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Overview

JobLeap offers specialized search capabilities for AI and machine learning professionals, with deep understanding of subfields, technical stacks, research methodologies, and the nuances between academic and industry roles.

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AI Subfield Filters

Find opportunities in your specific area of AI expertise:

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Major AI Subfields

  • Natural Language Processing
  • Computer Vision
  • Reinforcement Learning
  • Generative AI
  • Speech & Audio
  • Time Series & Forecasting
NLP Focus Areas:
  • Large Language Models (LLMs)
  • Text classification and sentiment analysis
  • Machine translation
  • Question answering systems
  • Named entity recognition
  • Text generation and summarization
  • Dialogue systems and chatbots
  • Information extraction
Common Requirements:
  • Transformers architecture expertise
  • Experience with BERT, GPT, T5, or similar
  • Understanding of attention mechanisms
  • Tokenization and embedding techniques
  • Sequence-to-sequence models
Example Queries:
"NLP engineer jobs working on LLMs"
"Senior NLP research scientist positions"
"Dialogue systems ML engineer roles"
"Transformer model specialist positions"

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Searching by Subfield

"Computer vision engineer jobs in San Francisco"
"NLP researcher positions at AI labs"
"Reinforcement learning roles remote"
Find roles using your preferred ML frameworks and tools:

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Deep Learning Frameworks

PyTorch Ecosystem:
  • Core PyTorch
  • PyTorch Lightning
  • Hugging Face Transformers
  • TorchVision, TorchAudio
  • PyTorch Geometric
  • ONNX export
Typical Roles:
  • Research scientist positions
  • ML engineer at research-focused companies
  • Startup ML roles
  • Computer vision specialists
Search Examples:
"PyTorch ML engineer jobs"
"PyTorch Lightning researcher positions"
"Computer vision engineer PyTorch"
PyTorch is dominant in research and increasingly in production. Emphasize if you have deployment experience with PyTorch models.
TensorFlow Ecosystem:
  • TensorFlow 2.x
  • Keras high-level API
  • TensorFlow Serving
  • TensorFlow Lite (mobile)
  • TensorFlow.js
  • TensorFlow Extended (TFX)
Typical Roles:
  • Production ML engineer
  • ML infrastructure positions
  • Large-scale deployment roles
  • Enterprise ML positions
Search Examples:
"TensorFlow ML engineer production"
"Keras deep learning engineer jobs"
"TensorFlow Serving specialist"
TensorFlow remains strong in production environments, especially at large companies. Highlight TFX or Serving experience for infrastructure roles.
JAX Ecosystem:
  • Core JAX
  • Flax
  • Haiku
  • Optax
  • Automatic differentiation
  • XLA compilation
Typical Roles:
  • Research scientist (Google, DeepMind)
  • High-performance computing ML
  • Advanced research positions
  • Scientific computing roles
Search Examples:
"JAX ML researcher positions"
"Flax deep learning engineer"
"High-performance ML JAX"
JAX is preferred in cutting-edge research. Highlight for roles at Google, DeepMind, or research-heavy startups.
Hugging Face Tools:
  • Transformers library
  • Datasets
  • Tokenizers
  • Accelerate
  • PEFT (Parameter-Efficient Fine-Tuning)
  • Diffusers
Typical Roles:
  • NLP engineer
  • LLM specialist
  • Generative AI engineer
  • Model fine-tuning roles
Search Examples:
"Hugging Face Transformers engineer"
"LLM fine-tuning specialist"
"NLP engineer Hugging Face stack"

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MLOps and Deployment Tools

Model Serving

Tools:
  • TensorFlow Serving
  • TorchServe
  • ONNX Runtime
  • Triton Inference Server
  • BentoML
Roles:
  • ML infrastructure engineer
  • MLOps specialist
  • Production ML engineer

Orchestration

Tools:
  • Kubeflow
  • MLflow
  • Airflow
  • Metaflow
  • Prefect
Roles:
  • ML platform engineer
  • Data engineer ML focus
  • MLOps engineer

Experiment Tracking

Tools:
  • Weights & Biases
  • MLflow
  • Neptune.ai
  • Comet
  • TensorBoard
Roles:
  • ML researcher
  • Research engineer
  • Applied scientist

Feature Stores

Tools:
  • Feast
  • Tecton
  • Hopsworks
  • AWS SageMaker Feature Store
Roles:
  • ML platform engineer
  • Feature engineering specialist
  • Data engineer for ML
"PyTorch ML engineer remote"
"TensorFlow production engineer SF"
"JAX researcher positions"

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Model Type Specialization

Search by the types of models you work with:
  • Large Language Models
  • Diffusion Models
  • Recommendation Systems
  • Graph Neural Networks
LLM Specializations:
  • Pre-training large models
  • Fine-tuning and adaptation
  • RLHF and alignment
  • Prompt engineering
  • RAG (Retrieval-Augmented Generation)
  • In-context learning
  • Few-shot learning
  • Model compression
Typical Requirements:
  • Transformer architecture expertise
  • Distributed training (FSDP, DeepSpeed)
  • Tokenization strategies
  • Evaluation metrics for LLMs
  • Production LLM deployment
Search Examples:
"LLM engineer fine-tuning positions"
"Large language model researcher"
"RLHF alignment engineer jobs"
"Prompt engineering specialist"
"RAG system engineer positions"
Hot skills for LLM roles:
  • RLHF and constitutional AI
  • Efficient fine-tuning (LoRA, QLoRA)
  • LLM evaluation and benchmarking
  • Prompt optimization
  • Vector databases for RAG

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Research vs. Applied Roles

Understanding the spectrum between research and production:

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Research-Focused Positions

Responsibilities:
  • Novel algorithm development
  • Publishing papers at top conferences
  • Exploring cutting-edge techniques
  • Proof-of-concept implementations
  • Collaborating with academia
Requirements:
  • PhD (usually required)
  • Publication record
  • Strong theoretical foundations
  • Expertise in specific subfield
  • Research methodology skills
Where to find:
  • Large tech companies (Google, Meta, Microsoft)
  • AI research labs (DeepMind, OpenAI, Anthropic)
  • University research groups
  • R&D divisions
Search Examples:
"Research scientist NLP positions"
"AI research scientist ML theory"
"Computer vision researcher PhD"
Research scientist roles typically require a PhD and publication history. Emphasize your papers, citations, and conference presentations.
Responsibilities:
  • Applying research to products
  • Prototyping new features
  • Experimenting with SOTA methods
  • Some publication expectations
  • Bridge between research and engineering
Requirements:
  • MS or PhD (often preferred)
  • Balance of theory and practice
  • Prototyping skills
  • Production awareness
  • Experimentation methodology
Where to find:
  • Product companies (Amazon, Microsoft)
  • AI-first startups
  • Research-heavy product teams
Search Examples:
"Applied scientist ML positions"
"Applied research engineer jobs"
"Applied ML scientist Amazon"
Responsibilities:
  • Implementing research ideas
  • Building research infrastructure
  • Scaling experiments
  • Reproducibility and tooling
  • Supporting researchers
Requirements:
  • Strong engineering skills
  • ML framework expertise
  • High-performance computing
  • Research paper implementation
  • Software engineering best practices
Where to find:
  • Research labs needing engineering support
  • Companies building ML platforms
  • Research-heavy startups
Search Examples:
"Research engineer ML infrastructure"
"Research software engineer AI"
"ML research engineer PyTorch"

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Production-Focused Positions

Responsibilities:
  • Deploying models to production
  • Building ML pipelines
  • Model optimization and serving
  • Monitoring and maintenance
  • Scaling ML systems
Requirements:
  • Strong software engineering
  • Production ML experience
  • Cloud platforms (AWS, GCP, Azure)
  • MLOps tools and practices
  • System design skills
Where to find:
  • Product companies
  • Startups in production phase
  • Enterprise companies
  • SaaS companies
Search Examples:
"ML engineer production systems"
"Production ML engineer AWS"
"ML engineer deployment focus"
Production ML skills:
  • Model serving and optimization
  • A/B testing and experimentation
  • Monitoring and observability
  • CI/CD for ML
  • Data pipeline engineering
Responsibilities:
  • Building ML infrastructure
  • Creating internal ML tools
  • Supporting ML teams
  • Standardizing ML workflows
  • Scaling ML capabilities
Requirements:
  • Platform engineering expertise
  • Kubernetes and containerization
  • ML frameworks knowledge
  • Infrastructure as code
  • Developer experience focus
Where to find:
  • Companies with large ML teams
  • ML-first organizations
  • Tech companies scaling ML
Search Examples:
"ML platform engineer positions"
"ML infrastructure engineer"
"ML tooling engineer jobs"
Responsibilities:
  • Solving business problems with ML
  • Building predictive models
  • A/B testing and experimentation
  • Stakeholder communication
  • Model evaluation and selection
Requirements:
  • Statistics and ML fundamentals
  • Python, SQL, and data tools
  • Business acumen
  • Communication skills
  • End-to-end project ownership
Where to find:
  • Product companies
  • Business analytics teams
  • Consulting firms
  • Industry-specific companies
Search Examples:
"Data scientist machine learning"
"ML data scientist product focus"
"Data scientist predictive modeling"

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Hybrid Roles

Applied ML Engineer

Combines research awareness with production skills
  • Implements papers
  • Experiments with new methods
  • Deploys to production
  • Balances innovation and reliability

ML Research Engineer

Engineering-heavy research role
  • Scales research experiments
  • Builds research infrastructure
  • Implements complex algorithms
  • Bridges research and product
"Research scientist computer vision PhD"
"Applied scientist NLP top conferences"
"Research engineer ML systems"

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Industry-Specific AI Roles

AI opportunities across different industries:
  • Healthcare & Biotech
  • Autonomous Vehicles
  • Finance & Trading
  • Robotics
  • E-commerce & Retail
  • Social Media & Content
Focus Areas:
  • Medical imaging (radiology, pathology)
  • Drug discovery and molecule design
  • Clinical decision support
  • Genomics and bioinformatics
  • Patient outcome prediction
  • Electronic health records analysis
Unique Requirements:
  • HIPAA compliance knowledge
  • Medical domain expertise
  • Clinical validation process
  • FDA approval awareness
  • Interpretability requirements
Search Examples:
"Medical imaging ML engineer"
"Drug discovery AI scientist"
"Healthcare ML engineer HIPAA"
"Bioinformatics ML specialist"
Healthcare AI roles often require domain knowledge and understanding of regulatory requirements. Highlight any healthcare or biology background.

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Advanced Search Techniques

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Combining Multiple Criteria

"Computer vision engineer PyTorch"
"NLP researcher Hugging Face Transformers"
"RL engineer JAX experience"

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Filtering by Paper Topics

For research-focused roles, search by paper topics: 
"Transformer architecture researcher"
"Attention mechanism ML scientist"
"Self-supervised learning positions"
"Contrastive learning researcher"
"Meta-learning ML engineer"
"Few-shot learning specialist"

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Searching by Company AI Focus

AI Research Labs

OpenAI, Anthropic, DeepMind, FAIR, MSR, Google Brain

AI-First Startups

Companies building AI products as core business

AI Teams at Tech Giants

Meta AI, Google AI, Amazon Science, Apple ML

Industry AI Labs

Companies applying AI to traditional industries
"AI researcher positions OpenAI Anthropic"
"ML engineer AI-first startups"
"Research scientist Google DeepMind"

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Skills to Highlight

For research roles:
  • Publication record (conferences: NeurIPS, ICML, ICLR, CVPR, ACL)
  • Paper implementation from scratch
  • Novel algorithm development
  • Theoretical understanding
  • Experimental design
  • Research collaboration
How to highlight:
  • List top-tier publications
  • Mention citation counts
  • Describe research contributions
  • Highlight novel insights
For production roles:
  • Deployed ML models at scale
  • MLOps and CI/CD for ML
  • Model optimization and compression
  • Production monitoring
  • A/B testing and experimentation
  • Cloud platform expertise
How to highlight:
  • Quantify scale (users, QPS)
  • Mention uptime and reliability
  • Describe optimization impact
  • Show business metrics improved
Deep knowledge of:
  • Multiple frameworks (PyTorch, TensorFlow)
  • Framework-specific optimizations
  • Custom layers/operations
  • Distributed training
  • Model export and serving
  • Framework internals
How to highlight:
  • Years of experience per framework
  • Contributions to open source
  • Advanced features used
  • Performance optimizations
Industry-specific:
  • Healthcare/medical terminology
  • Financial markets knowledge
  • Robotics fundamentals
  • Autonomous systems
  • Regulatory compliance
  • Safety-critical systems
How to highlight:
  • Prior industry experience
  • Relevant coursework or certifications
  • Domain-specific projects
  • Publications in domain venues

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Career Paths in AI/ML

1

Entry-Level ML Engineer

  • Junior ML engineer
  • ML engineer I
  • Data scientist (ML focus)
  • Research engineer (junior)
Focus: Learn production systems, build foundational skills
2

Mid-Level Specialist

  • ML engineer II/III
  • Applied scientist
  • Senior data scientist
  • Specialized ML engineer
Focus: Develop expertise in subfield, lead projects
3

Senior Technical Role

  • Senior/Staff ML engineer
  • Research scientist
  • Principal applied scientist
  • ML tech lead
Focus: Technical leadership, architecture, mentorship
4

Leadership or Deep Expertise

Management track:
  • ML engineering manager
  • Director of ML
  • VP of AI/ML
IC track:
  • Principal/Staff research scientist
  • Distinguished engineer
  • AI research lead
Career Switch Tips:From Research to Production:
  • Emphasize implementation skills
  • Show production deployment experience
  • Highlight scalability awareness
  • Demonstrate software engineering
From Production to Research:
  • Show paper implementations
  • Highlight novel solutions
  • Demonstrate theoretical depth
  • Emphasize experimentation experience

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Next Steps

Resume Optimization

Optimize your ML resume with technical keywords

Interview Prep

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Skills & Learning

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