Jobs at Raydar

Work directly with client teams to understand operational processes, translate ambiguous business challenges into automated workflows, and build production-ready software using Python, SQL, APIs, and large language models. Responsibilities include prototyping, requirements discovery, prompt design, autonomous components, evaluation, user enablement, executive communication, and monitoring measurable outcomes after launch.

Build scalable data pipelines that transform raw technical and business materials into structured training tasks and evaluations for machine learning systems. Collaborate with researchers and subject-matter experts to define requirements, review samples, develop quality checks, identify flawed or gameable tasks, and investigate delivery issues to improve future datasets.

Win large enterprise software contracts by building target-account pipelines, sourcing opportunities, leading complex multi-stakeholder sales cycles, developing customer-specific financial cases, navigating approvals and contracts, and closing net-new deals. Partner with leadership on pricing and deal strategy while documenting a repeatable sales playbook. Success requires proven enterprise sales performance, self-sourced pipeline generation, large-contract closing experience, and the ability to engage senior executives and technical stakeholders.

Lead strategic commercial relationships across a portfolio of approximately 100 accounts. Manage complex, multi-stage insurance solution sales cycles, including RFPs, diligence, governance reviews, negotiations, and seven-figure master agreements. Build relationships with senior claims, risk, operations, and finance executives; collaborate with deployment and compliance teams; represent the company at industry conferences; and provide market intelligence. The role requires extensive claims-related sales experience, strong quota performance, operational claims knowledge, and travel of 50% or more.

Create short-form video from concept through final edit for major social platforms. Grow the company’s social presence, analyze engagement data, design and execute marketing experiments across social, live, and online channels, and develop operating guides. Contribute to go-to-market strategy, national launch planning, and growth initiatives by translating user, founder, and engineering insights into content ideas.

Works directly with clients to map business processes, define automation requirements, build and test reliable software workflows, deploy solutions into production, monitor performance, and resolve failures. Partners with platform engineers to create reusable capabilities and supports expansion opportunities and business development. Requires strong programming, software validation, customer communication, and independent meeting skills, with experience in forward-deployed engineering or similar technical client-facing roles.

Designs and operates shared infrastructure supporting engineering and review workflows. Builds distributed services, data pipelines, internal tools, and reliable long-running jobs involving external APIs and remote environments. Investigates incidents, improves performance and resource efficiency, strengthens failure recovery, and establishes engineering standards. The role requires production software experience, large-scale systems familiarity, end-to-end project ownership, sound engineering judgment, and strong initiative. Experience with AI task automation, model serving, or model quality testing is advantageous.

Own and improve production AI dialogue systems, create a new automated assistant, establish evaluation, monitoring, and tracing practices, diagnose system failures, stay current with AI developments, and collaborate closely with operations and engineering teams.

Lead client AI initiatives from discovery through rollout and adoption. Analyze operations and data, define scope and success measures, test solutions with technical teams, manage stakeholders, train users, and verify business improvements. Feed field insights into product decisions and deployment practices. The role requires technical fluency in software systems, interfaces, and data, plus executive presence and customer-facing communication. It is based onsite in San Francisco or New York with travel to customer offices.

Leads and remains hands-on with an engineering organization building server-side, data, and machine learning software. Responsibilities include architecture, system design, delivery planning, technical standards, production operations, coaching, performance management, hiring, and developing reusable customer solutions. The role requires regular production coding, leadership of engineers or substantial projects, client-facing communication, and periods working at client premises.

Lead alliance strategy with major public cloud providers, expanding joint selling, cloud marketplace presence, partner pipeline, and revenue. Advise executives on cloud commitments, marketplace credits, competitive positioning, and spend allocation. Develop partner enablement programs and sales materials, support marketplace transactions, collaborate with product and go-to-market teams, and monitor partner activation, pipeline, and revenue metrics. The role requires substantial cloud ecosystem partnership experience and 30–40% travel.

Deploys and sustains backend software in secure government environments, translating mission needs into production solutions. The engineer scopes pilots, leads technical deployments, develops backend code—preferably in Python—and works directly with operators, analysts, and government program offices. Responsibilities include requirements gathering, user testing, compliance with government IT infrastructure requirements, and long-term customer support. The role is onsite in Virginia with 20–30% travel to customer and operational sites.

Develop and test AI-driven software, designing evaluations, improving LLM capabilities, analyzing experiments and logs, and converting prototypes into reliable production code. Collaborate with customers, researchers, and subject-matter experts to identify needs, deliver technical solutions, and maintain software end to end. The role also involves tool integrations, context handling, memory, and multi-step workflows.

Own the machine learning model lifecycle from experimentation through production deployment. Train and evaluate generative image and video models, design evaluation suites, build curated datasets from user activity and expert feedback, and develop compact in-house models optimized for speed and quality. This individual contributor role requires ownership of modeling work in a small team and practical experience delivering generative or computer vision systems.

Own machine learning modeling for a software platform, adapting large language models, building training data pipelines, and developing evaluation methods. Run experiments, refine models, and collaborate with researchers and systems engineers to deploy dependable models in production. The role focuses on electronic design automation and hardware design data, including netlists, RTL, surrogate models, and graph neural networks. It also involves post-training LLMs, distributed training or inference, and model optimization.

Design, deploy, and optimize machine learning model-serving systems across multi-node clusters. Diagnose GPU, memory, and stack-level bottlenecks; implement and benchmark performance improvements; build tooling to compare quality, speed, and resource usage. Partner with research scientists to productionize model architectures and optimizations, stay current with research, and clarify technical requirements directly with customers.

Own electrical engineering for heavy-duty machinery, including system design, schematics, wiring, harnesses, installation, integration, testing, debugging, and fault resolution. Collaborate with mechanical, software, manufacturing, hardware, and technician teams to improve machine designs and transition prototypes into repeatable production. The role is hands-on and primarily onsite, involving shop-floor work and electrical subsystem rework.

Support and improve a heavily used web application as a backend-oriented full-stack engineer. Responsibilities include infrastructure and backend support, defect resolution, incremental improvements, feature development, product collaboration, and maintaining application reliability as usage scales. The role requires quickly becoming productive in a production environment and includes increasing ownership of product work over time.

Own software products end to end, building backend services, integrations, and user-facing applications. Develop evaluation frameworks, feedback loops, instrumentation, and reproducible tests to improve AI-agent performance. Generate product ideas, shape product direction with leadership, and independently deliver solutions amid ambiguity. The role requires strong product sense, UI design ability, cloud and infrastructure expertise, and experience shipping production systems from concept through delivery.

Build and own backend services and core platform features while contributing to product management, UX, user research, and customer onboarding. Research and ship applied AI capabilities, reduce technical debt, and work directly with senior leadership on product direction. The role requires strong ownership, product judgment, customer engagement, and the ability to work independently in a fast-paced healthcare technology startup.