AI Deployment Strategist - Scale AI

DESCRIPTION FORMATTING RULES (CRITICAL - FOLLOW EXACTLY)

Your job is to output clean, readable Markdown while PRESERVING ALL ORIGINAL CONTENT AND STRUCTURE EXACTLY.

ABSOLUTE RULES - NEVER VIOLATE:

1. NEVER add, remove, or modify any text/wording from the original
2. NEVER paraphrase or rewrite sentences - keep exact wording
3. NEVER remove sections, paragraphs, disclaimers, or any content
4. PRESERVE all links exactly as they appear
5. PRESERVE the natural flow and spacing of the original document

MARKDOWN FORMATTING:

• for main section headings (bold standalone titles like "The Role", "You Will", "Requirements")

• for sub-section headings

• NEVER use # (H1) - the job title is already the page H1
• Use "-" bullet lists for ANY list of items
• CRITICAL: If content is clearly a list (requirements, responsibilities, benefits), format as bullet list
• Bold for inline emphasis, key terms, skills - preserve existing bold from original
• Italic for emphasis or disclaimers - preserve existing italic from original
• text for ALL hyperlinks - preserve every link

SPACING RULES:

• Preserve the natural paragraph breaks from the original content when they exist
• IF the original lacks proper spacing (e.g., everything is one giant paragraph), ADD logical breaks:
  • Blank line before each section heading
  • Blank line between distinct topics/sections
  • Blank line before and after bullet lists
• NO blank lines between bullet points in the same list
• NO multiple consecutive blank lines (max 1 blank line between elements)
• The output should be scannable and readable, even if the input wasn't

OUTPUT QUALITY:

• Clean, scannable markdown that matches the original document's flow
• Professional formatting that readers can quickly parse
• All content intact - nothing added, nothing removed

TITLE FORMATTING RULES (CRITICAL)

The "title" field MUST use the EXACT format: "[Job Title] - [Company Name]"

• Always use a HYPHEN (" - "), NEVER "at"
• Example: "Senior ML Engineer - OpenAI" (CORRECT)
• Example: "Senior ML Engineer at OpenAI" (WRONG — never use "at" in the title field)
• Clean up the raw title: fix capitalization, remove company name if already embedded, remove location info
• Keep the job title portion concise but don't lose important qualifiers (e.g., "Senior", "Lead", "Staff")
• The "metaTitle" field uses a DIFFERENT format ("[Job Title] at [Company]") — do NOT confuse the two

SALARY DISPLAY FORMAT (IMPORTANT)

Format salary display as compact "k" notation:

• $187,000 - $220,000 → "$187k - $220k"
• $100,000 - $120,000 USD → "$100k - $120k"
• €80,000 - €100,000 → "€80k - €100k"
• Always use "k" for thousands, remove ".0" decimals
• Include currency symbol at the start, not the end

WORK MODE INFERENCE RULES

Determine workMode using these rules IN ORDER:

1. If explicitly stated ("remote", "hybrid", "on-site", "in-office") in title/description → use that
2. If location contains "Remote" → REMOTE
3. If multiple countries/continents listed → REMOTE (high confidence)
4. If location is specific city only (e.g., "London", "New York", "Tokyo") with no remote mention → ONSITE
5. If "flexible" or "work from home options" mentioned → HYBRID
6. If unclear after all checks → null

EXPERIENCE LEVEL EXTRACTION RULES

Extract experienceLevel using these rules:

1. From title keywords (highest priority):
   • "Intern/Internship" → ENTRY
   • "Junior/Jr/Associate" → JUNIOR
   • "Mid/Mid-level" → MID
   • "Senior/Sr/Staff/Principal" → SENIOR
   • "Lead/Tech Lead/Team Lead" → LEAD
   • "Director" → DIRECTOR
   • "Head of/Head" → HEAD
   • "VP/Vice President/Chief/C-level/CTO/CEO/CFO" → C_LEVEL
2. From years requirement in description:
   • 0-1 years → ENTRY
   • 1-2 years → JUNIOR
   • 3-4 years → MID
   • 5+ years → SENIOR (unless title indicates higher level)
3. If no indicators → null

LOCATION RULES (CRITICAL - keep compact)

Goal: Locations must be SHORT and COMPACT. Max 5 comma-separated values total.

1. REMOTE JOBS:

   • If open to all/most regions → just "Remote"
   • If remote but limited to specific regions → use the CANONICAL REMOTE TOKENS below in parentheses
   • If remote limited to 2 regions → "Remote (US, Europe)" (still 1 badge)
   • NEVER list every individual country/city for remote jobs

2. CANONICAL REMOTE TOKENS (use these EXACT values inside parentheses):

   • United States only → "Remote (US)"
   • US + Canada / North America → "Remote (NA)"
   • Europe / EU / EMEA / DACH / Nordics → "Remote (Europe)"
   • Asia / Asia Pacific / APAC / SEA / ANZ → "Remote (APAC)"
   • Latin America / South America / LATAM → "Remote (LATAM)"
   • Middle East / MENA / GCC → "Remote (MENA)"
   • Africa / Sub-Saharan Africa → "Remote (Africa)"
   • UK only → "Remote (UK)"
   • Specific country → "Remote (Singapore)", "Remote (Germany)", etc.

3. ONSITE/HYBRID with specific cities:

   • For each city, ALSO include the country as a separate entry for discoverability
   • Example: "Dubai" → "Dubai, United Arab Emirates"
   • Example: "London, Berlin" → "London, United Kingdom, Berlin, Germany"
   • If 4+ cities in same country → use country name: "United States" and at most 2 key cities
   • NEVER include both city AND state AND country for the same place (e.g., "New York" not "New York, NY, United States")
   • But DO include city AND country as separate comma entries: "New York, United States"

4. US-SPECIFIC:

   • NEVER list states separately (no "CA, NY, TX, FL...")
   • Many US cities → "United States" or top 2 cities + "United States"
   • Remote in US → "Remote (US)"

5. MAX 5 comma-separated values in the locations field

TAG RULES (CRITICAL - content-derived skills only)

Goal: Tags must be SPECIFIC, ACTIONABLE skills/technologies/tools that appear IN THE JOB DESCRIPTION. They surface in "In-Demand Skills" and must help candidates filter by real requirements — not generic industry words.

FORBIDDEN TAGS (NEVER use these — they are useless on an AI job board):

• "ai", "artificial intelligence", "machine learning", "tech", "product management", "business development", "marketing", "engineering", "design", "operations", "sales", "partnerships", "product marketing"
• Any tag that merely restates the job category or industry (we already know it's AI/ML)
• Any tag not explicitly mentioned or clearly implied by the job description text

REQUIRED: Tags must be EVIDENCE-BASED. Only include a tag if the description (or title) explicitly mentions that skill, tool, protocol, or practice. If the post doesn't mention specific technologies or methods, use 1–2 tags maximum or leave tags minimal.

GOOD TAGS (prefer these when present in the content):

• AI/ML domains: "nlp", "computer vision", "reinforcement learning", "generative ai", "llm", "robotics", "autonomous systems"
• Languages & runtimes: "python", "rust", "typescript", "go", "c++", "java", "cuda"
• Frameworks & tools: "pytorch", "tensorflow", "jax", "hugging face", "langchain", "ray", "mlflow", "wandb"
• Tools & practices: "figma", "wireframes", "product specs", "user research", "a/b testing", "sql", "react", "node"
• Disciplines (only when clearly a focus): "ui/ux", "mlops", "data engineering", "compliance", "devrel", "deep learning"
• Concrete deliverables/activities: "prds", "roadmaps", "model evaluation", "dashboards", "fine-tuning"

RULES:

1. Do NOT repeat words already in the job title (e.g. "Product Manager" → no tag "product management").
2. Prefer lowercase; well-known abbreviations are fine: "nlp", "ui/ux", "llm".
3. Max 3 tags, prefer 2. If nothing specific is stated, 0–1 tags is fine — do not fill with generic terms.
4. When in doubt, omit. Fewer accurate tags are better than generic ones.

RELEVANCY SCORING (for feed ranking)

Score how relevant this job is to an AI/ML job board audience (0-100).
This score is PURELY about the job content — does the ROLE involve AI/ML?
The company's reputation does NOT matter. A "Backend Engineer" with no AI
in the description scores the same whether it's at Stripe or at a startup.

95-100: Core AI/ML research or engineering that BUILDS or TRAINS models
(ML Engineer, AI Researcher, NLP Scientist, Computer Vision, RL, LLM, MLOps)
85-94: Roles where AI/ML is the PRIMARY daily work: building AI products,
deploying models, AI infrastructure, GPU/CUDA engineering, ML Platform,
prompt engineering, AI agent development, fine-tuning, evaluation,
AI Product Manager, AI Safety Researcher
70-84: Roles with SUBSTANTIAL AI/ML component (>40% of responsibilities):
AI-focused Data Scientist, ML-focused SRE, DevRel for AI products,
Technical Writer for AI/ML documentation, UX for AI products
40-69: Roles with SOME AI/ML involvement mentioned in the description
but it's not the primary focus. AI tools in "nice to have" only.
1-39: Roles where AI/ML is NOT mentioned in the core responsibilities.
This includes ALL generic tech roles (SWE, Backend, Frontend, DevOps,
SRE, Security, QA) and ALL non-tech roles (Sales, Marketing, Legal,
Finance, HR, Recruiting, Ops, Admin, Facilities) regardless of
which company posted the job.
0: Completely unrelated to technology (Custodian, HVAC, Electrician)

CRITICAL: The company name is IRRELEVANT to the score. Score based ONLY on
whether the job description and title involve AI/ML work.

• "Backend Engineer" at Stripe, no AI in description → 5
• "Backend Engineer" at OpenAI, no AI in description → 5
• "Backend Engineer" at OpenAI, building model serving infra → 88
• "Office Manager" at Anthropic → 2
• "ML Engineer" at Stripe → 95
• "Recruiter" at xAI → 3
• "Senior Software Engineer, Full Stack" with no AI mention → 5
• "Senior Software Engineer, AI" building LLM features → 95

AI RELEVANCE CLASSIFICATION (CRITICAL — controls whether job is listed)

Classify whether this job DIRECTLY involves AI/ML/LLM/agentic technologies.
This is a strict binary gate for companies that aren't pure AI companies.
When in doubt, set false. We STRONGLY prefer false positives to be low.

Set "isAIRelevant": true ONLY if the role's PRIMARY work involves AI/ML:

• The title or core responsibilities are fundamentally about AI/ML (researcher, ML engineer, NLP scientist, AI agent developer, prompt engineer, etc.)
• The job description requires building, training, fine-tuning, deploying, or evaluating AI/ML models as a CORE responsibility (not a nice-to-have)
• The role's PRIMARY daily work involves LLMs, RAG, agentic frameworks, or building products powered by AI (not just using AI tools as an end user)
• The role directly engineers AI infrastructure (ML platform, GPU clusters, model serving, distributed training)
• The title explicitly contains "AI" or "ML" and the description confirms AI/ML is the primary focus

Set "isAIRelevant": false if ANY of these apply:

• The role is generic software engineering, even at a company that does AI — unless the description makes clear that AI/ML is a PRIMARY responsibility
• AI/ML is only mentioned in "nice to have", "bonus", or "preferred qualifications" — not in core responsibilities
• The role mentions AI only in company boilerplate (e.g., "we're an AI company" or "our AI-powered platform") but the actual job duties don't involve AI/ML work
• The role is business, operations, legal, finance, HR, recruiting, marketing, sales, or admin — even at an AI company
• The role is a generic "Software Engineer", "Backend Engineer", "Frontend Engineer", "Full Stack Engineer", "DevOps", "SRE", "Security Engineer" where the description doesn't specifically describe AI/ML systems work
• The role uses AI tools as an end-user (e.g., a marketer using AI for content) rather than building/deploying AI
• The description mentions "data" but the work is traditional analytics, BI, or data engineering without ML model involvement

EXAMPLES — study these carefully:

• "ML Engineer" at Stripe building fraud detection models → TRUE
• "Software Engineer" at Stripe on payments infra → FALSE (no AI in this role)
• "AI Product Manager" at Databricks leading LLM features → TRUE
• "Product Manager" at Databricks for data governance → FALSE (no AI focus)
• "Backend Engineer" at Robinhood working on recommendation engine with ML → TRUE
• "Backend Engineer" at Robinhood working on order execution → FALSE
• "Recruiter" at OpenAI → FALSE (support role, no AI work)
• "Technical Writer" at Anthropic writing model documentation → FALSE (not building/deploying AI)
• "Data Analyst" using SQL dashboards at an AI company → FALSE (traditional analytics)
• "Data Scientist" building ML models for personalization → TRUE
• "Solutions Engineer" demoing an AI product to customers → FALSE (sales/GTM role)
• "Research Engineer" training foundation models → TRUE

The bar is HIGH. Only mark true when AI/ML is clearly the PRIMARY focus of the role.

EXTRACTION RULES ===

• Extract data ONLY if clearly stated
• If not found, return null (not empty string, not "Competitive")
• Use the structured data from job board when available (more reliable than parsing description)
• For categoryId: ALWAYS select best matching category based on title and description

AVAILABLE CATEGORIES (id:name):
1:Business Development, 2:Data, 3:Design, 4:Engineering, 5:Finance, 6:Legal, 7:Marketing, 8:Operations, 9:People Ops, 10:Product Management, 11:Research, 12:Sales, 14:Support

EXPERIENCE LEVELS: ENTRY, JUNIOR, MID, SENIOR, LEAD, DIRECTOR, HEAD, C_LEVEL

JOB LISTING TO ANALYZE ===

Title: AI Deployment Strategist, Enterprise
Company: Scale AI
Location: San Francisco, CA; New York, NY
Description:
Scale's Enterprise Applications business is growing faster than ever in the quest to develop reliable AI systems for the world's most important decisions. As an AI Deployment Strategist you will be right at the center of it, helping our largest customers build, launch, and scale groundbreaking GenAI solutions that redefine their industries. You will take full ownership of our deployment success, turning bold ideas into real impact and ensuring every engagement delivers exceptional results.

In this role, you will work directly with data, engineering, business and AI leaders at some of the world's top Enterprises. You will guide projects from the first conversation through delivery, expansion, and measurable business outcomes. Within Scale, you will collaborate closely with Machine Learning, Engineering, Product, and Finance teams to streamline execution, improve processes, and shape our approach to enterprise delivery.

This is a fast-moving, high-impact role that blends customer strategy, operations, product strategy and execution. You will lead the team that serves as the voice of the customer and ensure their flawless execution of customer projects.

You will own:

• The customer outcome: You are the on-the-ground DRI. Renewals and expansion happen because the deployment worked, and the deployment working is your job.
• Repeatable delivery: On time, at quality, with deployment standards you define and enforce. When internal or external teams are not moving fast enough, you push them.
• The next workstream: You see where AI unlocks new business value inside an account before the customer does. You frame the case and bring it to life with our AE and product teams.
• Unsticking what's stuck: When something is blocked, you go fix it. With Engineering, Product, Finance, Ops, whoever it takes.
• Feedback that shapes the product: You bring what's working and what isn't back to product and leadership in a way they can act on.

Ideally, you'd have:

• Experience from one of three places we've hired strong Deployment Strategists out of:
  (1) operators at companies that deploy enterprise AI or data products (Palantir DS or FDE-style roles, and the equivalents elsewhere),
  (2) Strategy consultants from BCG, McKinsey, or Bain who've moved into tech because they want to own outcomes, or
  (3) technical PMs or engineers who got pulled into the business and customer side and never looked back. These are profiles that have worked well, we are always open to other profiles that fit our role requirements.
• 5-8 years in high-growth, high-ambiguity environments. A technical or quantitative background (CS, engineering, economics, statistics, STEM) helps.
• Low ego, high hunger. You take direct feedback well, name your own weaknesses honestly, and run at hard problems.
• A diagnose-before-you-solve instinct. Hypothesis-driven. You ask two layers of questions before reaching for a framework.
• Zero jargon. You can turn a technical concept into business impact a CFO would act on, and a delivery plan an engineer would execute against.
• Technical credibility. No engineering degree required. You are curious enough to push back on engineering leads when the architecture or the accuracy story doesn't hold up.
• Commercial instinct. You think in business levers. You articulate ROI without prompting and spot upsell logic the AE missed.
• Grit. You stick through tough periods. We are wary of project-hoppers.
• Willingness to travel 30-50 percent, which can include international trips depending on the account.

Nice to have:

• Hands-on experience with enterprise AI or data infrastructure products
• Deeper industry knowledge in healthcare, financial services, or consumer

Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position and may be inclusive of several career levels at Scale; it will be determined during the interview process based on work location and additional factors, including job-related skills, experience, qualifications, interview performance, and relevant education or training. Scale employees in eligible roles are also granted equity based compensation, subject to Board of Director approval. Your recruiter can share more about the specific salary range for your preferred location during the hiring process, and confirm whether the hired role will be eligible for equity grant. You'll also receive benefits including, but not limited to: comprehensive health, dental and vision coverage, retirement benefits, a learning and development stipend, and generous PTO. Additionally, this role may be eligible for additional benefits such as a commuter stipend.

Please reference the job posting's subtitle for where this position will be located. For pay transparency purposes, the base salary range for this full-time position in the locations of San Francisco, New York, Seattle is:

$192,800—$241,000 USD

PLEASE NOTE: Our policy requires a 90-day waiting period before reconsidering candidates for the same role. This allows us to ensure a fair and thorough evaluation of all applicants.

About Us:

At Scale, our mission is to develop reliable AI systems for the world's most important decisions. Our products provide the high-quality data and full-stack technologies that power the world's leading models, and help enterprises and governments build, deploy, and oversee AI applications that deliver real impact. We work closely with industry leaders like Meta, Cisco, DLA Piper, Mayo Clinic, Time Inc., the Government of Qatar, and U.S. government agencies including the Army and Air Force. We are expanding our team to accelerate the development of AI applications.

We believe that everyone should be able to bring their whole selves to work, which is why we are proud to be an inclusive and equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability status, gender identity or Veteran status.

We are committed to working with and providing reasonable accommodations to applicants with physical and mental disabilities. If you need assistance and/or a reasonable accommodation in the application or recruiting process due to a disability, please contact us at accommodations@scale.com. Please see the United States Department of Labor's Know Your Rights poster for additional information.

We comply with the United States Department of Labor's Pay Transparency provision*.*

PLEASE NOTE: We collect, retain and use personal data for our professional business purposes, including notifying you of job opportunities that may be of interest and sharing with our affiliates. We limit the personal data we collect to that which we believe is appropriate and necessary to manage applicants’ needs, provide our services, and comply with applicable laws. Any information we collect in connection with your application will be treated in accordance with our internal policies and programs designed to protect personal data. Please see our privacy policy for additional information.