Product Manager, Models
As the Product Manager for Heidi's models platform, you will own the product strategy and roadmap for the platform including evaluation pipelines, fine-tuning infrastructure, model routing, and safety systems. Your responsibilities include prioritising your team's work across enablement requests, model safety and quality, and new capability bets; fixing platform issues that cause blocks for product teams; building evaluation tooling and fine-tuning workflows usable in clinical settings; deciding improvements based on clinician feedback, model quality signals, and product team needs; allocating engineering capacity among competing requests and clearly communicating deferrals; working with engineers on evaluation design, fine-tuning trade-offs, and model architecture decisions; setting model quality and safety targets based on clinical outcomes; consolidating duplicate infrastructure across product teams; and monitoring foundation model developments to adjust the roadmap accordingly. You will collaborate closely with engineers, researchers, product PMs, and clinical safety teams and report to product leadership. This is a platform role whose outputs impact every user-facing product at Heidi.
Forward Deployed Engineer, Agentic Platform
Build and ship features for North, an AI workspace platform; develop autonomous agents that interact with sensitive enterprise data; experiment rapidly and with high quality to engage customers and deliver solutions that exceed expectations; work across the entire product lifecycle from conceptualization through production; lead end-to-end deployment of North in private cloud and on-premises environments including planning, configuration, testing, and rollout.
Forward Deployed Engineer - ML
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.
Research Product Manager — Structured AI Systems
The Research Product Manager is responsible for advancing foundational work in tabular data learning, structured and relational representation learning, compression-aware AI, hybrid symbolic, relational, and neural systems, and large-scale systems, linking these research efforts to real production systems managing petabytes of data. The role involves productionizing structured AI models by collaborating with Research and Systems teams to design training on Parquet/Iceberg/Delta data, define training infrastructure requirements, inference architectures, and maintenance loops, while understanding storage and compute trade-offs, data layout, compute scheduling, model lifecycle, infrastructure bottlenecks, and evaluation pipelines. The role also involves defining economic value extraction by identifying buyers, economic value sources, quantification methods, and converting research advances into revenue and platform advantages, requiring strong enterprise infrastructure economic intuition. Additionally, the Research Product Manager identifies viable modeling advances for production, terminates non-viable research directions, defines integration paths into enterprise workloads, and works with the Chief Research Scientist on research agenda prioritization. The position requires deep understanding of large AI model training, deployment, and maintenance in production systems, as well as translating foundational modeling advances into economically valuable infrastructure, shaping technical execution and economic strategy.
Solutions Engineer (Autonomous Vehicles & Robotics)
As a Solutions Engineer at Encord, you will be the core technical expert for customers building autonomous vehicles, robotics, and physical AI solutions, specializing in LiDAR data, sensor fusion, and perception. Your responsibilities include leading technical discovery with perception teams to understand their sensor stacks, model development pipelines, and data challenges; architecting complete solutions for complex multimodal datasets including LiDAR, camera, and radar fusion, and sensor calibration; acting as the technical authority on handling 3D point clouds, sensor fusion, temporal sequences, and multimodal annotation; building bespoke proofs of concept for LiDAR data ingestion, point cloud processing, coordinate transformations, and sensor calibration; developing custom integrations with robotics/AV stacks such as MCAP, ROS, Apollo, and Autoware; creating technical demos for LiDAR annotation, 3D bounding boxes, semantic segmentation, and multi-sensor fusion; debugging complex issues involving point cloud rendering, sensor calibration matrices, and multimodal data synchronization; guiding prospects through technical evaluations of LiDAR formats, sensor configurations, and annotation requirements; providing expert consultation on 3D annotation best practices, coordinate conventions, and quality control workflows; partnering with Account Executives to co-own technical wins in enterprise sales cycles; translating technical capabilities into business value for CTOs and senior stakeholders; and channeling customer feedback to Product and Engineering teams to shape the product roadmap.
Senior Fullstack Software Engineer
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.
Senior Program Manager, Infrastructure Strategy and Business Operations
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.
Helix AI Engineer, Agentic Systems
Design, deploy, and maintain Figure's training clusters. Architect and maintain scalable deep learning frameworks for training on massive robot datasets. Work together with AI researchers to implement training of new model architectures at a large scale. Implement distributed training and parallelization strategies to reduce model development cycles. Implement tooling for data processing, model experimentation, and continuous integration.
Software Engineer, Backend
Design, build, and own backend systems end-to-end, including services, APIs, data pipelines, and infrastructure that power the products. Solve complex technical challenges across distributed systems, scaling, concurrency, and performance. Integrate and operate large generative AI models in production by deploying, serving, and scaling systems that combine internal research and external capabilities to unlock new product experiences. Instrument, experiment, and iterate in production to continuously improve system and product quality. Design and operate core platform infrastructure, including integrations with third-party providers, storage systems, security, and internal APIs.
Lazo - Head of Engineering
The Head of Engineering at Lazo is responsible for owning the technology strategy and roadmap aligned with business and product OKRs, defining the reference architecture for agentic systems including LLMs and tool orchestration, establishing security and compliance baselines such as IAM, data privacy, and SOC2-readiness, and managing cost governance (FinOps). They present trade-offs, risks, and progress in leadership reviews. The role involves hands-on engineering and delivery, including shipping backend services in Python/TypeScript, orchestrating agents and toolchains, integrating external APIs and databases, building robust pipelines, and handling end-to-end DevOps using AWS/GCP, containerization, IaC, CI/CD, and observability, as well as on-call design. They work to reduce technical debt, improve latency and throughput, and manage infrastructure cost per workflow/client. Responsibilities also include defining SLOs and error budgets to reduce MTTR and change-fail rates, implementing data access policies and secure data flows for AI features, driving post-mortems and preventive engineering, hiring and mentoring engineers, setting performance scorecards, fostering a culture of thoughtful trade-offs and fast feedback loops, partnering with Product and AI teams for scalable solutions, collaborating with Ops, Growth, and Customer teams for reliability and launch readiness, and managing key vendors and build-versus-buy decisions with ROI narratives.
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