Research Engineer

• Build robust, reproducible and user-friendly pipelines for automated model fine-tuning, alignment and evaluation
• Design and implement modular, easy-to-maintain, multi-model pipelines for protein design.
• Develop highly scalable ETL pipelines to process petabyte-scale protein data for model pretraining
• Optimize model training and inference code to maximize throughput and resource utilization when deployed at scale
• Develop software and infrastructure that enable the ML team to work quickly and frictionlessly in distributed and multi-cloud environments
• Partner with ML and protein design scientists to prototype research ideas and bring them into production

Machine Learning Research Engineer

• Design, build, and improve the models that determine target portfolio allocations, balancing risk, return, and client-specific constraints.
• Build and maintain the systems that translate those target allocations into real trades, with an emphasis on tax efficiency (including tax-loss harvesting) and cost-aware execution.
• Apply sound risk and portfolio management techniques — including optimization, factor-based risk modeling, and statistical estimation — to keep our strategies robust as markets and client needs evolve.
• Backtest and validate new models and trading logic against historical data before they touch live portfolios.
• Bring an investment/portfolio-management perspective to tradeoffs the team makes, not just a numerical-optimization one.
• Partner with the investment team, product, and engineering to translate investment ideas into shipped, production-quality systems.
• Use AI coding tools as part of your day-to-day workflow to research and build faster.

Senior Quant Research Engineer, Trading & Portfolio Optimization

• Take a research direction and independently identify supporting resources – papers, benchmarks, blog posts – then implement or reimplement the relevant methods.
• Build and own the process to reproduce prior work internally and identify ways to improve on it.
• Own projects (for example, an RL/agentic environment build for a partner or a novel multimodal benchmark) end to end, including scoping, MVP implementation, and validation.
• Partner with strategic project leads and technical leads to translate ambiguous requirements into a concrete, testable research plan.
• Validate ideas through hands-on implementation, including annotating, evaluating, or sourcing data.
• Turn research directions into tangible outputs – a paid customer dataset, a customer pilot, an internal dataset, or a paper/blog post for publication or conference presentation.
• Bring an ML perspective to new opportunities — assessing technical feasibility of incoming requests and helping shape proposals where research depth is needed.

• Work alongside other motivated and talented research engineers to bring cutting-edge theory into customer-facing practice
• Build tools and pipelines to support ML and AI efforts
• Design, build, deploy, and maintain production services, like APIs, UIs, agentic evaluators, and more
• Roll out scalable cloud architectures
• Help with end-user requirements gathering and executive presentations

Applied Research Engineer

• Design and evaluate routing policies for LLM traffic to best use mixture of model systems.
• Build and run agentic benchmarks (e.g., Terminal-Bench ) to measure algorithm quality, and turn results into calibration data and routing profiles.
• Ship to an open-source repo: design docs, code review, docs, and community contributions.
• Collaborating with engineering teams across all of NVIDIA to ensure our software integrates seamlessly up and down the NVIDIA accelerated serving stack.

Senior Research Engineer – Enterprise Products

• Design, train, and evaluate ML models — including LLMs, diffusion models, and domain-specific architectures.
• Develop scalable experimentation pipelines spanning data ingestion, model training, and evaluation workflows.
• Collaborate with data and infrastructure teams to optimize training throughput and model quality.
• Contribute to open research, internal benchmarks, and new techniques in multimodal and generative AI.
• Rapidly prototype research ideas and productionize them into usable models and tools.
• Define and promote standards for research rigor, documentation, and reproducibility across the engineering org.

Founding Engineer – ML Research

• Design and run new evaluations of Claude's capabilities — reasoning, agentic behavior, knowledge, safety properties — and produce visualizations that make the results legible to researchers and decision-makers
• Build and harden the distributed eval execution platform so hundreds of evals run reliably against checkpoints throughout production RL training runs
• Own the dashboards researchers and leadership use to monitor model health during training, improving signal-to-noise, reducing latency, and making regressions impossible to miss
• Debug anomalous eval results mid-training-run, determine whether the cause is a model change or an infrastructure issue, and communicate the answer clearly under time pressure
• Improve the tooling, libraries, and workflows researchers use to implement and iterate on evaluations
• Partner with research teams across the full lifecycle of a new capability — from defining what to measure to interpreting results as training progresses
• Run experiments to characterize how prompting, sampling, and scaffolding choices affect results on internal and industry benchmarks
• Communicate evaluations and their results to internal stakeholders and, where appropriate, external audiences

Research Engineer, Model Evaluations

• Identify data quality issues including inconsistencies, formatting problems, and ingestion challenges
• Perform initial manual data quality review to deeply understand failure modes
• Build systems to automate quality checks at scale using rule-based and AI-driven approaches
• Design hybrid systems that balance automation with human-in-the-loop review where appropriate
• Continuously improve verification methods as the data landscape and AI tooling evolve

• Drive post-training experiments, pushing agent performance while navigating the Pareto frontier of cost, latency, security, and governance.
• Optimize agent harnesses, including domain-specific skills, tools, subagents, retrieval strategies, and validation loops that improve quality on long-horizon legal work.
• Design and develop grading and reward systems that are reliable enough for evaluation, efficient enough for iteration, and strict enough for high-stakes legal work.
• Study agent behavior, identifying patterns that correlate with successful work product, and converting those findings into training data, evals, or harness changes.
• Work with Harvey researchers and external research partners to define experiments, evaluate methodology, review results, and keep projects moving toward concrete model improvements.

Research Engineer – Post-Training

• 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

Research Engineer, Agent

• Design and run post-training workflows that improve the behavior, reliability, and usefulness of AI systems
• Develop datasets, preference signals, evaluation suites, reward models, fine-tuning workflows, and feedback loops for applied AI use cases
• Investigate how different post-training techniques affect system behavior across enterprise workflows and production constraints
• Build infrastructure for experimentation, model comparison, regression testing, and behavior analysis
• Partner with AI Researchers to explore new post-training methods and with AI Engineers to apply successful techniques in deployed systems
• Analyze model outputs, failure modes, human feedback, and production traces to identify opportunities for behavioral improvement
• Create repeatable processes for adapting AI systems to customer domains while preserving robustness, transparency, and maintainability
• Communicate clearly with internal teams and customer stakeholders about model behavior, evaluation results, limitations, and tradeoffs

Research Engineer, Post-Training

• 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

• 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.

Research Engineer jobs in California

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Machine Learning Research Engineer

Senior Quant Research Engineer, Trading & Portfolio Optimization

Research Engineer

Applied Research Engineer

Senior Research Engineer – Enterprise Products

Founding Engineer – ML Research

Research Engineer, Model Evaluations

Research Engineer

Research Engineer – Post-Training

Research Engineer, Agent

Research Engineer, Post-Training

Operations Research Engineer

Threat Intel Research Engineer

Research Engineer, Data

Research Engineer

Research Engineer

Senior Research Engineer, Electrical