Glossary

• Scans resumes for keywords
• Ranks candidates by match score
• Filters out applications not meeting criteria
• Manages applicant pipeline

• Your resume must be ATS-friendly to pass initial screening
• Keywords from job descriptions are critical
• Formatting matters (simple is better)
• Many applications never reach human eyes without ATS approval

• Include exact keywords from job descriptions
• Use standard section headings
• Avoid complex formatting, tables, or graphics
• Submit in requested format (usually PDF or Word)

• Job title and level
• Overview of the role
• Key responsibilities
• Required qualifications
• Preferred qualifications
• Compensation range (sometimes)
• Benefits and perks

• Required: Must-have qualifications (dealbreakers)
• Preferred: Nice-to-have skills (often flexible)
• Responsibilities: Day-to-day work expectations
• Level indicators: Junior, mid, senior, staff

• Unrealistic skill combinations
• Extremely long requirement lists
• Vague responsibilities
• Missing salary information
• “Rockstar” or “ninja” language (potential culture issues)

• Base salary: Your regular paycheck
• Bonus: Annual or performance-based bonuses
• Equity: Stock options, RSUs, or shares
• Benefits: Health insurance, 401k match, etc.
• Perks: Gym membership, meals, learning budget

• Base: $130,000
• Annual bonus: $15,000 (10%)
• RSUs: $40,000/year (vesting over 4 years)
• 401k match: $5,000
• Total TC: ~$190,000

• Some companies pay lower base but higher equity
• Benefits can add significant value
• Compare TC, not just base salary
• Consider tax implications of each component

• Breakdown of base vs. bonus vs. equity
• Vesting schedules for equity
• Value of benefits package
• Refresh grants for equity

• Working hours expectations
• Flexibility for personal needs
• PTO and vacation policies
• On-call requirements
• Remote work options
• Weekend/evening work frequency

• Reasonable hours (40-45/week)
• Flexible scheduling
• Generous PTO (15+ days)
• Minimal on-call
• Respect for personal time
• Mental health support

• “Work hard, play hard” culture
• Regular 60+ hour weeks
• Weekend work expectations
• Always-on availability
• Competitive PTO (must “earn” time off)

• Ask in interviews: “What does a typical week look like?”
• Check employee reviews on Glassdoor
• Research company culture
• Ask about on-call rotation
• Inquire about vacation usage norms

1. Company grants you X shares worth $Y
2. Shares vest over time (typically 4 years)
3. When vested, you own the shares
4. You can sell or hold them
5. Taxed as income when vested

• 4-year with 1-year cliff: Nothing for 1 year, then 25% each year
• 4-year monthly: Vests evenly each month over 4 years
• Front-loaded: More in early years
• Back-loaded: More in later years

• Year 1 (cliff): 2,500 shares vest = $250k
• Years 2-4: 2,500 shares/year = $250k/year
• Actual value depends on stock price when vesting

• Stock price can go up or down
• Taxed as ordinary income when vesting
• May need to sell shares to pay taxes
• Refresh grants may be offered
• Vesting accelerates with acquisition (sometimes)

• Entry-level: 0-2 YOE
• Junior: 1-3 YOE
• Mid-level: 3-5 YOE
• Senior: 5-8 YOE
• Staff/Principal: 8+ YOE
• Lead/Architect: 10+ YOE

• Full-time employment (always)
• Internships (sometimes, usually fractional)
• Relevant side projects (varies)
• Freelance work (usually)
• Academic research (for research roles)

• Unrelated jobs
• Bootcamps or courses
• Personal hobbies
• Pure learning time

• Career changers with transferable skills
• Self-taught developers with portfolios
• Bootcamp grads (may count as entry-level)
• Academic projects for new grads

• Sales roles
• Business development
• Account management
• Customer success (sometimes)

• Base salary: Guaranteed amount
• Variable comp: Commission or bonus based on performance
• OTE = Base + Expected Variable

• Base: $80,000
• Commission (if targets met): $40,000
• OTE: $120,000

• OTE assumes you hit 100% of quota
• Many reps don’t hit quota every period
• Top performers exceed OTE
• Underperformers earn less
• Ask: “What % of reps hit quota?”

• What’s the base/variable split?
• How realistic are the quotas?
• What % of team hits OTE?
• How often do quotas change?
• Is there an earnings cap?

• GPT-4 (OpenAI)
• Claude (Anthropic)
• Gemini (Google)
• Llama (Meta)

• Understand natural language queries
• Generate human-like text
• Answer questions
• Translate languages
• Summarize documents
• Write code

• Understand your job search queries
• Generate conversational answers
• Synthesize information from sources
• Provide career advice
• Extract key information from job postings

• LLM Engineer
• Prompt Engineer
• ML Research Scientist
• Applied AI Engineer

• Supervised learning: Learn from labeled examples
• Unsupervised learning: Find patterns in unlabeled data
• Reinforcement learning: Learn through trial and error
• Deep learning: Neural networks with many layers

• Recommendation systems (Netflix, Amazon)
• Image recognition
• Fraud detection
• Predictive analytics
• Natural language processing

• Machine Learning Engineer
• Data Scientist
• ML Research Scientist
• Applied Scientist
• AI/ML Platform Engineer

• Python, R
• TensorFlow, PyTorch
• Statistics and math
• Data processing
• Model deployment (MLOps)

• Sentiment analysis
• Named entity recognition
• Machine translation
• Text summarization
• Question answering
• Chatbots and conversational AI

• Transformers (BERT, GPT)
• Word embeddings
• Language models
• Tokenization
• Part-of-speech tagging

• Virtual assistants (Siri, Alexa)
• Email spam filtering
• Autocomplete and suggestions
• Document search
• Translation services

• NLP Engineer
• Computational Linguist
• Research Scientist (NLP)
• Applied NLP Scientist
• Conversational AI Engineer

• Image classification
• Object detection
• Facial recognition
• Semantic segmentation
• Image generation
• Video analysis

• Convolutional Neural Networks (CNNs)
• Object detection models (YOLO, R-CNN)
• Generative models (GANs, Diffusion)
• Image segmentation
• Feature extraction

• Self-driving cars
• Medical imaging
• Security systems
• Augmented reality
• Quality control in manufacturing

• Computer Vision Engineer
• CV Research Scientist
• Robotics Engineer
• Autonomous Systems Engineer
• Applied Vision Scientist

1. Agent takes action in environment
2. Receives reward or penalty
3. Updates strategy
4. Repeats to maximize cumulative reward

• Agent: The learner/decision maker
• Environment: What the agent interacts with
• State: Current situation
• Action: What the agent can do
• Reward: Feedback signal

• AlphaGo (board game mastery)
• Robotics control
• Autonomous vehicles
• Resource optimization
• Game AI

• Reinforcement Learning Engineer
• Robotics Engineer
• Game AI Developer
• RL Research Scientist
• Autonomous Systems Engineer

• OpenAI Gym
• Ray RLlib
• Stable Baselines
• TF-Agents

• Model deployment and serving
• Continuous training pipelines
• Model monitoring and logging
• Version control for models and data
• A/B testing for models
• Performance optimization
• Infrastructure management

• Deployment: Kubernetes, Docker, SageMaker
• Monitoring: Prometheus, Grafana
• Experiment tracking: MLflow, Weights & Biases
• Feature stores: Feast, Tecton
• Orchestration: Airflow, Kubeflow

• MLOps Engineer
• ML Platform Engineer
• ML Infrastructure Engineer
• DevOps Engineer (ML focus)

• Cloud platforms (AWS, GCP, Azure)
• Containerization (Docker, Kubernetes)
• CI/CD pipelines
• Python and scripting
• Monitoring and logging
• ML frameworks

1. User asks a question
2. System searches relevant documents/data
3. Retrieved information provided to LLM
4. LLM generates answer using retrieved context
5. Citations to sources included

• More accurate than pure LLM generation
• Reduces hallucinations
• Provides verifiable sources
• Can use up-to-date information
• Domain-specific knowledge

• Retrieves real-time job postings
• Searches company information
• Pulls salary data
• Generates answers with citations
• Provides sources for verification

• Retrieval: Search/database (vector DB, keyword search)
• Augmentation: Adding context to prompt
• Generation: LLM creates response

• Applied AI Engineer
• LLM Engineer
• Search Engineer
• ML Engineer

• Job title: Position name
• Company: Employer information
• Location: On-site, remote, or hybrid
• Key requirements: Essential skills and qualifications
• Salary range: When available from source
• Posting date: How recent the listing is
• Source link: Direct link to apply

• Scan quickly to evaluate fit
• Click for full details
• Click source link to apply
• Use for comparing multiple opportunities

• View full job description
• Visit company profile
• Apply via source link
• Save to favorites (with account)
• Share or export

“The average salary for data scientists in Boston is $115,000 [1], with top companies paying up to$160,000 [2].”

• Verification of AI claims
• Links to original sources
• Transparency about information origin
• Publication dates
• Source credibility

• Prevent AI hallucinations
• Allow you to verify important details
• Build trust through transparency
• Enable deeper research
• Show data recency

1. Click citation number
2. Review original source
3. Verify information accuracy
4. Check publication date
5. Cross-reference if needed

• “What about remote positions?” (after initial search)
• “Show me only senior-level roles” (narrowing results)
• “Which of these pay over $120k?” (adding filter)
• “Tell me about company culture at [Company]” (deeper research)

• No need to repeat full criteria
• AI remembers conversation context
• Iteratively refine results
• Explore related topics
• Natural conversation flow

• Filtering: Add or remove criteria
• Exploratory: Learn about market or roles
• Comparative: Compare options
• Clarifying: Get details on specific results

• Build on previous questions
• Use natural pronouns (“these”, “that”, “ones”)
• Ask one thing at a time
• Pivot when needed

• Top remote roles: Most popular remote positions
• In-demand skills: Jobs requiring trending technologies
• High-growth roles: Positions with increasing demand
• New postings: Recently added opportunities
• Trending companies: Employers actively hiring

• Industry sector
• Experience level
• Salary range
• Location preferences
• Company size
• Technologies used

• Discover trending opportunities
• See what’s in-demand
• Identify skill gaps
• Find active hirers
• Explore new roles

• High application volume
• Multiple openings at growing companies
• Emerging technologies or roles
• Frequent new postings
• Industry growth indicators

• Overview: Company mission and description
• Culture: Work environment and values
• Reviews: Employee ratings and feedback
• Benefits: Perks and compensation info
• Open positions: Current job listings
• Tech stack: Technologies used
• News: Recent company updates
• Diversity: D&I initiatives and data

• Click company name on job cards
• Search: “Tell me about [Company Name]”
• Browse trending companies
• From company-specific questions

• Work culture and environment
• Interview process insights
• Growth trajectory
• Remote work policies
• Team size and structure
• Career development opportunities

• Pre-interview research
• Comparing multiple offers
• Building target company list
• Understanding company values
• Evaluating cultural fit

• Average salaries by role and location
• Year-over-year salary changes
• Comparison across cities
• Impact of skills on compensation
• Industry-specific trends
• Total compensation breakdowns

• Job postings with listed salaries
• Salary survey databases
• Government labor statistics
• Crowdsourced reports
• Company-reported ranges

• “What’s the average salary for [role] in [location]?”
• “How do Python skills affect ML engineer salaries?”
• “Compare software engineer salaries: SF vs. Seattle”
• “Is $95k competitive for junior data analyst in Chicago?”

• Base salary: Regular pay
• Total comp: Base + bonus + equity + benefits
• Percentiles: 25th, 50th (median), 75th, 90th
• Ranges: Typical low to high

• Salary negotiation preparation
• Career planning
• Location comparison
• Skill value assessment
• Offer evaluation

• Backend engineers
• Full-stack developers
• Integration specialists
• Platform engineers

• REST: Most common web API style
• GraphQL: Query language for APIs
• gRPC: High-performance RPC framework
• WebSocket: Real-time bidirectional communication

• API design and development
• API documentation
• Authentication (OAuth, JWT)
• Rate limiting
• Versioning strategies

• Salesforce (CRM)
• Slack (communication)
• Zoom (video conferencing)
• Notion (productivity)
• GitHub (code hosting)

• Often venture-backed
• Subscription revenue model
• Fast-paced growth environments
• Customer-focused culture
• Metrics-driven (ARR, MRR, churn)

• SaaS Account Executive
• Customer Success Manager
• Product Manager
• Growth Engineer
• Solutions Architect

• Entry-level / Junior IC
• Mid-level IC
• Senior IC
• Staff IC
• Principal IC
• Distinguished IC / Fellow

• IC: Deep technical work, no direct reports
• Manager: Team leadership, people management
• Many companies offer parallel IC and management tracks

• Staff Engineer
• Principal Engineer
• Distinguished Engineer
• Research Scientist
• Lead Data Scientist (sometimes)

• Can advance without managing people
• Remain hands-on with technical work
• Influence through expertise
• High compensation at senior IC levels

• High compensation (often $200k-$500k+ TC)
• Competitive hiring (difficult interviews)
• Strong brand recognition
• Complex technical challenges at scale
• Excellent benefits and perks

• Multiple rounds (4-6+)
• Algorithm and data structures focus
• System design (for senior roles)
• Behavioral questions
• Months-long process

• Top compensation
• Resume prestige
• Learning from experts
• Cutting-edge technology

• High pressure
• Work-life balance challenges
• Bureaucracy at scale
• Limited individual impact