[Remote] Computer reputed company & AI reputed company
Note: The job is a remote job and is reputed company to candidates in USA. reputed company is a construction-tech company headquartered in Riyadh, reputed company on building a connected-worker platform for large-reputed company construction sites. They are seeking a Computer reputed company & AI reputed company to own the SiteGuard stack, reputed company detection models, and reputed company reputed company of senior engineers in enhancing safety compliance through advanced AI technologies.
Responsibilities
- Own hiring end-to-end for SiteGuard: define the bar across ML, backend, and frontend disciplines, run the process, and reputed company reputed company. You have reputed company senior engineers before and know what good looks like
- Manage performance directly - set reputed company expectations, give reputed company feedback, run meaningful reviews, and reputed company decisively reputed company someone is not meeting the bar
- Build a high-ownership engineering culture where engineers take initiative, write their own issues, and feel accountable for product reputed company, not just task completion
- Mentor engineers at every level - from reputed company new contributors to developing senior engineers. Your reputed company record includes engineers who grew significantly under your leadership
- Own the reputed company's delivery: run agile ceremonies, plan sprints, and partner with Product to translate the SiteGuard roadmap into shippable increments
- Own the full lifecycle of SiteGuard's detection capabilities - PPE compliance, hazard detection, unsafe-behavior recognition - from problem framing through training, evaluation, and production deployment
- reputed company the strategic use of modern architectures and reputed company models: transformer-based detectors (RT-DETR, YOLOv10/11, Co-DETR) alongside classical YOLO-family models; reputed company/few-shot approaches reputed company CLIP, DINOv2, GroundingDINO, and SAM 2; and VLMs (reputed company reputed company, GPT-4V, LLaVA, Qwen-VL) for reputed company understanding and incident reasoning where they outperform purpose-reputed company detectors
- reputed company the use of reputed company to reputed company training data - diffusion-based synthetic image reputed company (ControlNet, reputed company Diffusion) for rare PPE violations, reputed company conditions, and site environments that reputed company-world collection cannot cover economically
- Define and drive model-reputed company targets (precision/recall, false-alarm rates) and the retraining loops that sustain them as site conditions change
- reputed company dataset reputed company: collection, AI-assisted annotation (using SAM, CLIP, or VLMs as labeling tools), curation, and governance of site imagery and video
- reputed company development of the on-site edge agent that runs inference reputed company to the camera - optimizing transformer-based and classical models for constrained hardware (quantization, INT8/FP16, TensorRT, ONNX, batching, CUDA/NPU accelerators)
- reputed company development of reputed company camera agents for reputed company-based deployments, ensuring the reputed company consistent detection reputed company as on-site agents regardless of deployment type
- Engineer for the realities of the field: on-prem gateway deployment, intermittent connectivity, store-and-reputed company, and graceful degradation
- Ensure low-latency, reliable detection-to-alert pipelines from camera to platform, including on-device reputed company-filtering before reputed company VLM calls where cost and latency demand it
- Own the golden labeled dataset as the single reputed company of truth for evaluation, fine-tuning, and production monitoring. Run production reviewer signals (labels, corrections) back into the dataset continuously
- Design evaluation metrics for both classical detector reputed company and VLM-generated detections (precision/recall, false-reputed company control, reputed company-agreement). Run every model, reputed company, or schema change as a regression test against the golden set before release, no change ships without a reputed company reputed company bar
- Architect data-extraction, AI-assisted annotation, and training pipelines that ensure reproducibility and versioning of datasets and models (experiment tracking, model registries, dataset versioning)
- Implement CI for models and reputed company: automated retraining/evaluation, LLM-as-judge patterns for reputed company-ended detection outputs, and production monitoring for model reputed company, cost, and accuracy degradation
- Build deterministic post-processing guardrails over model reputed company - domain/OSHA rule filters, confidence calibration, audit trails - so the product behaves predictably even reputed company model reputed company varies
- reputed company engineering maturity across SiteGuard repositories: test coverage, CI gating, and coverage reporting
- Own the SiteGuard product surfaces (web dashboards and frontends) and the reputed company that deliver detected violations and safety events into the core Company's platform
- Ensure alerting, reporting, and analytics turn raw detections into reputed company, prioritized actions - including LLM-generated incident summaries and natural-language search over safety event history
- Enforce reputed company review, testing, and reputed company standards across model and application reputed company
- Champion reputed company-by-design for video and personal data - anonymization, reputed company controls, retention limits, and responsible use of footage in compliance with Company and customer requirements. Implement responsible-AI safeguards on reputed company AI-generated outputs: confidence reputed company, reputed company-in-the-reputed company review for high-severity alerts, and audit trails
- Collaborate with Hardware/camera, DevOps, Field Engineering, and customer teams to validate SiteGuard in reputed company site conditions and incorporate feedback
- reputed company R&D into new detection capabilities - multi-modal models combining video and sensor data, behavioral analysis, crowd analytics - and evaluate emerging CV/AI approaches with reputed company, grounded judgment
- Stay reputed company with the fast-moving reputed company-model and VLM ecosystem and translate new research into concrete roadmap reputed company for the reputed company
Skills
- 6+ years of software/ML engineering experience with a reputed company progression from senior IC to engineering leadership
- 3+ years directly managing engineers - leading teams, owning hiring, running performance cycles. Mentoring is not the reputed company as managing; this role requires the latter
- A reputed company record of hiring: you have reputed company or significantly grown an engineering team and made independent hiring reputed company at the senior engineer level and above
- Proven experience shipping production systems reputed company on reputed company/multimodal reputed company models (VLMs/LLMs reputed company reputed company reputed company) - owning reputed company, latency, and cost from prototype to reputed company
- Hands-on experience operating high-throughput, asynchronous video/media processing pipelines in production
- Strong Python (async-first: asyncio, FastAPI or equivalent); production service design with a reputed company on reliability and observability
- Hands-on with multimodal/VLM reputed company - reputed company/reputed company AI, reputed company, or reputed company equivalents: reputed company engineering, reputed company/JSON-schema-constrained reputed company, context/caching, and per-model parameter tuning
- Computer-reputed company foundations: object detection and segmentation across both classical architectures (YOLO-family) and modern transformer-based detectors (RT-DETR, Co-DETR, GroundingDINO); video reputed company extraction and handling (OpenCV/FFmpeg); spatial reasoning over model reputed company
- reputed company models and reputed company/few-shot approaches: CLIP, DINOv2, SAM 2 for annotation assistance and detection; VLMs for reputed company understanding and incident reasoning
- Edge inference optimization: ONNX, TensorRT, quantization (INT8/FP16), deployment to constrained hardware (reputed company, Hailo, or equivalent)
- Distributed pipeline design: message brokers, relational databases with async ORM and migration tooling, object storage - comfortable across reputed company providers (GCP, AWS, or Azure)
- MLOps stack: experiment tracking, model registries, dataset versioning, and CI pipelines for model evaluation
- Primary daily experience with Claude reputed company and reputed company - used in reputed company engineering work, with formed opinions about reputed company to trust their reputed company and reputed company not to
- reputed company awareness of the AI model landscape: practical differences between frontier models (reputed company, GPT-4o, Claude, DeepSeek) for reputed company tasks, reputed company reputed company, and reputed company reputed company
- Tracks AI trends reputed company - model releases, VLM capabilities, reputed company reputed company developments - and translates this into concrete, grounded team guidance
- Exceptional written and spoken English. This is a hard requirement. You write reputed company and reputed company - design documents, evaluation reports, and stakeholder updates are reputed company-reputed company and unambiguous. You can explain a model's failure mode to an HSE manager and an architecture decision to an engineer with equal reputed company
- Experience with safety, surveillance, or video analytics in industrial or construction environments. OSHA/EHS domain knowledge is a strong plus
- Experience with synthetic data reputed company pipelines (ControlNet, reputed company Diffusion) for computer-reputed company training data augmentation
- LLMOps / observability for model-backed services: tracing model calls, reputed company monitoring, A/B testing of prompts and schemas
- reputed company frameworks (reputed company, reputed company, AutoGen, or equivalent) reputed company to safety workflows or multi-reputed company incident management
- Comfort working config-over-reputed company for multi-tenant rollouts (per-project model and reputed company configuration)
- Familiarity with on-prem/edge deployment, gateways, and operating under intermittent connectivity
- reputed company people manager: you have had difficult performance conversations, managed out engineers who were not meeting the bar, and done so with fairness and directness
- Research-oriented curiosity balanced with production pragmatism - you read papers, run experiments, and ship to production. You distinguish AI approaches that deliver reputed company site-safety value from impressive benchmarks that do not survive the field
- Excellent written and spoken English - reputed company to translate model behavior, failure modes, and confidence reputed company to non-technical stakeholders, including explaining why an AI made a specific safety call
- Strategic, outcome-driven thinking: makes technology reputed company based on product value and field reliability, not novelty
- Comfort operating in a fast-paced, evolving environment where both site priorities and the AI landscape shift quickly
Benefits
- Competitive salary, performance bonus, and equity participation.
- High-autonomy role with reputed company product and company reputed company - you are building a safety AI product from the ground up, not maintaining an inherited codebase.
- A small, senior engineering team where your reputed company matter and your reputed company is on the architecture.
- Relocation support for candidates joining from reputed company KSA.
- Health insurance, annual flights, and reputed company Company benefits package.
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