AI Engineer, Curator Job at Her (New York)

Applied AI Engineer - AI Solutions

As an Applied AI Engineer, you’ll research and utilize state-of-the-art Gen AI a...

Location

United States , Redwood City; San Francisco

Salary:

172000.00 - 300000.00 USD / Year

Snorkel AI

Expiration Date

Until further notice

Requirements

B.S. degree in a quantitative field such as Computer Science, Engineering, Mathematics, Statistics, or comparable degree/experience
3+ years of customer-facing experience in the design and implementation of AI/ML solutions
Proficiency in Python, including strong grounding in software engineering fundamentals (e.g., modular design, testing, profiling, packaging) and experience with modern Python constructs and libraries for type validation and typed data modeling (e.g., pydantic), building type-safe systems (e.g., mypy), testing (e.g., pytest), packaging and environment configuration (e.g., poetry), API and service frameworks (e.g., FastAPI), serialization and structured data handling (e.g., msgspec), and orchestration tooling relevant to ML deployment (e.g., Ray, Airflow)
Expertise across the Applied AI stack, spanning classical ML libraries (e.g., scikit-learn), deep learning frameworks (e.g., PyTorch), foundation-model ecosystems (e.g., Hugging Face Transformers), vector/embedding tooling (e.g., FAISS), data processing frameworks (e.g., pandas, Spark), retrieval/RAG tooling (e.g., Chroma, Weaviate), synthetic dataset curation, evaluation workflows, and LLM orchestration, workflow, agent authoring tools (e.g., LlamaIndex, LangGraph, CrewAI)
Experience leading strategic, customer-facing initiatives and collaborating with business stakeholders to ensure ML solutions drive successful business outcomes, with a strong focus on teaching and enablement
Outstanding presentation skills to technical and executive audiences, whether impromptu on a whiteboard or using presentations and demos
Ability to work in a fast-paced environment and balance priorities across multiple projects at once

Job Responsibility

Partner with customers to build and deploy impactful Gen AI and machine learning solutions, from use case scoping and data exploration to model development and deployment. This may involve leveraging Snorkel Flow or designing custom approaches using state-of-the-art tools, with the goal of delivering real business value and informing the evolution of the Snorkel platform
Develop and implement state of the art AI systems such as retrieval-augmented generation (RAG), fine-tuning pipelines, prompt engineering recipes and agentic workflows
Create augmented real-world datasets and comprehensive evaluation workflows to ensure model reliability, transparency, and stakeholder trust. A data- and evaluation-first mindset is essential for success in this role
Forge and manage relationships with our customers’ leadership and stakeholders to ensure successful development and deployment of AI projects with Snorkel Flow
Collaborate closely with pre-sales Solutions and Product teams to map customer needs to existing capabilities, prioritize roadmap gaps, and guide successful project setup
Work with other Applied AI Engineers to standardize solutions and contribute to internal tooling and best practices
Lead stakeholder education on quantitative capabilities, helping them to understand the strengths and weaknesses of different approaches and what problems are best-suited for Snorkel AI
Serve as the voice of our customers for new AI paradigms, data science workflows, and share customer feedback to product teams
Conduct one-to-few and one-to-many enablement workshops to transfer knowledge to customers considering or already using Snorkel AI
Annual travel up to 25%

What we offer

equity in the form of employee stock options

Fulltime

AI Research Engineer, VLLM (vision large language models) - Generative AI

Meta is seeking a Research Engineer to join our Llama Large Language Model (LLM)...

Location

United States , Menlo Park

Salary:

217000.00 USD / Year ▼

Ai Engineer

Location

Romania , Bucuresti

Salary:

Not provided

NTT DATA

Expiration Date

Until further notice

Requirements

Bachelor’s degree in Computer Science, Engineering, or related field
At least 3 years of experience as Software Engineer / ML Engineer / AI Engineer, with at least 1-2 years working directly with LLMs in real applications
Strong proficiency in Python (core language features, packaging, testing, async, type hints)
Very strong software engineering practices: version control (Git), unit/integration testing, code reviews, CI/CD
Experience building and consuming REST/gRPC APIs and integrating external tools/services
Understanding of core ML concepts: supervised/unsupervised learning, train/validation/test splits, overfitting, regularization, and common metrics (precision, recall, F1, ROC-AUC, etc.)
Good understanding of deep learning basics (neural networks, embeddings) and at least one ML/DL framework (e.g., PyTorch, TensorFlow, JAX, scikit-learn)
Deep practical knowledge of large language models
Tokenization, context windows, temperature, top-p, system vs user prompts
Prompt engineering patterns (ReAct, chain-of-thought, tool-calling/tool-use)

Job Responsibility

Design and build complex agentic systems with multiple interacting agents
Implement robust orchestration logic (state machines / graphs, retries, fallbacks, escalation to humans)
Implement RAG pipelines, tool calling, and sophisticated system prompts for optimal reliability, latency, and cost control
Apply core ML concepts to evaluate and improve agent performance, including dataset curation and bias/safety checks
Lead the development of agents using Google ADK and/or LangGraph, leveraging advanced features for orchestration, memory, evaluation, and observability
Integrate with supporting libraries and infrastructure (e.g., LangChain/LlamaIndex, vector databases, message queues, monitoring tools) with minimal supervision
Define success metrics, build evaluation suites for agents (automatic + human evaluation), and drive continuous improvement
Curate and maintain comprehensive prompt/test datasets
run regression tests for new model versions and prompt changes
Deploy and operate AI services in production, establishing CI/CD pipelines, observability, logging, and tracing

What we offer

Smooth integration and a supportive mentor
Pick your working style: choose from Remote, Hybrid or Office work opportunities
Our projects have different working hours to suit your needs
Sponsored certifications, trainings and top e-learning platforms
Private Health Insurance
Individual coaching sessions or accredited Coaching School
Epic parties or themed events

Fulltime

Gen AI Engineer

We are currently seeking a Gen AI Engineer to join our team in a hybrid basis ei...

Location

United States , New York/Dallas

Salary:

Not provided

NTT DATA

Expiration Date

Until further notice

Requirements

5+ years of software development in one or more languages (Python, C/C++, Go, Java)
strong hands-on experience building and maintaining large-scale Python applications preferred
3+ years designing, architecting, testing, and launching production ML systems, including model deployment/serving, evaluation and monitoring, data processing pipelines, and model fine-tuning workflows
Practical experience with Large Language Models (LLMs): API integration, prompt engineering, fine-tuning/adaptation, and building applications using RAG and tool-using agents (vector retrieval, function calling, secure tool execution)
Understanding of different LLMs, both commercial and open source, and their capabilities (e.g., OpenAI, Gemini, Llama, Qwen, Claude)
Solid grasp of applied statistics, core ML concepts, algorithms, and data structures to deliver efficient and reliable solutions
Strong analytical problem-solving, ownership, and urgency
ability to communicate complex ideas simply and collaborate effectively across global teams with a focus on measurable business impact

Job Responsibility

Build agentic AI systems: Design and implement tool-calling agents that combine retrieval, structured reasoning, and secure action execution (function calling, change orchestration, policy enforcement) following MCP protocol
Productionize LLMs: Build evaluation framework for open-source and foundational LLMs
implement retrieval pipelines, prompt synthesis, response validation, and self-correction loops tailored to production operations
Integrate with runtime ecosystems: Connect agents to observability, incident management, and deployment systems to enable automated diagnostics, runbook execution, remediation, and post-incident summarization with full traceability
Collaborate directly with users: Partner with production engineers, and application teams to translate production pain points into agentic AI roadmaps
Safety, reliability, and governance: Build validator models, adversarial prompts, and policy checks into the stack
Scale and performance: Optimize cost and latency via prompt engineering, context management, caching, model routing, and distillation
Build a RAG pipeline: Curate domain-knowledge
build data-quality validation framework
establish feedback loops and milestone framework maintain knowledge freshness

Fulltime

Ai Engineer, Quality

Fieldguide is building AI agents for the most complex audit and advisory workflo...

Location

United States , San Francisco

Salary:

Not provided

Helpcare AI

Expiration Date

Until further notice

Requirements

Multiple years of experience shipping production software in complex, real-world systems
Experience with TypeScript, React, Python, and Postgres
Built and deployed LLM-powered features serving production traffic
Implemented evaluation frameworks for model outputs and agent behaviors
Designed observability or tracing infrastructure for AI/ML systems
Worked with vector databases, embedding models, and RAG architectures
Experience with evaluation platforms (LangSmith, Langfuse, or similar)
Comfort operating in ambiguity and taking responsibility for outcomes

Job Responsibility

Design and build a unified evaluation platform that serves as the single source of truth for all of our agentic systems and audit workflows
Build observability systems that surface agent behavior, trace execution, and failure modes in production, and feedback loops that turn production failures into first-class evaluation cases
Own the evaluation infrastructure stack including integration with LangSmith and LangGraph
Translate customer problems into concrete agent behaviors and workflows
Integrate and orchestrate LLMs, tools, retrieval systems, and logic into cohesive, reliable agent experiences
Build automated pipelines that evaluate new models against all critical workflows within hours of release
Design evaluation harnesses for our most complex Agentic systems and workflows
Implement comparison frameworks that measure effectiveness, consistency, latency, and cost across model versions
Design guardrails and monitoring systems that catch quality regressions before they reach customers
Use AI as core leverage in how you design, build, test, and iterate

What we offer

Competitive compensation packages with meaningful ownership
Flexible PTO
401k
Wellness benefits, including a bundle of free therapy sessions
Technology & Work from Home reimbursement
Flexible work schedules

Fulltime

Senior Applied AI Engineer, Image Generation

We’re hiring a Senior Applied AI Engineer, Image Generation to join a fast‑movin...

Location

United States , Redmond

Salary:

119800.00 - 234700.00 USD / Year

Microsoft Corporation

Expiration Date

Until further notice

Requirements

Bachelor's Degree in Computer Science or related technical field AND 4+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience.
Master’s Degree AND 3+ years of experience in engineering, problem solving, model building, evaluation, data analysis OR equivalent experience.
PhD in engineering, applied math, statistics, or related analytical field.
2+ years shipping production-level code, models, or data analysis.
1+ years using AI-assisted coding and analysis techniques.
Solid grasp of deep learning: loss functions, optimization, regularization, training stability
Experience deploying ML models at scale (inference optimization, quantization, distillation)
Familiarity with image preprocessing pipelines, data augmentation, and dataset curation
Experience working on small teams and mid-stage startup environments.
Experience working on AI products.

Job Responsibility

Model Development & Training
Train, fine-tune, and evaluate image generation models (diffusion, GAN, transformer-based)
Implement and adapt techniques from research papers into working production systems
Design and run experiments to improve image quality, diversity, and controllability
Curate, clean, and manage large-scale image-text training datasets
Evaluation, Hillclimbing & Quality Systems
Build and maintain evaluation frameworks for correctness, safety, grounding, and UX quality.
Run hillclimbing loops across prompts, models, and tool‑use strategies to continuously improve assistant performance.
Analyze failure modes, design mitigations, and drive systematic improvements across the stack.
LLM Tooling & Internal Infrastructure

Fulltime

AI Engineer

The AI Engineer role involves designing and building complex agentic systems, im...

Location

Romania , Bucharest

Salary:

Not provided

NTT DATA

Expiration Date

Until further notice

Requirements

Bachelor’s degree in Computer Science, Engineering, or related field
At least 3 years of experience as Software Engineer / ML Engineer / AI Engineer, with at least 1-2 years working directly with LLMs in real applications
Strong proficiency in Python (core language features, packaging, testing, async, type hints)
Very strong software engineering practices: version control (Git), unit/integration testing, code reviews, CI/CD
Experience building and consuming REST/gRPC APIs and integrating external tools/services
Understanding of core ML concepts: supervised/unsupervised learning, train/validation/test splits, overfitting, regularization, and common metrics (precision, recall, F1, ROC-AUC, etc.)
Good understanding of deep learning basics (neural networks, embeddings) and at least one ML/DL framework (e.g., PyTorch, TensorFlow, JAX, scikit-learn)
Deep practical knowledge of large language models
Tokenization, context windows, temperature, top-p, system vs user prompts
Prompt engineering patterns (ReAct, chain-of-thought, tool-calling/tool-use)

Job Responsibility

Design and build complex agentic systems with multiple interacting agents
Implement robust orchestration logic (state machines / graphs, retries, fallbacks, escalation to humans)
Implement RAG pipelines, tool calling, and sophisticated system prompts for optimal reliability, latency, and cost control
Apply core ML concepts to evaluate and improve agent performance, including dataset curation and bias/safety checks
Lead the development of agents using Google ADK and/or LangGraph, leveraging advanced features for orchestration, memory, evaluation, and observability
Integrate with supporting libraries and infrastructure (e.g., LangChain/LlamaIndex, vector databases, message queues, monitoring tools) with minimal supervision
Define success metrics, build evaluation suites for agents (automatic + human evaluation), and drive continuous improvement
Curate and maintain comprehensive prompt/test datasets
run regression tests for new model versions and prompt changes
Deploy and operate AI services in production, establishing CI/CD pipelines, observability, logging, and tracing

What we offer

A smooth integration and a supportive mentor
Pick your working style: choose from Remote, Hybrid or Office work opportunities
Projects have different working hours to suit your needs
Sponsored certifications, trainings and top e-learning platforms
Private Health Insurance
Individual coaching sessions
Accredited Coaching School
Epic parties or themed events

Data engineer AI and Analytics - Vice president

We are seeking a highly skilled and forward-thinking Vice President to lead our ...

Location

India , Pune

Salary:

Not provided

Citi

Expiration Date

Until further notice

Requirements

10+ years of technology experience, with a significant track record in wealth management technology at leading financial institutions
Deep, hands-on experience managing large-scale, high-throughput OLTP databases (e.g., Oracle, PostgreSQL, SQL Server)
Extensive experience with analytical data platforms, data warehousing, and data lakes (e.g., Snowflake, BigQuery, Redshift, Hadoop ecosystem)
Hands-on experience with the data engineering aspects of AI/ML, including feature engineering, data preparation for model training, and deploying models into production pipelines. Familiarity with MLOps principles
Proven track record of designing, building, and operating mission-critical, real-time RESTful APIs at scale
Strong data modeling skills for both transactional and analytical use cases
Expertise with performance measurement and monitoring tools (e.g., AppDynamics, Splunk, Prometheus, Grafana)
Proven ability to lead, mentor, and manage a high-performing engineering team
A strong, demonstrable work ethic and a deep sense of ownership, with a history of seeing complex systems through from conception to long-term operational stability
The ability to maintain a holistic, end-to-end view of a complex data ecosystem, understanding the flow of data from real-time transaction to analytical insight

Job Responsibility

Unified Data Platform Ownership:Lead a team of skilled engineers responsible for the design, development, and 24/7 operation of the unified client and account data platform, encompassing both high-throughput OLTP systems and large-scale OLAP analytical stores
AI & Advanced Analytics Enablement:Pioneer the application of AI/ML models on core client and account data to generate actionable insights, identify patterns, and create intelligent features for advisor and client applications. Partner with data science teams to provide them with high-quality, curated data
Real-Time API Management:Oversee the entire lifecycle of the high-throughput, low-latency APIs that provide real-time data to advisor workstations, client-facing portals, and other critical applications, ensuring they are performant, reliable, and secure
Data Integrity & Scale:Take ultimate responsibility for the integrity, availability, and performance of massive volumes of data across both transactional and analytical systems
Measurement & Analytics:Champion a culture of measurement. Define, monitor, and report on key performance indicators (KPIs) across the entire ecosystem, from database performance and API latency to data quality and the efficacy of AI models
End-to-End Ecosystem View:Serve as the subject matter expert for the entire client and account data ecosystem, from the foundational OLTP databases and real-time APIs to the analytical data warehouses/lakes and the AI/ML models they feed
Technical Strategy:Partner with global architects and business leaders to define the technical roadmap for the core data platforms, including strategies for data replication, ETL/ELT processes, and the integration of AI/ML capabilities

Fulltime

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AI Engineer, Curator

Job Description

Job Responsibility

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What we offer

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