Research Engineer

• design, develop, and apply advanced engineering and mathematical models
• drive innovative solutions for complex Intel manufacturing and supply chain challenges
• identify and solve highly complex problems across factory performance, production loading, equipment optimization, decision science, and supply and distribution network operations
• build, develop, validate, and deploy mathematically-based decision algorithms, heuristics, optimization models, and simulation tools
• work collaboratively with business units to identify root causes, define strategic and tactical requirements, and implement appropriate business and technical solutions

Operations Research Engineer

• Design and implement behavioral detections and analytic content using DTEX’s proprietary telemetry and data models.
• Build and maintain full-stack applications and internal tools that support intelligence workflows, detection pipelines, and customer-facing features.
• Develop and optimize algorithms for behavioral modeling, anomaly detection, and risk scoring using structured and unstructured data.
• Lead QE efforts for the Intelligence team, including test automation, regression validation, and release readiness for detection content and supporting tools.
• Partner with data scientists, detection engineers, product managers, and customer success teams to translate intelligence needs into scalable solutions.
• Stay current with emerging threats, detection methodologies, and full-stack technologies to continuously improve DTEX’s detection and response capabilities.

Threat Intel Research Engineer

• Design and build data systems that power reliable AI workflows across enterprise environments
• Develop pipelines for collecting, cleaning, transforming, labeling, and evaluating domain-specific data used by AI systems
• Create data quality frameworks that identify coverage gaps, ambiguity, drift, duplication, leakage, and other failure modes
• Build tools and workflows that help teams turn raw customer data into usable context for retrieval, evaluation, reasoning, and execution
• Partner with AI Researchers and AI Engineers to understand how data quality affects system behavior and production outcomes
• Develop synthetic data, annotation, and feedback-loop strategies to improve system performance in areas where real-world data is sparse or noisy
• Analyze customer workflows and datasets to determine what information AI systems need, where that information should come from, and how it should be represented
• Communicate clearly with internal teams and customer stakeholders about data assumptions, limitations, risks, and tradeoffs

Research Engineer, Data

• Design, prototype, and implement agentic AI systems that perform reliably across complex enterprise workflows
• Build compound AI architectures that combine planning, tool use, retrieval, memory, evaluation, orchestration, and execution
• Investigate how agents reason, coordinate, recover from errors, and interact with external systems under real-world constraints
• Develop evaluation frameworks that measure agent behavior, task completion, reliability, robustness, and failure modes
• Create tools and abstractions that make agent behavior easier to observe, debug, test, and improve
• Partner with AI Researchers to explore new agent architectures and with AI Engineers to harden successful approaches for production use
• Integrate agents into customer APIs, applications, data platforms, and operational workflows
• Communicate clearly with internal teams and customer stakeholders about agent capabilities, limitations, tradeoffs, and risks

• Define and enforce quality standards for training data
• Build tooling and workflows to audit supplier-generated datasets, including sampling strategies, validation pipelines (rule-based and model-assisted), and feedback loops
• Determine if and how human-in-the-loop review workflows can be used to optimize QA
• Partner with data vendors to debug quality issues, provide actionable feedback, and improve their data generation processes
• Continuously integrate QA learnings into infrastructure tools and data vendor portal to reduce anomalies, inconsistencies, and edge cases

• Develop measurement performance requirements. Select and evaluate new sensors for current and new sensing capabilities.
• Root-cause sensor signal issues from fleet to bench: use fleet telemetry (time-series + metadata) to isolate cohorts and failure signatures, test hypotheses via circuit simulation and benchtop reproduction, drive fixes design solutions, and partner with HW and FW engineering to implement them.
• Design and validate measurement chain changes to improve physical phenomena observability.
• Downsize on-device data. Develop signal compression techniques, selective data storage decisions, and sampling rate reduction strategies.
• Develop and own test methods to characterize and validate sensor performance.
• Develop hardware-in-the-loop test infrastructure to reproduce the real-world physical phenomena Gridware technology detects. Run hardware-in-the-loop tests to validate changes to our tech stack (HW, phenomena detection algorithms).
• Mentor team members. Raise the technical rigor of experiments, analysis, and validation work.
• Collaborate closely with product managers, data scientists, and SW/HW/FW engineers.

Senior Research Engineer, Electrical

• Design and evaluate information access + reasoning strategies across RAG, agents, and classic ML: chunking, embedding models, hybrid search, metadata filtering, semantic routing
• Prototype GenAI workflows (including agentic systems) that map and reason over compliance objects (controls ↔ risks ↔ requirements ↔ evidence)
• Explore ML + probabilistic approaches where GenAI is not the best fit: classifiers, ranking models, graph/link prediction, calibration, and structured prediction
• Build and maintain evaluation frameworks: golden datasets, automated quality metrics, regression detection
• Implement and tune ranking/reranking systems: cross-encoders, LLM-based rerankers, learning-to-rank, custom scoring functions
• Run experiments to validate hypotheses and quantify improvements before production rollout
• Debug failure modes and build error taxonomies across retrieval, reasoning, and generation
• Collaborate with AI and Software Engineers to hand off validated approaches for productionization
• Stay current on applied research in RAG, agents, LLM evaluation, and relevance modeling; bring innovations into the product

Applied Research Engineer

• Design and evaluate information access + reasoning strategies across RAG, agents, and classic ML
• Prototype GenAI workflows that map and reason over compliance objects
• Explore ML + probabilistic approaches where GenAI is not the best fit
• Build and maintain evaluation frameworks
• Implement and tune ranking/reranking systems
• Run experiments to validate hypotheses and quantify improvements before production rollout
• Debug failure modes and build error taxonomies across retrieval, reasoning, and generation
• Collaborate with AI and Software Engineers to hand off validated approaches for productionization
• Stay current on applied research in RAG, agents, LLM evaluation, and relevance modeling

Senior Applied Research Engineer

• Design and evaluate information access + reasoning strategies across RAG, agents, and classic ML: chunking, embedding models, hybrid search, metadata filtering, structured retrieval, tool use, and multi-step workflows
• Prototype GenAI workflows (including agentic systems) that map and reason over compliance objects (controls ↔ risks ↔ requirements)
• Explore ML + probabilistic approaches where GenAI is not the best fit: classifiers, ranking models, graph/link prediction, calibration, and weak supervision
• Build and maintain evaluation frameworks: golden datasets, automated quality metrics, regression detection
• Implement and tune ranking/reranking systems: cross-encoders, LLM-based rerankers, learning-to-rank, custom scoring functions
• Run experiments to validate hypotheses and quantify improvements before production rollout
• Debug failure modes and build error taxonomies across retrieval, reasoning, and generation
• Collaborate with AI and Software Engineers to hand off validated approaches for productionization
• Stay current on applied research in RAG, agents, LLM evaluation, and relevance modeling; bring innovations into the product

Staff Applied Research Engineer

• Deliver mission-critical improvements and new features for our autonomy motion planning and control stack
• Work alongside engineers, research scientists, and domain experts to build optimal and data-driven controls to realize planned vehicle trajectories
• Develop machine-learning vehicle models and learning-based control policies leveraging extensive data from autonomous trucking fleet
• Solve real-world autonomy system challenges by participating in vehicle performance analysis, tuning, and troubleshooting.

Senior Research Engineer, Controls

• Lead adversarial testing of AI by designing, scoping, and executing red team assessments against our AI ecosystem using a risk-based prioritization approach to discover and address vulnerabilities before they can be exploited.
• Innovate in AI attack techniques by combining cutting-edge academic research with proven offensive security methods to establish new Tactics, Techniques, and Procedures, and operationalize emerging research to keep assessments aligned with the state of the art.
• Build and scale security tooling using an automation-first philosophy, driving initiatives to shift security testing left by sharing purpose-built tools with AI security stakeholders across Engineering, Research, and Ethics.
• Serve as a strategic partner across the company, providing an offensive security perspective to guide product development, support corporate governance, and contribute to policies such as Salesforce's Generative AI Security Standard.

Adversarial AI, Research Engineer

• Build the eval stack from scratch
• Design and own the systems that measure whether Firecrawl's outputs are actually good — across scrape, crawl, extract, and map
• Design benchmarks that reflect reality
• Own LLM-as-judge pipelines
• Close the loop with models and RL
• Run fast experiments and communicate clearly

Research Engineer – Evals

• Designing and implementing new information architecture strategies
• Performing finetuning and reinforcement learning for language models
• Building knowledge base eval sets to identify failure modes of language models
• Designing and evaluating advanced agentic search capabilities

Research Engineer, Knowledge Team

• Own a research direction for search post-training problems
• Build instrumentation for controlled experiments
• Design evaluations to distinguish reasoning over evidence
• Drive optimization rigor across the stack
• Collaborate with researchers across various teams
• Set the bar for experimental standards

Research Engineer, Search and Knowledge

• Develop/maintain network security analysis (NSA) to produce reliability constraints across large-scale ISO systems
• Assist in building and validating SCUC/SCED models, with realistic transmission and contingency constraints.
• Help automate NSA for integration into market optimization workflows.
• Support data processing pipelines for network, generator, and market datasets across ISOs (ERCOT, PJM, CAISO, etc.)
• Experiment with machine learning approaches to accelerate dispatch or contingency evaluation.
• Document results, highlight performance gaps, and propose improvements

Energy Systems Research Engineer Intern

• Work with applied researchers to design, implement and test next generation of RL and pos-training algorithms
• Contribute and advance open source by developing NeMo-RL, Megatron Core, and NeMo Framework and yet to be announced software
• Solve large-scale, end-to-end AI training and inference challenges, spanning the full model lifecycle from initial orchestration, data pre-processing, running of model training and tuning, to model deployment.
• Work at the intersection of computer-architecture, libraries, frameworks, AI applications and the entire software stack.
• Performance tuning and optimizations, model training with mixed precision recipes on next-gen NVIDIA GPU architectures.
• Publish and present your results at academic and industry conferences

Senior Research Engineer

• Build and improve RL training infrastructure
• Identify and remove bottlenecks across the RL stack
• Partner with researchers and engineering teams
• Own reliability and performance of research runs

Research Engineer, Machine Learning – RL Velocity

• Be part of a multidisciplinary team of Engineers and Scientists working on a scalable system to produce fast, accurate calibration for a wide array of sensing modalities (e.g. camera, lidar, radar, IMU, GNSS).
• Develop the next generation of calibration algorithms leveraging classical and learning-based approaches in 3D geometry with a focus on robustness, compute efficiency, and safety-critical accuracy.
• Design and implement real-time calibration integrity monitoring algorithms to ensure safe autonomous driving on a diverse fleet of vehicles.
• Coordinate large-scale deployment of the calibration of an entire fleet of autonomous vehicles.

Research Engineer, Calibration

• Accelerate researchers by taking on the heavy parts of large-scale ML pipelines and building robust tools.
• Interface cutting-edge research with production: integrate checkpoints, streamline evaluation, and expose APIs.
• Conduct experiments on the latest deep-learning techniques (sparsified 70 B + runs, distributed training on thousands of GPUs).
• Design, implement and benchmark ML algorithms; write clear, efficient code in Python.
• Deliver prototypes that become production-grade components for Le Chat and our enterprise API.

Research Engineer, Data Infrastructure

• Lead fine-tuning and continued training of video generation models, including image-to-video and joint audio-visual generation.
• Design and experiment with novel model architectures for multimodal generation, including multimodal conditioning (voice, structured text, reference images).
• Leverage techniques such as LoRA, RLHF, and full-parameter fine-tuning to improve model quality across diverse visual scenarios.
• Design and build large-scale data pipelines and automated annotation workflows to support continuous model improvement.
• Explore model compression, inference acceleration, and serving optimizations to enable efficient real-time video processing at scale.

See all jobs on JobTailor

Operations Research Engineer

Threat Intel Research Engineer

Research Engineer, Data

Research Engineer

Research Engineer

Senior Research Engineer, Electrical

Applied Research Engineer

Senior Applied Research Engineer

Staff Applied Research Engineer

Senior Research Engineer, Controls

Adversarial AI, Research Engineer

Research Engineer – Evals

Research Engineer, Knowledge Team

Research Engineer, Search and Knowledge

Energy Systems Research Engineer Intern

Senior Research Engineer

Research Engineer, Machine Learning – RL Velocity

Research Engineer, Calibration

Research Engineer, Data Infrastructure

Research Engineer, Multimodal