Docker AI Jobs

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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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Build systems that integrate with the EHRs used in American healthcare to make Heidi feel like a native capability rather than a plugin. Develop systems that simplify the complexity of US healthcare billing, compliance, and payer constraints so clinicians do not have to manage these complexities. Write clean, testable code with strong interfaces, error handling, and observability, ensuring the workflows are reliable for clinicians, operators, and downstream systems. Focus on outcomes by ensuring that the built systems help clinicians and improve practice revenue. Create agentic workflow functionalities where AI assists with extraction, reconciliation, and drafting within workflows, incorporating human review, auditability, and control. Collaborate closely in a team environment with frequent pairing and shared ownership of design and implementation. Learn about healthcare organizational operations, especially those serving US customers, to translate requirements and constraints into product improvements.

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The Director of Forward Deployed Engineering will own the Forward Deployed Engineering program end-to-end, including building the team, defining the operating model, and ensuring top strategic accounts feel prioritized. Responsibilities include building, hiring, and managing a team of software engineers and managers deployed into strategic accounts; defining staffing models, engagement structures, and capacity allocation across accounts; developing specialist pods of engineers for new verticals; setting and upholding quality standards for client deliverables, documentation, and knowledge transfer. The role also requires maintaining deep technical fluency to scope custom builds, unblock engineering decisions, and evaluate solution quality; overseeing the design and implementation of tailored workflows, retrieval systems, agent tools, and knowledge sources on Harvey's platform; and ensuring solutions are operationalized with evaluations, documentation, and user training. Additionally, the Director will identify patterns across client engagements to inform product and engineering leadership about client needs and product opportunities with specificity.

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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 and techniques for large-scale rollout collection and evaluation. Use pipelines to train, evaluate, and iterate on frontier models based on the inference stack. Co-design algorithms and infrastructure tightly coupling objectives, rollout collection, and evaluation to efficient inference and quickly identify bottlenecks across training engine, inference engine, data pipeline, and user-facing layers. Run ablations and scale-up experiments to understand trade-offs between model quality, latency, throughput, and cost, and feed insights back into model, RL, and system design. Profile, debug, and optimize inference and post-training services under real production workloads. Drive roadmap items requiring engine modifications including kernels, memory layouts, scheduling logic, and APIs. Establish metrics, benchmarks, and experimentation frameworks to rigorously validate improvements. Provide technical leadership, set technical direction for cross-team efforts intersecting inference, RL, and post-training, and mentor engineers and researchers on full-stack ML systems work and performance engineering.

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Create functional black box tests for large codebases in various source languages. Create and manage Docker environments to ensure 100% reproducible builds and test execution across different platforms. Monitor code coverage and configure automated scoring criteria to meet industry benchmark-level standards. Leverage LLMs such as Roo Code and Claude to accelerate development cycles, automate repetitive tasks, and improve overall code quality.

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Create functional black box tests for large codebases in various source languages. Create and manage Docker environments to ensure 100% reproducible builds and test execution across different platforms. Monitor code coverage and configure automated scoring criteria to meet industry benchmark-level standards. Leverage LLMs (Roo Code, Claude) to accelerate development cycles, automate repetitive tasks, and improve overall code quality.

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Create functional black box tests for large codebases in various source languages; create and manage Docker environments to ensure 100% reproducible builds and test execution across different platforms; monitor code coverage and configure automated scoring criteria to meet industry benchmark-level standards; leverage LLMs (Roo Code, Claude) to accelerate development cycles, automate repetitive tasks, and improve overall code quality.

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Create functional black box tests for large codebases in various source languages; create and manage Docker environments to ensure 100% reproducible builds and test execution across different platforms; monitor code coverage and configure automated scoring criteria to meet industry benchmark-level standards; leverage large language models (LLMs) such as Roo Code and Claude to accelerate development cycles, automate repetitive tasks, and improve overall code quality.

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Create functional black box tests for large codebases in various source languages. Create and manage Docker environments to ensure 100% reproducible builds and test execution across different platforms. Monitor code coverage and configure automated scoring criteria to meet industry benchmark-level standards. Leverage LLMs (Roo Code, Claude) to accelerate development cycles, automate repetitive tasks, and improve overall code quality.

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Create functional black box tests for large codebases in various source languages; create and manage Docker environments to ensure 100% reproducible builds and test execution across different platforms; monitor code coverage and configure automated scoring criteria to meet industry benchmark-level standards; leverage large language models (LLMs) such as Roo Code and Claude to accelerate development cycles, automate repetitive tasks, and improve overall code quality.