Machine Learning

Machine Learning Data Jobs

232 jobs found

Today

AI Engineer

Booz Allen Hamilton Arlington, VA

Arlington, VA

Today

$78k - $176k/yr Full time AI Ai Ml Artificial Intelligence

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Job Description

AI Engineer on Booz Allen Hamilton's Global Defense team responsible for training, testing, deploying, and maintaining ML models, with exposure to next generation tooling such as LLMs, RAG pipelines, and AI agents.

Responsibilities

Train, test, deploy, and maintain models that learn from data.
Apply suitable ML algorithms and technologies to mission-critical solutions.
Collaborate with senior architects, platform leads, and business stakeholders to deliver solutions across enterprise and mission environments.
Engage with next-generation tooling such as LLMs, RAG pipelines, and AI agents while contributing to high-impact efforts requiring both technical skill and strategic insight.
Help clients navigate the landscape of ML algorithms, tools, and frameworks.
Collaborate with the team to address real-world challenges.
Define ML strategy for Global Defense.

Requirements

2+ years of experience in machine learning engineering within software engineering or data engineering roles.
Experience building AI-enabled features or prototypes using LLMs, embeddings, or third-party APIs including OpenAI or Anthropic.
Experience with prompt engineering concepts and using LLM SDKs or frameworks such as LangChain or LlamaIndex.
Experience with application containerization, CI/CD deployments, and Python.
Experience with cloud platforms including AWS, Azure, or GCP and their AI or ML services.
Experience with a vector database or search framework such as OpenSearch or Weaviate.
Experience developing backend services and building or consuming RESTful APIs.
Ability to follow code review practices, document work, and maintain clean, reusable code.
Secret clearance.
Bachelor’s degree.

Technologies

Python
Docker
Git
AWS, Azure, GCP
OpenAI, Anthropic
LangChain, LlamaIndex
Ollama, Weaviate, OpenSearch
Cursor, Cline, Windsurf, Claude Code, LLMs

Benefits

Health benefits
Life insurance
Disability benefits
Financial benefits
Retirement benefits
Paid leave
Professional development
Tuition assistance
Work-life programs
Dependent care

Clearance

Applicants selected will be subject to a security investigation and may need to meet eligibility requirements for access to classified information; Secret clearance is required.

Compensation

Salary at Booz Allen is determined by location, education, knowledge, skills, competencies, experience, contract affordability, and organizational requirements.
Projected compensation range: USD 77,600.00 to 176,000.00 per year.
The displayed estimate represents the typical salary range and is one component of the total compensation package.
This posting will close within 90 days from the Posting Date.

Identity Statement

Identity verification will leverage advanced biometrics and AI; you are expected to be on camera during interviews and assessments. Booz Allen may take your picture to verify identity and prevent fraud.

Candidate AI Usage Policy

AI is part of daily work at Booz Allen, and use of AI or other tools to assist with responses during interviews is prohibited unless explicit permission is provided.

Work Model

Remote: Occasional in-person work at Booz Allen or customer facilities may be required.
Hybrid: Frequent work from a Booz Allen facility; may also involve customer facilities.
Onsite: Work primarily at a Booz Allen office or customer facility, collaborating with colleagues and customers.

Commitment to Non-Discrimination

All qualified applicants will receive consideration for employment without regard to disability, veteran status, or any other status protected by applicable law.

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Today

Agentic AI Engineer — Healthcare AI

Deloitte Raleigh, NC

Raleigh, NC

Today

$135k - $265k/yr Full time Agentic Ai Agents Artificial Intelligence

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Job Description

Deloitte in Raleigh, NC offers an onsite role focused on healthcare AI within an AI-first initiative. You will build end-to-end LLM- and SLM-powered agentic systems for healthcare decisioning and deploy them into live clinical and operational settings. This position blends advanced engineering with practical, regulated workflows and a collaborative, impact-driven culture. The role carries a competitive salary and the opportunity to contribute to cutting edge AI in healthcare.

Location and compensation

Onsite in Raleigh, North Carolina. Salary range of USD 134,500 to 265,100 per year. A bachelor’s degree is required.

Responsibilities

Design and implement agentic systems capable of multi-step reasoning, planning, tool use, and workflow execution against complex, regulated operational processes.
Build stateful workflows using frameworks such as LangGraph and LangChain, including branching, retries, self-correction, human-in-the-loop checkpoints, and reusable orchestration patterns.
Engineer for long-horizon reliability, enabling multi-step task completion, recovery from compounding errors, planning under uncertainty, and robust tool use when steps fail.
Build the reasoning behind regulated decisions with policy- and criteria-grounded outputs, structured proposer/critic/judge style review, and auditable rationales for high-stakes decisions across clinical review, prior authorization, claims integrity, and care management.
Develop end-to-end Retrieval-Augmented Generation pipelines, covering ingestion, chunking, embeddings, vector and hybrid retrieval, reranking, contextual compression, and grounding strategies.
Engineer memory and context management, including conversational state, persistent memory, retrieval-aware context assembly, and token-efficient context selection.
Apply modern context-delivery patterns to ensure agents access the right information at the right time.
Implement observability and tracing for prompts, tool calls, retrieval quality, agent traces, failures, drift, latency, and production behavior.
Apply guardrails, safety controls, and failure-handling to reduce hallucinations and unsafe actions.
Evaluate agents at the trajectory and task level with multi-step task success metrics, failure-mode analysis, sandboxed testing, and integrated quality checks alongside human review.
Engineer healthcare-grade safety with deployment eval gates, human oversight and escalation models, auditability and traceability for regulated decisions, and PHI/HIPAA-aware data handling.
Build integrations with internal and external tools, APIs, enterprise systems, databases, and model providers to operate safely within real business workflows.
Deliver production-quality code with robust testing, CI/CD, logging, versioning, and documentation; balance quality, safety, latency, cost, and model risk in architectural decisions.
Partner with modeling and post-training engineers to improve model behavior for tool use, grounding, and long-horizon reasoning through evaluation-driven feedback and fine-tuning where appropriate.
Translate ambiguous, high-complexity operational processes into robust system logic and reusable AI patterns; stay current with agentic system advances and translate research into practical engineering decisions.

Requirements

Bachelor's degree in Computer Science, Engineering, Data Science, Computational Linguistics, or a related field.
Demonstrated depth building and shipping production agentic systems as a primary craft, with a track record of shipped systems, research, model releases, and open source work over years.
Strong hands-on experience building production agent systems with modern orchestration, including LangGraph/LangChain or equivalent with custom orchestration.
Experience designing and optimizing end-to-end RAG systems: indexing, retrieval, reranking, grounding, and evaluation.
Solid understanding of memory and context management, including context windows, retrieval-driven context assembly, persistent memory, and high-signal context selection.
Deep practical knowledge of LLM behavior, limitations, hallucination risks, reasoning constraints, and latency/cost trade-offs, plus evaluation methodologies.
Experience evaluating and debugging agent behavior through task-success and trajectory analysis, not just output quality.
Strong Python engineering skills and modern software practices: testing, CI/CD, version control, API integration; experience implementing observability, tracing, and debugging for production LLM-based systems.
Hands-on experience with at least one frontier model platform (e.g., Anthropic, Google, OpenAI) and/or open-weight/self-hosted models (e.g., Llama via vLLM), including production tool use and agent capabilities.
Ability to travel 0-50 percent, depending on client needs and engagement scope.
Limited immigration sponsorship may be available.

Technologies

LangGraph, LangChain, Python, vLLM, Llama
Pinecone, Weaviate, Milvus
LoRA, QLoRA
RAG, Anthropic, Google, OpenAI
FHIR

Preferred Qualifications

Experience with multi-agent systems and agent collaboration patterns.
Familiarity with vector databases and retrieval infrastructure such as Pinecone, Weaviate, or Milvus.
Exposure to model adaptation and fine-tuning techniques such as LoRA or QLoRA.
Understanding of traditional NLP concepts including tokenization, semantic similarity, entity extraction, summarization, and transformer fundamentals.
Experience operating in regulated, high-stakes environments; healthcare exposure or standards such as FHIR is a plus, not required.
Proven habit of staying current with AI research, benchmarks, and emerging engineering patterns.

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Today

Generative AI Engineer III

Deloitte Seattle, WA

Seattle, WA

Today

$111k - $218k/yr Full time Ai Ml Artificial Intelligence Big Data

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Job Description

Based onsite in Deloitte's Seattle, WA office, the Generative AI Engineer III role centers on turning ideas into scalable AI solutions. You will design, implement, test, and deploy ML and AI systems for business and client use, build data pipelines and production-grade components, and collaborate with cross-functional teams to improve model performance and scalability. The position offers a salary range of USD 110,700 to 218,300 per year.

Responsibilities

Design, develop, validate, and deploy ML and AI solutions for business and client use cases.
Create and maintain data pipelines and model training workflows, along with production-ready application components that support AI-enabled products.
Examine structured and unstructured data to identify patterns and derive insights that inform model development and validation.
Collaborate with engineers, data scientists, product stakeholders, and business teams to translate requirements into practical technical solutions.
Monitor and troubleshoot model and application performance, implementing enhancements to improve accuracy, reliability, and scalability.

Requirements

Bachelor's degree in computer science, data science, engineering, mathematics, statistics, or a quantitative field.
Five or more years of professional experience designing, developing, deploying, or supporting ML, AI, or advanced analytics solutions.
Three or more years programming with Python, PySpark, PyTorch, and TensorFlow.
A minimum of one year in technology consulting serving state or local government sectors.
Active certification in Python, PySpark, PyTorch, or TensorFlow.
Ability to travel approximately 20 percent of the time, depending on client engagements and sectors served.
Authorized to work in the United States without employer sponsorship now or in the future.

Technologies

Python
PySpark
PyTorch
TensorFlow
Docker
Kubernetes

Benefits

A discretionary annual incentive program awarded based on individual and organizational performance.

The Team

Deloitte's Government & Public Services (GPS) practice focuses on impact through people, ideas, technology, and outcomes. Serving federal, state, and local government clients as well as public higher education institutions, the GPS team brings fresh perspectives to help clients anticipate disruption, reimagine possibilities, and fulfill their mission.

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Today

Senior Legal Specialist, Data Analyst/AI Specialist - Commercial Law Group

The Bank of New York Mellon Corporation Lake Mary, FL

Lake Mary, FL

Today

Full time Analyst Analytics Artificial Intelligence

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Job Description

The Bank of New York Mellon Corporation seeks a Senior Legal Specialist to advance data analytics and AI enabled operations within the Commercial Law Group. This onsite role is based in Lake Mary, Florida.

Responsibilities

Collaborate with attorneys to design, build, and manage BNY Legal's bespoke AI capabilities to streamline legal operations, including creating an Agentic AI workflow to support contracting and negotiations.
Contribute to firmwide AI governance, encompassing intake triage, issue tracking, workflow coordination, and maintaining governance documentation.
Conduct data analytics and generate reports to support transactional work, including extracting, analyzing, and presenting contract insights and trends; partner with attorneys and business clients to maintain and enhance datasets, dashboards, and risk management reporting.
Administer, optimize, and promote adoption of legal technology platforms, including CLM systems, contract repositories, analytics tools, and custom AI solutions.
Engage with a broad range of internal and external stakeholders, including business teams, sourcing, and external counsel, to support contracting processes, vendor management, data quality, and related governance initiatives.

Requirements

Background as a paralegal, analyst, or legal operations professional, ideally within a corporate or financial services setting.
Strong data analytics capabilities, with experience handling structured and unstructured data, spreadsheets, reporting tools, dashboards, or legal analytics platforms.
Demonstrated experience or strong interest in AI, automation, and legal innovation.
High comfort level working with technology platforms and digital workflows.
Strong analytical, organizational, and problem-solving skills, with meticulous attention to detail and a proactive mindset.
Ability to manage multiple priorities in a fast-paced environment.
Effective communicator and collaborator.
Experience supporting contracting, negotiations, or third-party governance is beneficial.

Technologies

Experience with Contract Lifecycle Management (CLM) systems, contract repositories, and analytics tools.

Legal Department Awards

FT Innovative Lawyers North America 2024 – Winner: Innovation in Operational Transformation (adoption of generative AI)
Legalweek 2025 | ALM Law.com – Most Innovative Legal Department of the Year
2025 Pennsylvania Legal Awards – Winner: In-House Impact Award

Firmwide Recognition

America’s Most Innovative Companies, Fortune, 2024
World’s Most Admired Companies, Fortune 2024
Human Rights Campaign Foundation, Corporate Equality Index, 100% score, 2023-2024
Best Places to Work for Disability Inclusion, Disability: IN – 100% score, 2023-2024
“Most Just Companies,” Just Capital and CNBC, 2024
Dow Jones Sustainability Indices, Top performing company for Sustainability, 2024
Bloomberg’s Gender Equality Index (GEI), 2023

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Today

Principal Machine Learning Engineer, Accelerated Apache Spark

NVIDIA Santa Clara, CA

Santa Clara, CA

Today

$272k - $431k/yr Full time Python Big Data Bigdata

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Job Description

NVIDIA seeks a Principal Machine Learning Engineer to guide ML engineering on the GPU-accelerated Apache Spark program in Santa Clara, focusing on ML-driven performance prediction and optimization for Spark workloads. The role also involves building AI-based deployment tools and providing mentorship across environments. This onsite position offers a salary range of USD 272,000 to 431,250 per year.

Role overview

In this capacity, you will lead the ML engineering efforts for a GPU-accelerated Spark initiative, developing ML-based methods to forecast performance and optimize Spark workloads. You will additionally create AI-enabled tools and provide technical mentorship to enable deployment across diverse environments.

Responsibilities

Design and implement ML solutions for forecasting performance and optimizing GPU-accelerated enterprise Apache Spark workloads.
Develop advanced algorithms and adaptive systems to continually enhance Spark performance on GPUs.
Create AI-driven agents and tools to assist with diagnosing system issues and optimizing applications.
Collaborate with partners and customers to deploy complex ML solutions across varied environments.
Maintain deep domain expertise by staying current with the latest advances in ML systems and algorithms.
Provide technical leadership and mentorship in data science and ML for a team of engineers.

Requirements

Education: BS, MS, or PhD or equivalent experience in Machine Learning, Data Science, Computer Science, or a closely related field.
12+ years of professional experience designing, implementing, and productionizing high-quality ML/DL solutions.
5+ years of experience as a technical lead in ML model development.
Proven hands-on experience (2+ years) with large-scale data processing platforms such as Apache Spark.
Demonstrated ability to apply modern tooling and robust practices across all stages of building, deploying, and maintaining ML models.
Excellent programming skills in Python and related data science libraries (NumPy, pandas, scikit-learn, SciPy, PyTorch, TensorFlow).
Deep expertise in advanced ML methodologies, including LLMs/GenAI, reinforcement learning, and adaptive online ML systems.
Strong capability in feature engineering, feature importance assessment, and developing boosted tree models (e.g., XGBoost).

Technologies

Python
NumPy
pandas
scikit-learn
SciPy
PyTorch
TensorFlow
Apache Spark
XGBoost
Scala
Java
C++
CUDA

Benefits

Equity and benefits

Ways to stand out from the crowd

Understanding of the internal workings and architecture of Apache Spark
Familiarity with NVIDIA GPUs and CUDA
Experience coding in Scala, Java, and/or C++

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Today

Senior Machine Learning Engineer, End‑to‑End Autonomous Driving

NVIDIA Santa Clara, CA

Santa Clara, CA

Today

$184k - $357k/yr Full time Senior Active Learning Artificial Intelligence

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Job Description

NVIDIA in Santa Clara is seeking a Senior Machine Learning Engineer to design, train, and deploy end-to-end autonomous driving models while building data-centric pipelines and data flywheels that accelerate development. This onsite role involves close collaboration with researchers and engineers to convert advanced research into robust, production-ready ML systems for autonomous vehicles.

Responsibilities

Design, implement, and train scalable end-to-end driving models for autonomous vehicles.
Drive the data flywheel by identifying failure modes, defining data collection and labeling needs, and iterating models to close real-world performance gaps.
Build, curate, and maintain high-quality multimodal datasets including video, sensor streams, and language/action traces for end-to-end driving.
Develop and apply data-centric learning strategies such as active learning, curriculum learning, automated hard-example mining, outlier and novelty detection, and semi/self-supervised methods.
Explore and productize new data sources, including simulation, synthetic data, and world-model-based generation or augmentation to improve coverage and robustness.
Design and implement automated data workflows that handle discovery, labeling, evaluation, and retraining to maximize development velocity.
Foster collaborative partnerships with researchers and engineers to transform innovative research into robust, production-ready ML models.

Requirements

PhD with 4+ years, MS with 6+ years, or BS (or equivalent) with 8+ years of relevant experience in Computer Science, Computer Engineering, or a related field.
Strong background in modern deep learning, including transformer architectures, video modeling, and multimodal VLM/VLA or foundation models.
Hands-on experience training and deploying deep learning models on real-world datasets, covering preprocessing, distributed training, evaluation, debugging, and iterative improvement.
Practical experience with data-centric methods such as active learning, curriculum learning, outlier/novelty detection, or large-scale sample mining.
Proficiency in Python and at least one major deep learning framework (PyTorch, TensorFlow, or JAX), plus solid software engineering practices (testing, code reviews, CI/CD).
Demonstrated ability to collaborate across teams, drive designs from prototype to production, and communicate with technical and non-technical partners.
Track record of leading complex cross-team projects, setting technical direction, and making critical technical decisions that impact multiple teams or products.

Technologies

Python
PyTorch
TensorFlow
JAX

Benefits

Equity
Benefits

Ways to stand out from the crowd

Experience building and operating data flywheels or large-scale data pipelines for ML, including data quality monitoring and continuous retraining loops.
Direct experience with end-to-end driving models, large-scale behavior cloning, or reinforcement/imitation learning for driving or robotics.
Experience leveraging simulation, synthetic data, or world models to generate training and evaluation data for autonomous systems.
Contributions to advanced data-centric ML methods, VLM/VLA, or autonomous driving through publications, open-source projects, or widely used internal tools.
Background with safety, reliability, and validation requirements for autonomous driving or other safety-critical applications.

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Today

Applied AIML Data Scientist Lead - Vice President

JPMorganChase Jersey City, NJ

Jersey City, NJ

Today

$164k - $260k/yr Full time Manager Ai Ml Analytics

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Job Description

Lead-level Applied AI/ML Data Scientist (Vice President) within JPMorgan Chase's Corporate Technology team, focusing on GenAI and LLM-driven solutions to address complex business problems and communicating insights to non-technical audiences. This onsite role is based in Jersey City, NJ, with a salary range of USD 164,350 to 260,000 per year, and requires a PhD or MS/BS in Computer Science or a related field along with relevant experience.

Responsibilities

Collaborate with product managers, data scientists, ML engineers, and stakeholders to capture requirements and prioritize use cases.
Design GenAI and LLM based solutions to address business challenges.
Apply optimization strategies to tailor generative models for specific GenAI use cases to ensure high-quality outputs.
Manage end-to-end model development workflow, including data wrangling and analysis, model training, testing, and selection.
Generate structured, actionable insights from data analysis and modelling, presenting results in formats appropriate to the audience.
Communicate AI and GenAI capabilities and results to both technical and non-technical audiences.
Monitor evolving AI research trends, implement state-of-the-art techniques, and leverage external APIs to enhance functionality.

Requirements

PhD in Computer Science or a related quantitative discipline with 2+ years of relevant experience, or MS/BS in Computer Science or a related field with 4+ years of relevant experience.
Hands-on experience with LLM projects as well as other supervised and unsupervised techniques; a proven track record deploying AI/ML applications in production.
Proficient in Python and SQL, with practical experience in additional languages such as R and Java.
Demonstrated experience working with large and complex datasets.
Experience with ML frameworks, libraries, and APIs such as TensorFlow, PyTorch, Scikit-learn, and the OpenAI API.
Experience integrating user feedback to drive refinements and self-improving AI applications.
Strong understanding of statistics and machine learning fundamentals, including classification, regression, time series, deep learning, reinforcement learning, and transformer-based generative models.
Ability to identify AI and LLM challenges, implement optimizations, and tune models for optimal NLP performance.
Excellent problem solving, verbal and written communication, and collaboration skills.