Vision
Not an AI course. A system for producing AI-native engineers who can target frontier labs, FDE-style roles, AI coding companies, AI product teams, infrastructure labs, and serious AI-native startups in 2026-2030.
Antern Data Solutions / Cohort 2 / August 1, 2026 orientation
The AI-Native Engineering Sprint
For working professionals who feel AI is catching up fast and want to transition into serious AI engineering roles.
Antern teaches AI engineering as a system: math, ML, transformers, backend systems, LLM engineering, retrieval, agents, evaluation, reliability, research thinking, outreach engineering, and production capstones. The goal is not another course. The goal is to become credible for AI-native work before the market forces the transition on you.
Outcomes are contextual. Antern does not guarantee a job, salary, offer, or placement.
Philosophy
Distribution over skill alone. Proof-of-work over credentials. Systems, product, reasoning, and execution, not just models.
Role Transition
Target roles across the cohort.
The sprint is calibrated against the skill mix expected by frontier AI labs, FDE teams, AI product teams, infrastructure labs, and seed-to-Series-B AI-native startups.
Applied AI Engineer
Product-facing AI systems, workflow automation, LLM apps, and domain-specific AI tools.
Forward Deployed Engineer
Ambiguous customer problems, end-to-end deployment, stakeholder communication, and production ownership.
Agent Engineer
Planning loops, tool use, memory, orchestration, recovery, evaluation, and human-in-the-loop workflows.
AI SWE
AI-native software engineering, code agents, review systems, developer tools, and rapid product iteration.
AI Infrastructure Engineer
Inference systems, serving, evaluation infra, observability, cost control, and reliability.
AI Product Engineer
AI workflows packaged into usable products with deployment, UX, feedback loops, and business context.
Research Engineer
Paper reading, reproduction, experiments, benchmarks, failure analysis, and research-to-system translation.
AI Startup Engineer
Seed to Series B environments where shipping, taste, product judgment, and distribution matter.
Hiring Signal
What top AI teams actually screen for.
The curriculum is designed around the overlap: software engineering, systems, ML depth, agentic workflows, evaluation, product judgment, communication, taste, and shipping.
Applied AI labs
Software engineering, systems, ML/LLM fundamentals, agent engineering, evaluation, reliability, research thinking, communication, product judgment, originality, and taste.
Research-heavy AI teams
Reasoning depth, research thinking, writing clarity, alignment awareness, intellectual curiosity, and careful technical judgment.
FDE-style roles
Ambiguity tolerance, end-to-end systems thinking, customer communication, product judgment, deployment, and the ability to turn vague business pain into a working system.
AI coding product teams
SWE skill, product taste, AI-native workflow, fast iteration, shipping velocity, and the loop from idea to prototype to user feedback.
AI startup engineer
Shipping ability, product engineering, AI/LLM knowledge, full-stack ability, communication, taste, open-source contribution, and distribution ability.
The Vault
Open every drawer.
Each drawer is a real crawlable page. The Vault gives applicants a fast way to inspect curriculum, method, evaluation, outcomes, network, and enrollment terms.
01
Curriculum
The public curriculum map shows the architecture of the cohort. The complete week-by-week syllabus, exact projects, and topic-level breakdown are shared through the Antern counsellor flow.
Open the drawer -> 02Live Builds
The sprint is implementation-first. Participants build AI systems live, document architectural decisions, evaluate failure modes, and turn the work into proof-of-work that can survive technical scrutiny.
Open the drawer -> 03Outcomes
Antern keeps outcomes evidence in a counsellor-mediated vault with masked participant identities, outreach activity, positive conversations, and meetings. The data is presented as pipeline activity, not as a job, salary, or offer guarantee.
Open the drawer -> 04Teaching Method
Antern teaches participants to feel the problem before learning the solution, reconstruct why ideas were invented, build systems, challenge AI output, explain decisions, and verify work under ambiguity.
Open the drawer -> 05Evaluation
Evaluation is process-aware: participants are assessed on reasoning, verification, business consequence, failure detection, and ability to defend technical decisions, not only on whether they produced a polished artifact.
Open the drawer -> 06Positioning
Professionals learn to choose a domain, build credible proof-of-work, explain technical decisions, publish learning, run outreach, and communicate their value in terms founders and engineering teams understand.
Open the drawer -> 07Research
Antern research combines first principles, historical context, academic papers, industry validation, open-source implementations, production constraints, experiments, and feedback from real users and businesses.
Open the drawer -> 08Outreach Engine
Outreach Engineering combines ICP selection, lead sourcing, AI-assisted research, campaign design, follow-ups, CRM tracking, campaign debugging, and positioning so professionals can create opportunity instead of waiting for it.
Open the drawer -> 09Instructor & Network
Ayush Singh teaches AI engineering through research, implementation, business reality, and operator-level judgment. The network layer comes from Antern, SecondBrain Labs, public teaching, business operations, and counsellor-mediated introductions.
Open the drawer -> 12Enroll
The sprint is for working professionals who already know Python, have some mathematical maturity, use AI natively, and want to transition into serious AI engineering depth.
Open the drawer ->