Inference Technical Lead Jobs

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 builds the infrastructure that lets engineers run AI workloads without the usual pain. To do this well, we need exceptional people – and that’s where you come in. As the first dedicated GTM recruiter on our Talent team, you’ll own sales, GTM, and other G&A searches end-to-end. You’ll work closely with our Head of Talent, founders, and GTM leads to shape how we hire and help bring in the people who will define what Modal becomes. What you’ll do: Drive full-cycle recruiting for key hires across GTM and G&A functions (sourcing, pitching, guiding interviews, and closing candidates) Partner with GTM leaders to understand the real work and calibrate on what great looks like Help set our hiring bar and how we evaluate talent Execute creative top-of-funnel strategies that resonate with a strong community of experienced GTM talent Deliver a fast, respectful, h

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: We're looking for a People Operations Generalist to join our growing People team. You'll touch the employee lifecycle end-to-end — from offer acceptance through offboarding — while helping to build the processes and documentation that let our People function scale with the business. This is a great fit for a highly organized, systems-oriented people person who thrives in a fast-paced environment and wants to build operational foundations, not just maintain them. What you’ll do Own and continuously improve the new hire onboarding experience, ensuring employees are set up for success and internal tasks are tracked and completed on time. Serve as a first point of contact for employee questions across the full HR spectrum, triaging and routing more complex issues to the right People team member or external partner. Maintain and improve self-service resources (FAQs, Not

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. About the Role: As a Manager, Enterprise Sales, you will lead and scale our enterprise sales team, driving strategic revenue growth with a consultative, customer-first approach. You will oversee complex deal cycles, coach Enterprise Account Executives, and build the motion that wins high-impact, multi-stakeholder deals in a rapidly evolving AI landscape. What You’ll Do Lead, mentor, and develop a team of Enterprise Account Executives, fostering a culture of performance, strategic thinking, and collaboration Own and guide the full enterprise sales cycle, from targeted outbound and discovery to multi-threaded navigation, negotiation, and close Build and refine enterprise sales playbooks, qualification frameworks, and forecasting models that increase accuracy and velocity Collaborate cross-functionally with Product, Marketing, and Engineering to align on go-to-market strategy, unblock en

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: We are looking for PhD research interns with strong research experience in reinforcement learning, machine learning, and foundation models, including large language and multimodal models, to join our research team. This internship is well suited to candidates interested in improving existing methods and developing new techniques for large-scale model training, optimization, and inference, extending models to long-context and long-horizon tasks, and improving inference-time efficiency, reliability, and robustness in high-stakes real-world deployments. Preferred Qualifications: Currently pursuing a PhD in computer science, machine learning, or a related field. A demonstrated record of research in reinforcement learning, machine learning, foundation models, or related areas. Experience developing and evaluating large-scale models or machine learning systems. Familiari

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: We're looking for a Detection & Response Engineer to build the systems that help us identify, investigate, and respond to threats across our platform. This is an engineering role focused on automation. You'll build detections, investigation tooling, and response capabilities that scale with our infrastructure, using AI where it meaningfully improves signal, investigation speed, and operational effectiveness. You'll work closely with infrastructure, platform, and security engineers to ensure every incident makes the platform more resilient. What You'll Work On: Detection Engineering Design and build high-fidelity detections for attacks, abuse, and anomalous behavior across our infrastructure and production systems Continuously improve detections based on telemetry, threat intelligence, and lessons learned from incidents Improve visibility across cloud infrastruc

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. About the Role We're seeking a Revenue Operations Manager with a strong track record, a builder's mindset, and a bias for action to join our in-person team in New York or SF. This is a high-impact, hands-on role. You'll own the entire revenue operations function, from top-of-funnel lead routing through deal close and commission administration. You'll work closely with our Head of Finance & People Ops and sales leadership to build the systems, dashboards, and processes that scale our go-to-market motion. What You'll Do: Own the lead routing process from inbound and partnering with marketing to ensure proper attribution Run effective territory management & strategy for Geo based decisioning Support & strategise every aspect of revenue operations in your territory Own the strategy for capacity forecasting, inputs, throughputs & outputs being the conduit back to finance in

About Ramp Ramp is building the smart infrastructure for finance teams, embedded in the transaction flow of every dollar a business spends. We automate how over $200B in annualized spend flows in and out of 70,000+ companies: authorizing payments, flagging risk, categorizing spend, and closing books. The problems are high-stakes, data-dense, and unforgiving. We hire people with high agency and high urgency. We look for slope over intercept. We care less about where you trained and more about what you’ve built. At Ramp, everyone is a builder who owns problems end to end and makes consequential decisions that shape the outcome. The median Ramp customer saves 5% and grows revenue 16% in their first year – far in excess of businesses operating without Ramp. We believe every ambitious company deserves the same. If you want to build systems that directly shape how companies move and manage billions, Ramp is the place to do it. About the Role The Applied AI team at Ramp is at the forefront of leveraging AI to drive innovation across our platform. We are seeking strong full-stack engineers who are proficient in web frameworks, backend development, and infrastructure. You will work on exciting projects such as AI Agents, Retrieval-Augmented Generation, Structured Extraction (we made https://github.com/1rgs/jsonformer ), internal tooling for customer-facing teams, fine-tuning models, and build infrastructure for LLM inference. If you're passionate about working on real production use cases of large language models (LLMs) and want to contribute to groundbreaking AI applications, this role is for you. What You’ll Do Ship full-stack AI projects end to end Build and integrate components for AI infrastructure, supporting production-level inference and fine-tuning Develop and improve engineering processes, tools, and systems to scale AI solutions across Ramp Create tools and internal platforms to enhance the productivity and capabilities of Ramp's AI and engineering teams What You

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).

A World-Changing Company Palantir builds the world’s leading software for data-driven decisions and operations. By bringing the right data to the people who need it, our platforms empower our partners to develop lifesaving drugs, forecast supply chain disruptions, locate missing children, and more. The Role Palantir is at the forefront of some of the most critical and challenging problems in the world. We develop alongside our customers everyday. Our customers span from the cloud to the frontline. As we adapt to solve their most pressing issues in latency, performance, and compute cost, we are building a team of software engineers relentlessly focused on low-level optimization and novel compute architectures. This is a team of developers creating software for the far-edge, including streaming ETL pipelines, inference platforms, and various timing critical applications. This role requires an experienced software engineer who is well versed in low-level development in compiled, native languages such as Rust and C/C++. A successful candidate can optimize software for constrained embedded devices or across large-scale distributed systems. You should have strong knowledge of computer architecture and OS internals.

About Us At Cloudflare, we are on a mission to help build a better Internet. Today the company runs one of the world’s largest networks that powers millions of websites and other Internet properties for customers ranging from individual bloggers to SMBs to Fortune 500 companies. Cloudflare protects and accelerates any Internet application online without adding hardware, installing software, or changing a line of code. Internet properties powered by Cloudflare all have web traffic routed through its intelligent global network, which gets smarter with every request. As a result, they see significant improvement in performance and a decrease in spam and other attacks. Cloudflare was named to Entrepreneur Magazine’s Top Company Cultures list and ranked among the World’s Most Innovative Companies by Fast Company. At Cloudflare, we’re not looking for people who wait for a polished roadmap; we’re looking for the builders who see the cracks in the Internet that everyone else has simply learned to live with. We value candidates who have the instinct to spot a "normalized" problem and the AI-native curiosity to create a solution using the latest tools. Our culture is built on iteration, leveraging AI to ship faster today to make it better tomorrow, while ensuring that every improvement, no matter how small, is shared across the team to lift everyone up. If you’re the type of person who values curiosity over bureaucracy, and that AI is a partner in solving tough problems to keep the Internet moving forward, you’ll fit right in. Available Locations: Austin, TX or San Francisco, CA About the Role AI inference is becoming core infrastructure. Every serious application will need access to many models, across many providers, with reliability, observability, security, cost control, and routing built in from the start. AI Gateway is Cloudflare’s bet that this layer should exist at the network edge: close to users, close to compute, and simple enough that a developer can adopt i

Asana's Data Science team helps us fulfill our mission by informing strategy, defining success metrics, and identifying new ways to deliver user value . Data scientists are at the crux of deepening our understanding of the customers and driving more business outcomes by leveraging experimentation, causal inference, statistical and machine learning techniques, and data storytelling . As an Analytical Engineering Manager, you lead a team of Analytical Engineers who own the data foundations for the business: the Gold layer, canonical metrics, certified dashboards, and semantic layer that make Asana's most important numbers trustworthy, and that make AI-powered self-serve through Claude and Databricks Genie actually work. You sit at the intersection of Data Engineering, Analytics, and Data Science, and you are accountable for whether business stakeholders trust the data in your team's domains and can answer their own questi ons without routing through your team. This role is based in our Warsaw office with an office-centric hybrid schedule . The standard in-office days are Monday, Tuesday, and Thursday . Most Asanas have the option to work from home on Wednesdays . Working from home on Fridays depends on the type of work you do, and your recruiter can share more about the in-office requirements . We offer a Contract of Employment (UoP) for our employees in Poland. What you’ll achieve Lead, grow, and develop a team of Analytical Engineers: Own hiring, coaching, performance, and career growth, setting a high bar for data-model quality and stakeholder trust. Own the Gold layer and semantic-layer strategy across your team's domains (e.g. PLG, marketing, revenue, NPI/AWM), taking accountability for curated data models, canonical metrics, dashboards, and Genie spaces. Treat every recurring insight as a product with an owner, a cadence, and an SLA, building a catalog of trusted, versioned data products instead of one-off rebuilds. Drive self-serve enablement by prioritizing Go

Data Scientist The Data Science team helps us fulfill our mission by informing strategy, defining success metrics, and identifying new ways to deliver user value . Data scientists are at the crux of deepening our understanding of the customers and driving more business outcomes by leveraging experimentation, causal inference, statistical and machine learning techniques, and data storytelling . We understand our company’s goals and proactively inform their direction with data so that our product can help more teams do great things. As a Data Scientist at Asana, you’ll help us ask the right questions and answer them rigorously. You’ll work closely with our Pro duct and Business teams to understand their goals and proactively inform their direction with data. You’ll keep taking on new responsibilities as you grow—from defining core metrics to building machine learning models and keeping the data flowin g in our pipelines This role is based in our Warsaw office with an office-centric hybrid schedule . The standard in-office days are Monday, Tuesday, and Thursday . Most Asanas have the option to work from home on Wednesdays . Working from home on Fridays depends on the type of work you do, and your recruiter can share more about the in-office requirements . We offer a Contract of Employment (UoP) for our employees in Poland . What you’ll achieve Design and analyze experiments to measure the impact of new product features. Investigate high-level questions like “What are the collaborative patterns of the most successful teams using Asana?” Add new metrics and aggregations to our data warehouse to make new classes of questions answerable. Build models to predict the growth trajectory of different customer segments. Partner with cross-functional stakeholders across engineering, product management, and business teams to drive data-informed decision-making. About you Bachelor's Degree in Computer Science, Math, Statistics, Engineering, a related quantitative field, or equivale

We’re looking for an experienced sourcing leader to join the Datadog Procurement team and help grow the Strategic Sourcing group. Make an impact by partnering closely with senior technology and business leaders, owning our fastest-growing AI, neocloud, and inference spend end-to-end, and continuing to prove the value that Strategic Sourcing brings to the organization. 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: Own the AI spend category end-to-end, covering foundation-model and AI APIs, AI development and productivity tools, and AI-enabled SaaS (with neocloud and inference compute as an emerging area), while developing and executing category strategies aligned to business objectives Champion AI and automation adoption across the sourcing function by identifying tools (e.g. Claude, ChatGPT) and building repeatable workflows that make sourcing faster, smarter, and more scalable Lead high-value AI vendor negotiations spanning foundation-model and API agreements, AI development and productivity tools, and AI SaaS, structuring seat- and consumption-based pricing across net-new purchases and strategic renewals, and building neocloud and GPU-capacity capability as the category grows Partner with Engineering and Finance to turn architectural and consumption trade-offs into commercial business cases, forecasts, and savings targets Build pricing and consumption models and apply FinOps discipline to quantify buying scenarios and uncover savings across a fast-moving spend base Work alongside executive and senior technology leadership as a trusted advisor on AI and neocloud investment decisions, influencing strategy and commercial trade-offs at the leadership level Track category KPIs (savings, pipeline, cycle time), monitor AI market, vendor, and pricing trends,

We’re looking for a Software Engineer 3 to help bring Voyage’s embedding models - used for semantic search, retrieval, and AI-native experiences; to the platforms and environments where customers already run their workloads, beyond first-party MongoDB Atlas. You’ll join the broader Search and AI Platform organization and collaborate closely with the engineers building Voyage’s first-party inference. Together, we’re extending that platform across cloud marketplaces, third-party inference providers, and self-managed deployments so customers get the same Voyage models, behaving consistently, wherever they choose to run them. As a Software Engineer 3, you'll focus on building the systems, tooling, and deployment workflows that power third-party model delivery. You'll own key components of how Voyage models are packaged, validated, and deployed, work across teams to ensure tight integration with the core inference platform, and contribute to delivery surfaces designed for reliability, observability, and ease of use. We are looking to speak to candidates who are based in Sydney for our hybrid working model. What you'll do Port and tune the model server that runs Voyage embedding and reranking models: improving inference performance, consistency, and runtime behavior across environments Productionize new Voyage models for delivery beyond first-party Atlas, owning the packaging, configuration, and deployment workflows that get them running on AWS, Azure, GCP and more Design correctness, correlation, and performance validation that proves third-party deployments match first-party behavior Build operability into every surface: structured logging, metrics, diagnostics, and health checks with tools like Prometheus and OpenTelemetry Debug problems that span model servers, containers, deployment configuration, and partner cloud environments Work alongside Voyage's model-serving teams, and partner with GTM, SAs, TSEs, and strategic customers on the hardest external deployments Who

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. As an Applied Scientist specializing in Machine Learning and Operations Research on this team, you will develop mathematical models and launch algorithms that power these key pricing and ETA decisions. You will leverage your skills to build ML and optimization models and productionalize pipelines that can scale to millions of calls per day while solving critical business problems that have a big impact on the marketplace and rider experience. You will get exposure to a diverse set of real-world problems across optimization, prediction, machine learning, and inference and collaborate closely with teammates and stakeholders across Pricing, from Product Managers to Engineers and Analysts. We are looking for someone who is excited about working in a fast-paced, innovative, and impactful environment, and is adept at balancing complexity and efficiency to translate real world business problems into reliable solutions, systems and decision frameworks. Responsibilities Partner with Data Scientists, Engineers, Product Managers, and Business Partners to frame problems mathematically and within the business context Write production quality code. Design, build and deploy production-grade ML and Optimization models. Able to build custom methods and tooling beyond off-the-shelf libraries. Perform data analysis and build proof-of-concepts to explore and propose ML and Optimization solutions to both new and existing problems. Evaluate machine learning systems against business goals. Collaborate with Engineers to implement algorithms in live systems and ensure the robustness of the systems Establish metrics and development measurement methodologies to monitor the health of our products, as well as the impacts on user and marketplace outcomes Drive collaboration and coordination with cross-functional teams