using Claude to extract TTPs and generate targeted hunting queries Develop behavioral analytics capabilities using DBT-based frameworks and create searchable audit logging infrastructure Establish feedback loops with investigators to tune detection systems and reduce false positives Scrape and normalize data from external sources to feed threat detection and enrichment workflows You may be a good

Senior Data Scientist Location: London (Hybrid) | Practice Area: Data & Analytics | Type: Permanent Shape intelligent solutions. Lead with insight. Drive data innovation. The Role We are looking for a Senior Data Scientist to join Capco’s growing UK Data Practice. You will play a leading role in designing and implementing cutting-edge data science solutions across financial services. This is an opportunity to build intelligent systems that drive commercial and customer outcomes while mentoring others and collaborating in a dynamic, multi-disciplinary environment. You'll also embrace AI-enabled ways of working, using approved AI tools where appropriate to enhance delivery while applying sound professional judgement. What You’ll Do Lead the end-to-end delivery of data science solutions including PoCs, MVPs and production deployments. Develop and prototype ML models to solve complex business challenges using modern techniques and tooling. Collaborate closely with engineers, domain experts, and business teams to translate requirements into deliverables, using approved AI tools where appropriate to improve efficiency and accelerate insight. Guide and coach data science pods, supporting skill development and solution design. Act as a subject matter expert on ML architecture, model calibration, and productionisation, promoting responsible AI practices and scalable model governance where applicable. What We’re Looking For Hands-on experience building and deploying data science solutions in Python and related ML libraries. Strong background in applied machine learning, model development, and data engineering, with an appreciation of responsible AI principles. Experience with cloud environments (Azure, AWS, GCP) and tools such as Spark, Hive, and Redshift. Demonstrated ability to lead cross-functional teams and mentor junior practitioners. Ability to communicate complex technical concepts clearly to non-technical audiences and apply AI-enabled approaches to deliver practi

Software is eating the world, but AI is eating software. We live in unprecedented times – AI has the potential to exponentially augment human intelligence. Every person will have a personal tutor, coach, assistant, personal shopper, travel guide, and therapist throughout life. As the world adjusts to this new reality, leading platform companies are scrambling to build LLMs at billion scale, while large enterprises figure out how to add it to their products. To make them safe, aligned and actually useful, these models need human evaluation and reinforcement learning through human feedback (RLHF) during pre-training, fine-tuning, and production evaluations. This is the main innovation that’s enabled ChatGPT to get such a large headstart among competition. At Scale, our products include the Generative AI Data Engine, SGP, Donovan, and others that power the most advanced LLMs and generative models in the world through world-class RLHF, human data generation, model evaluation, safety, and alignment. The data we are producing is some of the most important work for how humanity will interact with AI. At the foundation of these products is the Platform Engineering team. In this role, you will lead the design and development of core data storage, streaming, caching, and indexing platforms and underlying systems. You’ll also get widespread exposure to the forefront of the AI race as Scale sees it in enterprises, startups, governments, and large tech companies. You will: Drive the architecture, design, implementation, and reliability of our foundational data platforms and systems, working closely with stakeholders and internal customers to understand and refine requirements. Collaborate with cross-functional teams to define, design, and deliver new features. Proactively identify opportunities for, and driving improvements to, current programming practices, including process enhancements and tool upgrades. Present technical information to teams and stakeholders, providing

About the Team The Safety Systems org is responsible for various safety work to ensure our best models can be safely deployed to the real world to benefit the society and is at the forefront of OpenAI's mission to build and deploy safe AGI, driving our commitment to AI safety and fostering a culture of trust and transparency. The Safety Engineering team builds the platforms and tools that make OpenAI’s models safe to use in the real world. We partner closely with researchers, product teams, and policy to turn safety ideas into reliable, scalable systems: measuring risk, enforcing safeguards, and continuously improving how models behave in production. Our work sits at the intersection of product engineering, data, and AI, and directly shapes how millions of people experience OpenAI’s technology. About the Role We’re looking for a self-starter engineer who loves building products in an iterative, fast-moving environment—especially internal tools that unlock real-world impact. In this role, you’ll build full-stack tooling for our Safety Systems teams that directly improves the safety and reliability of OpenAI’s models, including in sensitive areas like mental health and other vulnerable-user protections. Your work will increase the team’s velocity in identifying and fixing safety issues and help tighten the feedback loop between policy, data, and the model training cycle. In this role, you will: Own the end-to-end development of internal tools that help improve the safety of OpenAI’s models (with a focus on areas like mental health and other vulnerable-user protections) Partner closely with Safety Systems researchers, engineers, and model policy creators to understand workflows, pain points, and requirements—and translate them into durable product solutions Build full-stack experiences to support core model policy workflows, such as labeling and inspecting data, analyzing and reviewing failure cases, and surfacing insights for iteration Optimize internal applications f

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role We are looking for research engineers to build the safety and oversight mechanisms that govern how our models handle biological knowledge. As a bio safety researcher, you will spend your time: designing and running capability evaluations against frontier models, generating and curating training data for our safety classifiers, training and iterating on those classifiers alongside our ML engineers, and measuring how they hold up against adversarial pressure in production traffic. This work sits at the intersection of applied ML and biosecurity. You will help define what responsible AI safety looks like in the biological domain, translating threat models into evals, datasets, and deployed systems. The core technical tension you will own is precision: safeguards need to be robust against sophisticated actors while staying out of the way of the far larger population of legitimate researchers using Claude to accelerate life sciences work. Getting that tradeoff right is an empirical problem, and you will be the person measuring it. You do not need to be an ML researcher today. We are looking for strong scientific programmers with real depth in modern biology who want to bring that depth to bear on model evaluation and classifier development. Key responsibilities Design, build, and run capability evaluations to assess what new models can do in the biological domain, and turn results into concrete deployment recommendations Develop training and evaluation datasets for our safety classifiers, working with internal and external threat modeling experts to ground them in realistic risk Train, tune, and iterate on safety classifiers

About Us: AI needs a new infrastructure layer. We're building it at Modal. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now. Our customers include category-defining companies like Lovable , Ramp , Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September. Our team includes creators of popular open-source projects (e.g., Seaborn , Luig i ), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience. The Role: Modal is seeking an experienced Forward Deployed Engineer (FDE) to partner with our sales team and drive technical sales success. As an FDE, you will be the technical voice in our sales process, working directly with Account Executives to help enterprise customers understand how Modal can transform their AI/ML infrastructure. You will: Partner with Account Executives to identify, qualify, and close strategic enterprise opportunities Lead technical discovery sessions with prospective customers to understand their current infrastructure, pain points, and requirements Design and present compelling technical solutions that demonstrate how Modal addresses customer needs Architect migration paths from existing cloud infrastructure (AWS, GCP, Azure) to Modal's serverless platform Conduct technical demos, experiments, and proof-of-concepts that showcase Modal's capabilitie

About Us: AI needs a new infrastructure layer. We're building it at Modal. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now. Our customers include category-defining companies like Ramp , Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We've passed $1B in annualized run rate, up 15x from a year ago. In October 2026 we raised a $750M Series D led by Accel at a $15.75B valuation. Our team includes creators of popular open-source projects (e.g., Seaborn , Luigi ), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience. The Role: We are looking for strong engineers with experience and interest in designing, building, and maintaining the novel, high-performance systems that make up our serverless platform. Requirements: 5+ years of experience writing high-quality production code Experience building high-performance distributed systems at a large scale (the more battle scars, the better) Strong cloud skills Strong knowledge of low-level operating system foundations (Linux kernel, file systems, containers, etc.) Experience with performance engineering (tell us a story of when you shaved off a few milliseconds!) Ability to work in-person in our Stockholm office. Prior experience with Rust is nice to have, but not required. Ability to participate in on-call rotation and respond to production incidents.

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the Role We're looking for a Software Engineer to join the team building Claude Security. In this role, you'll take new security capabilities in frontier models and turn them into products that customers can rely on, working across the full stack from early prototype through production. This role sits close to both research and go-to-market. You'll work with researchers on the team to understand what the models can do and where they fall short, and with customers and partners to understand which problems are worth solving for them. You'll be expected to form your own view from both and to own significant pieces of the roadmap, including deciding what not to pursue. The right candidate is a strong generalist engineer with the technical depth to engage with research, the product instincts to know what's worth building, and the drive to move fast. Prior security experience is useful but not required; range across the stack and good judgment about what to build matter more. Responsibilities Build and ship user-facing and/or backend features for Claude Security Build the agent loops, tool integrations, and enabling infrastructure for cybersecurity products Collaborate tightly with product managers and go-to-market to understand user needs, shape feature scope, and make informed tradeoffs as you build Iterate quickly based on user feedback and internal testing, improving the experience on a continuous basis Collaborate with research teams to identify and develop new model capabilities for security applications You may be a good fit if you: Contribute across the stack, from front-end UI to back-end infrastructure Are proficient in Type