Applied Ai Engineer

Applied Ai Engineer Jobs

5 jobs found

Today

Lead AI Engineer

Qualified Health PBC Palo Alto, CA

Palo Alto, CA

Today

$230k - $350k/yr Full time Agentic Ai Ai Agent Ai Agent Platform

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Job Description

Qualified Health PBC is building agentic AI systems that support its full product portfolio, and you will help set the technical direction for how that work scales. This Lead AI Engineer role is based in Palo Alto, CA (onsite) and comes with a USD 230,000 - 350,000 salary range per year, plus equity packages.

You will partner with founders and engineering leadership to connect AI infrastructure investment to company strategy. The focus is both hands-on and organizational: defining multi-year architecture, establishing standards for LLM evaluation and deployment safety, and mentoring staff-level engineers as the applied AI function grows.

What you’ll do

Define the multi-year technical strategy and architecture for agentic workflow infrastructure across the QH product portfolio, supporting 100+ workflows and growing.
Shape decisions on product direction, build-versus-buy, and platform investment by representing applied AI engineering at cross-functional and leadership levels.
Set org-wide engineering standards for LLM evaluation, deployment, safety, and monitoring, contributing to QH’s broader AI governance approach.
Serve as a technical escalation point for the hardest and highest-ambiguity problems across multiple product lines.
Mentor and develop Staff-level engineers, including support for the technical career ladder for applied AI roles.
Stay ahead of model and tooling changes by evaluating emerging options such as OpenAI, Anthropic, and Mistral, and determining what is worth adopting.

Requirements

Python required with a strong foundation in software engineering and demonstrated experience architecting systems used company-wide.
8+ years building production software, including 4+ years building and scaling AI-powered or LLM-driven applications in production.
Proven track record setting technical direction and standards adopted across multiple teams, not limited to a single product line.
Deep expertise in MLOps, model deployment, and workflow automation at an organizational or platform level.
Ability to operate with full autonomy on ambiguous, high-stakes technical and strategic problems.
Strong influence at the leadership level, including with non-technical stakeholders.

Technologies

Python
LLM
MLOps
OpenAI
Anthropic
Mistral

Benefits

Competitive salaries with equity packages
Robust medical/dental/vision insurance
Flexible working hours
Hybrid work options
Inclusive environment that fosters creativity and innovation
Eligible for equity and benefits

Desirable skills

Bachelor’s degree + 10 years OR Master’s degree + 8 years in Computer Science, Engineering, or related field (or equivalent experience).
Recognized expertise in applied AI or agentic systems (internally or externally), such as talks, publications, open source contributions, or prior platform-level roles.
Experience building AI infrastructure in a regulated or high-stakes domain (healthcare, finance, etc.).
Experience founding or scaling a technical function from the ground up in a startup environment.
Experience mentoring Staff-level engineers and shaping technical career ladders.

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2d ago

AI Engineer

Better Direct AI Tempe, AZ

Tempe, AZ

2d ago

$60k - $70k/yr Full time Backend Developer Agentic Ai Ai Agent

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Job Description

Better Direct AI is seeking an AI Engineer to build and deliver AI-powered features from early concept through production. The work centers on turning documents and business workflows into reliable tools for sales and engineering teams, with ownership across models, data pipelines, APIs, and user-facing experiences.

Key Responsibilities

Design document-processing pipelines that convert RFQ PDFs, scanned pages, spreadsheets, and emails into structured outputs such as part numbers, quantities, manufacturers, and shipping addresses.
Run, optimize, and monitor self-hosted models, including self-hosted LLMs, vision-language models, OCR systems, embedding models, and smaller task-specific models on on-premises NVIDIA GPUs within a Kubernetes-based AI cluster.
Build search and RAG systems for contracts, product catalogs, and historical quotes using PostgreSQL, pgvector, BM25, vector retrieval, reranking, and deduplication.
Create tool-using agents that research products across distributor catalogs, including TD SYNNEX, Ingram Micro, and D&H, and prepare quotes for human review.
Develop classifiers, NER models, sequence-labeling models, and other efficient alternatives when large language models are too slow or costly.
Produce labeled evaluation datasets, regression tests, and performance benchmarks measuring accuracy, recall, latency, reliability, and cost.
Implement and maintain Python services using FastAPI, asynchronous processing, Celery, and Redis for extraction, crawling, polling, and other background workloads.
Integrate AI output into the CRM using Express and Prisma APIs, PostgreSQL schema changes and migrations, and React interfaces.
Embed AI results into production workflows with confidence scores, human review, feedback capture, permissions, and audit trails.
Deploy and maintain services using Docker, Git, and documented runbooks. Monitor production jobs, diagnose failures, and support features after release.
Write clear pull requests, design notes, test plans, and handoff documentation to support ongoing maintenance by other engineers.

Required Qualifications

U.S. citizenship.
At least 1 year of experience building application features powered by LLMs or other machine-learning models used in real workflows.
Strong Python skills, including experience with FastAPI (or a similar framework), asynchronous programming, packaging, and automated testing.
Working knowledge of TypeScript or JavaScript, and experience building REST APIs or React applications, or the ability to learn these quickly.
Strong SQL fundamentals and PostgreSQL expertise, including schema design, migrations, indexing, and query optimization.
Hands-on experience in at least two of the following areas:
- RAG, embeddings, or semantic search
- Document extraction or OCR
- Agent tools, prompting, or workflow orchestration
- Model fine-tuning or training
- Model serving with vLLM, SGLang, or a similar platform
- Creating labeled test sets, selecting appropriate metrics, and objectively measuring performance improvements
- Working comfort with Linux, Git, and Docker
- Clear written and verbal communication, including the ability to explain technical trade-offs to non-technical stakeholders

Technologies

Python, FastAPI
TypeScript, JavaScript, REST, React
SQL, PostgreSQL, pgvector, BM25
PostgreSQL schema changes and migrations
Express, Prisma
Docker, Git, Linux, Kubernetes
NVIDIA GPUs, Celery, Redis
vLLM, SGLang, OpenAI Codex
OCR, embeddings, RAG, vector retrieval, reranking, deduplication
TD SYNNEX, Ingram Micro, D&H
KServe, Docling, Selenium, Playwright, MinIO, CUDA

Benefits

401(k)
Dental insurance
Health insurance
Life insurance
Paid time off
Professional development assistance
Retirement plan
Vision insurance

AI-Assisted Engineering

AI-assisted development is part of how the team works. Engineers may use tools such as OpenAI Codex to research systems, explore solutions, write code, generate tests, debug problems, and produce documentation. Tool output supports engineering tasks, while the engineer remains responsible for the final result.

Translate unclear business requests into well-defined technical tasks and acceptance criteria.
Provide AI tools with appropriate context, constraints, and technical requirements.
Read, understand, explain, and maintain all code submitted.
Identify incomplete, unsafe, inaccurate, or unnecessarily complex generated solutions.
Verify assumptions against the codebase, database, architecture, and application behavior.
Evaluate trade-offs and choose practical, maintainable solutions.
Test permissions, security, data integrity, performance, failure modes, and edge cases.
Use approved tools responsibly when working with company and customer data.
Troubleshoot and support work after release.

Preferred Qualifications

Experience running open-weight models such as Qwen, Llama, or Mistral on self-managed GPUs.
Knowledge of quantization, multi-GPU inference, model optimization, and NVIDIA driver or CUDA troubleshooting.
Kubernetes experience, particularly with KServe or another model-serving platform.
Experience with vision-language models or OCR frameworks such as Docling for document understanding.
Background in traditional NLP techniques such as text classification, named-entity recognition, active learning, weak supervision, or conditional random fields.
Experience using Celery and Redis, Selenium or Playwright, or S3-compatible storage such as MinIO.
Familiarity with IT hardware and software products, manufacturer part numbers, and distributor catalogs.
Contributions to open-source machine learning or developer tooling projects.

What We Value

Systems thinking: understanding how models, services, databases, interfaces, and integrations work together.
Data correctness: preventing duplicate, missing, inconsistent, or unauthorized changes.
Practical problem-solving: identifying root causes and comparing solutions using evidence.
Production ownership: validating releases, monitoring outcomes, and responding when issues occur.
Scalability and maintainability: designing for performance, reliability, and extensibility.
Business understanding: learning why a workflow exists, not only how to implement it.
Good judgment: knowing when to proceed, investigate further, simplify, or ask for help.
Continuous learning: acquiring new skills quickly and taking on greater technical ownership over time.

Location and Compensation

Location: Tempe, AZ (onsite)
Salary: USD 60,000 - 70,000 per year

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2d ago

Legal AI Engineer, Corporate

Debevoise & Plimpton LLP New York, NY

New York, NY

2d ago

$240k - $300k/yr Full time Applied Ai Engineer Artificial Intelligence Law Firm

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Job Description

Debevoise & Plimpton LLP is seeking a Legal AI Engineer, Corporate to join its Practice Systems & Applied AI team. This role supports corporate practice and client engagements by leading client-facing discussions on AI and helping lawyers apply artificial intelligence responsibly within corporate legal workflows.

Role Overview

Working closely with the Head of Practice Systems & Applied AI, the position focuses on translating corporate legal needs into practical, responsible AI use across matters and client teams. The role combines corporate legal subject-matter knowledge with hands-on AI guidance, client training, and support for the firm’s applied AI roadmap.

Key Responsibilities

Collaborate with the Head of Practice Systems & Applied AI to implement the Firm’s AI strategy and expand responsible AI use across corporate practices and client engagements.
Prepare for and lead client-facing presentations, meetings, demonstrations, trainings, and continuing legal education programs covering AI concepts, capabilities, risks, and legal use cases.
Act as a subject-matter expert to help lawyers explain the Firm’s AI capabilities, approach, governance, and experience to clients.
Advise corporate clients on applying AI in legal departments and matter teams, including use-case development, workflow design, adoption considerations, and responsible-use practices.
Partner with Partners, Counsel, and Associates in the Corporate Department to understand legal workflows, identify high-value AI opportunities, and develop practical integration approaches.
Apply knowledge of corporate legal practice to evaluate how AI may support drafting, review, diligence, transactions, fundraisings, M&A, capital markets, private equity matters, and related corporate processes.
Provide hands-on support for lawyers using AI on specific matters, including use-case selection, workflow design, prompt development, quality control, and attorney review.
Develop corporate practice-specific AI guidance, demonstrations, use cases, talking points, playbooks, and training materials for lawyers and client teams.
Contribute AI-related content and presentations to support pitches, requests for proposals, client alerts, panel discussions, and other business development activities.
Educate attorneys on communicating with clients about AI accurately and credibly, including capabilities, limitations, risks, governance, and potential value.
Translate legal and client needs into business and functional requirements for the Applied AI Engineering & Solutions team and other technical stakeholders.
Support evaluation and rollout of new AI tools by assessing relevance to legal practice, developing adoption strategies, and assisting incorporation of approved capabilities.
Contribute to the Applied AI roadmap by identifying corporate practice and client priorities, assessing potential impact, and recommending initiatives for research, development, or implementation.
Prepare communications, briefings, and recommendations for Firm leadership and other stakeholders regarding AI adoption, corporate client activity, practice opportunities, and emerging legal-industry developments.
Stay current on advances in AI, legal technology, legal-service delivery, client expectations, professional responsibility, and industry practices to keep guidance practical and up to date.

Required Qualifications

J.D. required; minimum of three years working as a practicing attorney, legal innovation professional, knowledge management attorney, practice support attorney, or similar law firm professional.
Demonstrated knowledge of corporate legal practices, transactional matter workflows, attorney work product, and delivery of legal services in a corporate practice context.
Proven ability to independently lead presentations, meetings, trainings, and substantive discussions with clients, lawyers, and senior stakeholders.
Experience advising lawyers or clients on AI, legal technology, knowledge management, innovation, legal operations, or technology-enabled legal-service delivery.
Strong working knowledge of generative AI, large language models, prompt design, AI-assisted legal workflows, and the opportunities and limitations of AI in legal practice.
Experience developing legal or business requirements and working with technical teams to design, evaluate, or implement technology solutions.
Understanding of confidentiality, privilege, data governance, professional responsibility, privacy, and ethical considerations associated with the use of AI in legal services.
Proven track record of leading complex, cross-functional initiatives from opportunity identification through implementation, adoption, and ongoing support.
Exceptional presentation and public-speaking skills, including the ability to lead credible, engaging discussions with clients, Partners, Firm leadership, attorneys, and professional staff.
Deep understanding of corporate legal practice areas and the ability to analyze workflows across different corporate matter types, transactions, and stages of legal work.
Strong understanding of AI concepts and legal use cases, with the ability to explain capabilities, limitations, risks, and technical concepts in clear, practical language.
Excellent written and verbal communication skills, including executive briefings, client materials, training content, demonstrations, and practice-specific guidance.
Strong judgment, professional credibility, and interpersonal skills, with the ability to influence senior stakeholders and navigate sensitive considerations.
Ability to translate legal and corporate client needs into clear use cases, functional requirements, implementation priorities, and recommendations for technical teams.
Strong organizational and project-management skills to manage multiple corporate client, practice, training, and implementation initiatives in a fast-paced environment.

Preferred Qualification

Experience supporting client development, pitches, requests for proposals, client training, or other business development initiatives.

Technologies

Generative AI
Large language models
Prompt design
AI-assisted legal workflows

Location & Compensation

Location: New York, NY (onsite)
Salary: USD 240,000 - 300,000 per year

How to Apply

A resume is required to apply.
Please include salary requirements and indicate where you saw the position posted.
Send required materials to: Human Resources; jferrigno@debevoise.com; 212.909.8310.

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3d ago

Senior Applied AI Engineer

Ledgent Technology La Jolla, CA

La Jolla, CA

3d ago

$140k - $180k/yr Full time Senior Applied Ai Engineer Artificial Intelligence

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Job Description

Ledgent Technology is hiring a Senior Applied AI Engineer in the La Jolla/Torrey Pines area for a hybrid role. This position focuses on building RAG (Retrieval-Augmented Generation) pipelines and integrating AI capabilities with internal systems and data sources, with frequent communication that supports executive decision-making.

The role reports into the Director of Data Science and sits within a data science team of approximately 6 to 7 people. The schedule is hybrid with T/W/TH in and M/F out, and there is no relocation assistance available.

What you’ll do

Build and maintain RAG pipelines for retrieval and generation workflows
Integrate AI solutions with internal systems and data sources
Present ideas clearly through strong communication and presentation skills
Explain to executives why a solution should be built
Communicate business value and impact in executive-facing discussions
Work independently as a self-starter with loosely defined requirements
Operate effectively in a fluid, fast-changing environment

What you’ll bring

Experience building RAG (Retrieval-Augmented Generation) pipelines
Ability to connect AI solutions to internal systems and data sources
Strong communication and presentation skills
Comfort speaking with executives, including explaining why to build a solution
Comfort presenting business value and impact
Ability to work independently with loosely defined requirements
Comfort working in a fluid, fast-changing environment
Degree is a plus (not required)

Tools and technologies

OpenAI, Claude, Gemini
Microsoft Copilot Studio
LangChain
Snowflake
Python

Compensation and interview approach

Salary: USD 140,000 - 180,000 per year
Bonus: 10%

The interview process includes a virtual HR screen, a virtual interview with the hiring manager, a take-home skills assessment, and a final interview with the Chief Medical Officer.

Take-home assessment is a practical AI exercise
Candidates choose from 3 project options
Estimated completion time: up to 3 hours
One week is provided to complete the assessment

Client note: no C2C and no 3rd-party candidates. The client cannot sponsor now or in the future.

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6d ago

Applied AI Engineer, Startups (Life Sciences)

Anthropic San Francisco, CA

San Francisco, CA

6d ago

$280k - $320k/yr Full time Applied Ai Engineer Artificial Intelligence

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Job Description

Build Claude-based solutions with life sciences and biotech startups, helping move from prototypes to trusted scientific workflows.

Responsibilities

Act as a deep technical advisor to life sciences and biotech startups, supporting use cases where Claude is embedded in the product or applied to how discoveries are made
Work hands-on with startup engineering teams and scientists using pair programming and architecture reviews
Map scientific workflows end to end so guidance aligns with real research pipelines
Develop prototypes and technical documentation, including evaluation suites, scientifically grounded benchmarks, AI engineering techniques, and architecture diagrams
Create reference implementations and sample code to help teams adopt solutions without starting from scratch, including example MCP servers for domain sources (genomics platforms, literature databases, experimental repositories) and agent skills for common scientific workflows
Partner with Applied AI Architects to maintain context and continuity across customer engagements
Spot recurring deployment challenges in life sciences, such as heterogeneous data, auditability requirements, and the gap between a working prototype and what scientists trust
Share insights across Product, Engineering, Research, and the broader Applied AI team, including feedback on model capabilities and product features that accelerate discovery work
Produce technical content for startup audiences, including documentation, tutorials, and sample code
Represent Anthropic in the life sciences startup ecosystem through hackathons, webinars, technical office hours, in-person founder events, and research show-and-tells
Travel to customer sites for workshops, implementation guidance, and relationship building

Requirements

Deep experience in life sciences or biotech through research or industry work in areas such as genomics, drug discovery, molecular design, lab automation, or clinical development
Strong personal perspective on bridging what AI can do today with what life sciences teams need
4+ years of technical experience as a software engineer, ML or research engineer, computational biologist, research scientist, or technical founder
Production experience building LLM-powered applications, including prompting, context engineering, agent architectures, evaluation frameworks, and deployment at scale, or a proven track record applying AI to real scientific work in labs or research settings
Experience with startups or high-growth companies, with an interest in working with founders and small teams that move quickly
Strong technical communication skills for translating AI concepts into architectural decisions and actionable implementation plans for both engineering and scientific audiences
Ability to run technical workshops, hackathons, or developer-focused events
Collaborative working style across teams, comfort context-switching across scientific domains and use cases, and a commitment to making powerful technology safe and beneficial

Benefits

Competitive compensation and benefits
Optional equity donation matching
Generous vacation and parental leave
Flexible working hours
Lovely office space for collaboration

Location and Salary

Location: San Francisco, CA (hybrid)
Annual salary: USD 280,000 - 320,000

Logistics

Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience
Required field of study: A field relevant to the role, demonstrated through coursework, training, or professional experience
Minimum years of experience: Years required correlate with internal job level requirements
Hybrid policy: Expect staff to be in one of the offices at least 25% of the time; some roles may require more
Visa sponsorship: Visa sponsorship is available, and if an offer is made, reasonable efforts will be made to support immigration with an immigration lawyer

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Applied Ai Engineer Jobs

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