Machine Learning Engineer

• Collaborate closely with ML Scientists to productize and scale ML models, from experimentation to robust production systems
• Design, build, and own large-scale, distributed machine learning systems for training, deployment, inference, and monitoring
• Lead design discussions and architecture reviews; drive high-impact engineering decisions for ML platforms and applications
• Mentor and coach junior engineers and peers on best practices in ML engineering, system design, and code quality
• Develop and maintain reusable components, libraries, and tools to accelerate ML development lifecycle
• Proactively identify areas for improvement in model performance, pipeline efficiency, data quality, or platform capabilities
• Ensure scalability, observability, and fault-tolerance across all components of the ML stack
• Promote engineering excellence by advocating for best practices in testing, CI/CD, infrastructure-as-code, and monitoring
• Partner with stakeholders across Data Engineering, Product, Marketing, and Platform teams to align solutions with business goals
• Stay up to date on advancements in MLOps, ML frameworks, distributed systems, and apply learnings to improve systems and processes

Senior Machine Learning Engineer

• Architect and integrate hybrid AI systems that combine traditional machine learning, deep learning, large language models (LLMs), and retrieval-augmented generation (RAG) pipelines.
• Design and deploy scalable AI architectures including APIs, microservices, and model-serving frameworks that integrate seamlessly with analytic, simulation, or operational systems.
• Lead the full AI/ML lifecycle — from data ingestion and feature engineering through training, deployment, and sustainment within secure DoD environments (IL5/IL6, ATO, GovCloud).
• Engineer event-driven data pipelines and feature stores for both structured and unstructured data, including text, imagery, and simulation outputs.
• Ensure Responsible AI practices by embedding traceability, explainability, and confidence scoring into deployed systems.
• Implement and maintain MLOps pipelines (MLflow, Kubeflow, Airflow, Docker/Kubernetes) to support continuous integration, retraining, and drift detection.
• Transition R&D prototypes into production, optimizing for mission constraints such as limited compute, edge environments, or disconnected operations.
• Provide technical leadership and mentorship, setting standards for model quality, architectural design, and ethical AI deployment across programs.
• Collaborate across engineering, data, and modeling teams to unify FTI’s AI portfolio, ensuring interoperability and reuse across mission systems.
• Support proposal and solution development, providing technical inputs for AI/ML architectures, data strategies, and Responsible AI assurance frameworks.

Distinguished AI/ML Engineering Lead

AI Research Scientist

• Engage in relevant research and development (R&D) of AI systems, models, and advanced machine learning algorithms that augment physics-driven modeling and simulation systems
• Explore and implement AI-powered simulation tooling in support of AI workflows through reinforcement learning, multi-agent systems, and hybrid modeling approaches
• Collaborate with research, engineering, and product teams to build AI-powered solutions that meet mission-critical modeling and decision-support needs
• Engage in and support the drafting and review of conference and journal articles and presentations, sharing advances with both internal stakeholders and the wider research community.
• Contribute content to technical invention disclosures, including associated narrative, graphics, and engagements in support of patent development
• Perform additional responsibilities (no more than 10% of duties) in support of the company’s technology and product development initiatives.

AI Research Scientist, Applied AI

AI Engineer

• Manage mission-critical collaboration and content management systems
• Integrate AI capabilities such as intelligent search, workflow automation, and context-aware assistance
• Collaborate closely with stakeholders, system owners, security teams, and end users
• Stay current with Department AI initiatives and systems
• Design, develop, test, implement, and deploy SharePoint infrastructure and solution components
• Drive innovation within the M365 and SharePoint environments
• Conduct stakeholder consultations and workshops

M365 Automation and AI Developer

Computer Vision Engineer

• Stay up to date with the latest research and innovations in deep learning, implement and experiment with new ideas.
• Foster collaborative partnerships with our researchers and engineers, transforming innovative research into robust, industrial-strength machine learning models.
• Taking approaches from initial evaluation and experimentation all the way to shipping.
• Defining and collecting training datasets.
• Building training pipelines and real-time inference run-times (PyTorch, TensorFlow, TensorRT, Python, C++).

Senior Deep Learning and Computer Vision Engineer – Autonomous Vehicles

• Define and drive the architecture, design, and development of scalable, reliable, and high-performance ML frameworks and platform capabilities to support model training at scale.
• Lead model training performance analysis and optimization efforts across distributed training workflows, improving scalability, efficiency, and cost across heterogeneous hardware environments.
• Raise the bar on system observability, debuggability, operational excellence, and developer experience across the ML training stack.
• Own large, ambiguous, cross-functional technical initiatives from strategy through execution, including technical roadmap definition, tradeoff analysis, and delivery.
• Influence platform direction by identifying long-term infrastructure investments, setting engineering standards, and driving adoption of best practices across teams.
• Collaborate across organizational boundaries to align requirements, resolve technical disagreements, and integrate new capabilities into the platform ecosystem.
• Mentor engineers through design reviews, technical guidance, and hands-on partnership, while elevating engineering quality across the team.

Staff Machine Learning Engineer – ML Training Infrastructure

• Define and execute the roadmap for runtime intelligence capabilities
• Partner with executive stakeholders to align runtime intelligence investments with enterprise priorities
• Lead development of core runtime capabilities: Context orchestration and state management, Memory systems, Retrieval systems, Personalization frameworks
• Establish observability standards across runtime intelligence systems
• Oversee end-to-end delivery of runtime intelligence platforms

Director, Machine Learning Engineering

• Own end-to-end design, development, and maintenance of high-performance AI solutions that use agentic workflows to deliver concrete business value for internal stakeholders and customer-facing applications.
• Collaborate with cross-functional teams, including data scientists, ML engineers, software engineers, product managers, and designers to gather requirements, define project scope and prioritize feature development.
• Establish pragmatic technical visions and roadmaps that balance business outcomes, product release timelines, and engineering excellence.
• Integrate and build solutions using GEICO’s AI platform architecture.
• Partner with platform teams to communicate requirements, understand current capabilities and gaps, and contribute to platform feature roadmaps and development.
• Ideate, define, and build first-of-its-kind solutions within GEICO, with a deep understanding of business and technical processes, applications, and architecture to guide development.
• Drive the selection, evaluation, and implementation of software technologies, tools, and frameworks, balancing build vs. buy, speed to market, maintainability, etc.
• Take ownership in project planning and stakeholder management, driving technical alignment, ensuring efficient resource allocation, and timely delivery of solutions.
• Mentor and guide junior engineers via code reviews and design sessions, establish and enforce best practices, and foster a collaborative and high-performance team culture.

Staff Machine Learning Engineer

• Architect, train, fine-tune, and evaluate state-of-the-art speech representations and ASR models (e.g., End-to-End Conformer, Whisper, RNN-T, and hybrid CTC/Attention architectures) across multiple global languages
• Design and deploy highly scalable algorithms for dynamic vocabulary insertion, contextual biasing, and language model (LM) personalization to precisely capture customer-specific terminology, acronyms, and product names
• Implement automated framework evaluations to benchmark model performance, rigorously tracking Word Error Rate (WER), Character Error Rate (CER), embedding-based metrics, latency budgets (RTF), and computing efficiency profiles under varying acoustic environments
• Develop pioneering multilingual benchmarks for end-to-end conversational AI agents, including speech-to-text and text-to-speech components
• Partner with core engineering teams to build, optimize, and maintain high-throughput pipelines optimized for both ultra-low latency real-time streaming inference and high-efficiency asynchronous (batch) multi-channel speech analysis
• Translate product requirements into technical AI roadmaps, working hand-in-hand with Product Managers to ship speech-to-text, simultaneous translation, and semantic speech analytics features

AI Researcher / ML Engineer – ASR, Speech Specialist

• Design, build, and deploy AI-powered tools and automations for HR and Talent use cases, including intelligent workflow automations, employee-facing chatbots, document processing solutions, and decision-support systems
• Identify opportunities to improve manual or repetitive HR workflows through scalable AI/ML-driven automation solutions
• Develop integrations between AI solutions and enterprise systems including Workday, ATS platforms, ServiceNow Ticketing, and other internal tools ensuring seamless data flow and user experience
• Partner directly with HR Operations and cross-functional stakeholders to gather requirements, prototype solutions, run pilots, and drive successful adoption
• Continuously monitor and improve deployed tools based on user feedback, operational metrics, and evolving business needs
• Create technical documentation, implementation guides, and operational handoff materials to support long-term maintainability and scalability.

Principal AI Engineer, HR Automation

LLM Engineer

• Lead bring-up, validation, and debugging of large-scale AI clusters, infrastructure, and end-to-end workloads, setting the standard for how the team operates.
• Bring up, tune, and benchmark AI pre-training, post-training, and inference workloads using PyTorch, NeMo / Megatron, TensorRT-LLM, and adjacent NVIDIA AI software stacks.
• Profile and optimize end-to-end workload performance across compute, memory, networking, and communication layers using tools such as Nsight Systems, NCCL tests, and custom microbenchmarks.
• Analyze scaling efficiency for distributed LLM workloads using data, tensor, pipeline, and expert parallelism across modern GPU clusters, and translate findings into concrete tuning guidance.
• Own root-cause analysis of complex failures — hangs, performance regressions, topology sensitivity in large distributed environments.
• Define and build the resilience and failure-attribution stack: detecting, triaging, and attributing node, fabric, and workload failures across the cluster at scale.
• Build repeatable benchmark suites, automation, acceptance criteria, and qualification workflows on new platforms.
• Tune runtime settings, communication parameters, and deployment configurations in close partnership with framework, systems, and platform teams.
• Deliver actionable, data-driven recommendations based on profiling, benchmark results, and cluster characterization.
• Mentor engineers, drive technical standards, and act as a force multiplier across the broader performance and infrastructure organization.

Senior Software Engineer, DGX Cloud AI Infrastructure

• Bring up, validate, and debug large-scale AI clusters, infrastructure, and end-to-end workloads.
• Bring up, tune, and benchmark AI pre-training, post-training, and inference workloads using PyTorch, NeMo / Megatron, TensorRT-LLM, and adjacent NVIDIA AI software stacks.
• Perform root-cause analysis of failures in large distributed environments.
• Contribute to the resilience and failure-attribution tooling that detects, triages, and attributes node, fabric, and workload failures across the cluster.
• Build and maintain repeatable benchmark suites, automation, acceptance criteria, and qualification workflows on new platforms.
• Tune runtime settings, communication parameters, and deployment configurations in close partnership with framework, systems, and platform teams.
• Deliver actionable, data-driven recommendations based on profiling, benchmark results, and cluster characterization.

Software Engineer, DGX Cloud AI Infrastructure

• Architect the ranking backbone for query understanding and personalization
• Design and build a search autosuggest system
• Develop production-grade Multi-Task Learning
• Own the inference layer for quality optimization
• Advance evaluation practices for long-horizon metrics
• Partner across teams to translate business goals into ranking policies
• Mentor ML engineers in ranking expertise

Senior Machine Learning Engineer II, Search & Recommendations Ranking

• Develop and maintain machine learning models that are integral to our production decision-making system and directly influencing business outcomes.
• Adopt or build new technologies for training and serving ML models (e.g. support large models, increase developer velocity, etc)
• Monitor the latest ML research for functional ideas that the team could try.
• Optimize ML Pipelines – Build and scale efficient pipelines for real-time and batch processing.
• Model Monitoring & Improvement – Track performance, detect drift, and automate retraining.
• Use strong communication skills (verbal and written) to explain statistical and machine learning concepts to both technical and non-technical audiences
• Collaborate with a team of world-class engineers with diverse backgrounds.
• Be part of an “engineering excellence” culture through state-of-the-art tools, risk-driven testing, explainable systems, and code review.

Machine Learning Engineer, Conversion ML

• Lead, mentor and guide Data Scientists, Machine Learning and AI engineers to build solutions adhering to industry best practices and deliver scalable solutions including model architecture and algorithm selection
• Lead by example and always strive to improve the design for more scalable, cleaner, and decoupled implementations
• Drive adoption of best practices in model development, code quality, testing, and documentation
• Solid understanding and usage of automated tools (AI) while adhering to company policy
• Define strategic direction for machine learning projects and collaborate with product and engineering stakeholders
• Oversee end-2-end machine learning workflow, including data collection, model development, deployment and modeling
• Foster innovation by exploring new ML techniques, tools, and technologies
• Communicate strategies, progress, and results to leadership and cross-functional teams
• Ensure responsible AI practices, including fairness, explainability, and compliance with privacy and ethical standards
• Develop partnerships across the organization to identify and prioritize high-impact ML opportunities
• Available for On-Call rotations based on the team’s escalation policy and support schedule for ML/AI solutions

Manager, Machine Learning Engineering – Ad Platforms

• Create, research, implement, and maintain state-of-the-art machine learning models and data pipelines
• Conduct applied research into financial modeling for investment professionals
• Collaborate with senior investment professionals and technology associates
• Uphold high standards of quality ensuring integrity in both form and function
• Collaborate effectively to contribute to decisions regarding modeling and technology

Machine Learning Engineer II

• Architecting and delivering scalable prototypes for various features
• Research, design, and prototype cutting-edge generative AI solutions that enhance CrowdStrike's AI Detection and Response capabilities
• Create, curate, and maintain high-quality datasets for training and evaluating deep learning models; establish data collection pipelines, labeling workflows, and quality assurance processes to ensure dataset integrity
• Conduct experiments to further the capabilities of existing models

Senior AI Scientist – AI Detection and Response

• Design and build agentic AI workflows that can plan and execute complex, multi-step tasks
• Build and maintain RAG solutions end-to-end
• Implement retrieval evaluation and continuous improvement loops
• Integrate tools and data sources using MCP or equivalent standard interfaces
• Deploy and operate AI services on AWS
• Engineer robust runtime behavior
• Enable workflow automation and integration
• Create and maintain technical documentation
• Collaborate cross-functionally with product/business stakeholders
• Other duties as assigned

• Design and build agentic AI workflows that can plan and execute complex, multi-step tasks
• Build and maintain RAG solutions end-to-end
• Implement retrieval evaluation and continuous improvement loops
• Integrate tools and data sources using MCP or equivalent standard interfaces
• Deploy and operate AI services on AWS with strong operational practices
• Engineer robust runtime behavior including error handling, retries, fallbacks, circuit breakers, and guardrails
• Enable workflow automation and integration by identifying high-value repetitive processes
• Create and maintain technical documentation for architectures, interfaces, evaluation approaches, and operational runbooks
• Collaborate cross-functionally with product/business stakeholders to validate outcomes against measurable success criteria
• Other duties as assigned.

• Architect, build and scale a recommendation system that powers the state-of-the-art personalization experience to users across Max, HBO, Discovery+ and other WBD offerings
• Collaborate with ML/Ops engineers to develop and improve core components, infrastructure and architecture to train, deploy and serve models at scale
• Collaborate with other data scientists, engineers, product teams and other key stakeholders and drive ML projects from conception to completion
• Author, test, review, and optimize production-level code in Python, Go and Java while executing best practices in version control and code integration
• Use and build upon open-source cloud computing technologies
• Participate and support engineering leaders in strategic planning and demonstrate good judgment in setting and delivering against strategic goals for the team
• Mentor, influence engineers across organizations and lead by example with high quality examples of your work at the organization level
• Motivate, inspire and create a culture of experimentation and data-driven innovation while constantly striving to be an advocate for doing what is right for our customers

See all jobs on JobTailor

Senior Machine Learning Engineer

Distinguished AI/ML Engineering Lead

AI Research Scientist, Applied AI

M365 Automation and AI Developer

Senior Deep Learning and Computer Vision Engineer – Autonomous Vehicles

Staff Machine Learning Engineer – ML Training Infrastructure

Director, Machine Learning Engineering

Staff Machine Learning Engineer

AI Researcher / ML Engineer – ASR, Speech Specialist

Principal AI Engineer, HR Automation

Senior Software Engineer, DGX Cloud AI Infrastructure

Software Engineer, DGX Cloud AI Infrastructure

Senior Machine Learning Engineer II, Search & Recommendations Ranking

Machine Learning Engineer, Conversion ML

Manager, Machine Learning Engineering – Ad Platforms

Machine Learning Engineer II

Senior AI Scientist – AI Detection and Response

AI Engineer

AI Engineer

Senior Machine Learning Engineer – Personalization