Machine Learning Engineer Jobs

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Advance inference efficiency end-to-end by designing and prototyping algorithms, architectures, and scheduling strategies for low-latency, high-throughput inference. Implement and maintain changes in high-performance inference engines such as SGLang- or vLLM-style systems and Together's inference stack, including kernel backends, speculative decoding (e.g., ATLAS), and quantization. Profile and optimize performance across GPU, networking, and memory layers to improve latency, throughput, and cost. Design and operate reinforcement learning (RL) and post-training pipelines (including RLHF, RLAIF, GRPO, DPO-style methods, and reward modeling) where most of the cost is inference, jointly optimizing algorithms and systems. Make RL and post-training workloads more efficient with inference-aware training loops such as asynchronous RL rollouts and speculative decoding techniques. Use these pipelines to train, evaluate, and iterate on frontier models based on the inference stack. Co-design algorithms and infrastructure to tightly couple objectives, rollout collection, and evaluation with efficient inference, identifying bottlenecks in training engines, inference engines, data pipelines, and user-facing layers. Run ablations and scale-up experiments to study trade-offs between model quality, latency, throughput, and cost and integrate findings into model, RL, and system design. Profile, debug, and optimize inference and post-training services under production workloads. Drive roadmap items requiring real engine modifications, including changing kernels, memory layouts, scheduling logic, and APIs. Establish metrics, benchmarks, and experimentation frameworks to rigorously validate improvements. Provide technical leadership by setting technical direction for cross-team efforts at the intersection of inference, RL, and post-training. Mentor engineers and researchers on full-stack ML systems work and performance engineering.

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Optimize orchestration processes to ensure efficient deployment and management of AI models. Implement cost-saving strategies to minimize infrastructure expenses while maximizing performance. Upgrade throughput to enhance scalability and responsiveness of AI systems. Collaborate with cross-functional teams to identify bottlenecks and implement solutions to improve workflow efficiency. Ship new features and updates rapidly while maintaining high levels of quality and reliability. Deploy and monitor machine learning models produced by deep learning engineers. Design, deploy, and maintain performant and scalable processes for data acquisition and manipulation to enhance dataset accessibility. Participate actively in the team's software development process, including design reviews, code reviews, and brainstorming sessions. Maintain accurate and updated software development documentation.

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Lead the design and implementation of scalable ML/AI systems focused on large language models, vector databases, and retrieval-based architectures. Integrate and apply foundation models from providers like OpenAI, AWS Bedrock, and Anthropic for prototyping and production use cases. Adapt, evaluate, and optimize large language models for domain-specific enterprise applications. Build and maintain infrastructure for AI model experimentation, deployment, and monitoring in production. Improve model performance and inference workflows addressing latency, cost, and reliability. Provide technical leadership by mentoring engineers and promoting best ML engineering practices. Partner with product and cross-functional stakeholders to translate requirements into scalable ML solutions. Contribute to the evolution of internal standards for AI experimentation, evaluation, and deployment. Lead the design and delivery of end-to-end voice AI solutions combining large language models with speech technologies including speech-to-text, text-to-speech, and real-time streaming audio pipelines, architecting low-latency, highly reliable conversational voice systems and guiding a team through ambiguity toward production excellence. Understand and apply constraints of voice experiences such as latency, turn-taking, interruption handling, streaming inference, and audio quality to create scalable, enterprise-grade systems.

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As a Forward Deployed ML Engineer at Modal, you will work hands-on with companies like Suno, Lovable, Cognition, and Meta to architect and optimize production AI workloads on Modal. You will contribute to open-source projects, publish technical content demonstrating Modal's capabilities across the AI stack, and collaborate with Modal's product and sales teams as both an engineer and a product stakeholder. Additionally, you will build trusted relationships with technical leaders at companies doing frontier AI work and conduct technical demos, experiments, and proof-of-concepts that highlight Modal's performance advantages.

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Advance inference efficiency end-to-end by designing and prototyping algorithms, architectures, and scheduling strategies for low-latency, high-throughput inference. Implement and maintain changes in high-performance inference engines including kernel backends, speculative decoding, and quantization. Profile and optimize performance across GPU, networking, and memory layers to improve latency, throughput, and cost. Design and operate RL and post-training pipelines optimizing algorithms and systems where most cost is inference. Make RL and post-training workloads more efficient with inference-aware training loops, async RL rollouts, speculative decoding, and other techniques to reduce rollout collection and evaluation costs. Use these pipelines to train, evaluate, and iterate on frontier models. Co-design algorithms and infrastructure tightly coupling objectives, rollout collection, and evaluation to efficient inference, and identify bottlenecks across training engine, inference engine, data pipeline, and user-facing layers. Run experiments to understand trade-offs between model quality, latency, throughput, and cost, feeding insights back into design. Profile, debug, and optimize inference and post-training services under production workloads. Drive roadmap items requiring engine modifications such as kernel, memory layout, scheduling logic, and API changes. Establish metrics, benchmarks, and experimentation frameworks for rigorous validation of improvements. Provide technical leadership by setting technical direction for cross-team efforts in inference, RL, and post-training; mentor engineers and researchers on full-stack ML systems and performance engineering.

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The Staff Software Engineer for the Model LifeCycle team is responsible for building a comprehensive managed platform for the entire application development lifecycle focused on Machine Learning models, including Large Language Models (LLMs). Responsibilities include contributing to fine-tuning systems for large foundation models, including multi-node orchestration, checkpointing, failure recovery, and cost-efficient scaling, implementing and maintaining end-to-end training pipelines for LLMs, contributing to distillation and reinforcement learning pipelines, developing and maintaining agent execution infrastructure, and implementing features for dataset, model, and experiment management such as versioning, lineage, evaluation, and reproducible fine-tuning at scale. The role involves close collaboration with Principal Engineers, product, business, and platform teams to implement core abstractions and APIs, contributing to architectural decisions around training runtimes, scheduling, storage, and model lifecycle management, and engaging with the open-source LLM ecosystem. The role offers significant scope for ownership in implementing and contributing to the design of core systems.

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Engineer the systems required to train foundation models at scale with the objective to maximize hardware utilization and training throughput on large-scale GPU clusters. Profile training loops using tools such as PyTorch Profiler, Nsight Systems, and Nsight Compute to identify system- and kernel-level bottlenecks and maximize model throughput. Configure and tune composite parallelism strategies, optimizing load balance, minimizing critical-path bottlenecks, and managing communication-to-computation trade-offs for large-scale large language model training. Partner with AI Researchers to define model architectures for hardware efficiency without compromising convergence.

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As a modeling lead for the AI lab, you will be responsible for defining the technical roadmap for the team and supporting the modeling needs across the organization. You will define and establish best practices to manage the model life cycle from data acquisition to deployment, and build tools and platforms to facilitate building and deploying ML models on different devices with specific constraints. You will work closely with different teams across the organization to support their modeling needs, translating high level user needs to specific modeling requirements, creating plans, and technically driving the team to execute on those. You will define and drive the AI Lab technical strategy in support of HP’s AI roadmap, owning decisions across models, runtimes, inference engines, and optimization. You will lead on device AI strategy, including model compression, quantization, distillation, and hardware aware optimization across CPUs, GPUs, NPUs, and TPUs. You will architect and evolve tooling and platforms that support the full model lifecycle from data and training through evaluation, deployment, and monitoring. You will establish standards and evaluation frameworks to ensure high quality, safe, and performant Gen AI models in production. You will partner closely with cross functional leaders and teams to align technical direction with product and hardware strategy. Additionally, you will mentor a small group of senior engineers while operating as a hands on technical leader who sets direction and moves quickly.

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The Lead ML Engineer is responsible for architecting conversational agents that maintain long-running coherent dialogues and handle complex reasoning tasks. They develop low-latency retrieval augmented generation (RAG) pipelines that ground LLM responses in proprietary data to ensure accuracy and minimize hallucinations. The role leads the development of NLP pipelines to extract structured insights from various unstructured data sources, implements advanced personalization layers that adapt model behavior based on user history and context, and owns the deployment lifecycle of models including prompt architecture, evaluation frameworks, latency optimization, and cost management on Vertex AI. Additionally, the engineer acts as a technical mentor by reviewing code, setting architectural standards, and guiding technical decisions without managing personnel.

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The role requires engineers with experience training production machine learning models and the contribution to open-source projects as well as evolving Modal's infrastructure to train the next generation of language models.