I built a lightweight runtime layer for AI agents that survives crashes, remembers state, and makes confident decisions — no GPU needed.

Most AI frameworks solve the model problem. Nobody solves the runtime problem. Your agent works perfectly in development, then crashes, forgets state, and retries blindly in production.

Adaptive Runtime fixes this:

- ✅ Crash recovery — state persists across restarts

- ✅ Memory — agent remembers context between sessions

- ✅ Confidence scoring — decisions made with certainty scores

- ✅ Event-driven architecture — reacts to real-time signals

- ✅ SQLite-backed — no cloud lock-in

Benchmarks (mid-range Windows laptop, Python 3.10):

- Cold start: 446ms

- Idle memory: 29MB

- Event processing: 109.2ms avg

- GPU required: Never

Runs on a $5 VPS. No GPU. No cloud dependency.

GitHub: https://github.com/stateflow-dev/adaptive-runtime

Would love feedback from the community!

Sr. Staff Machine Learning Engineer, Content Ecosystem
Pinterest
Remote, San Francisco, CA, Remote, US
$227,871 - $469,147

https://www.moaijobs.com/job/sr-staff-machine-learning-engineer-content-ecosystem-pinterest-3179

Efficient ML Engineer Research Intern (AI Platform) - 2026 Start (PhD)
TikTok
San Jose, CA
$60

https://www.moaijobs.com/job/efficient-ml-engineer-research-intern-ai-platform-2026-start-phd-tiktok-7742

Machine Learning Intern, AQ
Coupang
Mountain View
$40 - $45

https://www.moaijobs.com/job/machine-learning-intern-aq-coupang-1126

Machine Learning Engineer Intern - Scenario Generation
Plus
Santa Clara, CA
$19 - $65/ hr

https://www.moaijobs.com/job/machine-learning-engineer-intern-scenario-generation-plus-8117

Machine Learning Intern
Coupang
Mountain View
$40 - $45

https://www.moaijobs.com/job/machine-learning-intern-coupang-5048

Conversational Speech AI Evaluator; Australia (English)
CrowdGen
Remote Australia

https://www.moaijobs.com/job/conversational-speech-ai-evaluator-australia-english-crowdgen-2827

Machine Learning Engineer, AWS Applied AI Solution
Amazon
US, WA
$143,700 - $194,400 

https://www.moaijobs.com/job/machine-learning-engineer-aws-applied-ai-solution-amazon-55

Machine Learning Engineer, User Understanding (Entry-Level / New Grad)
Unity
Mountain View, CA
$100,800 - $155,980

https://www.moaijobs.com/job/machine-learning-engineer-user-understanding-entry-level-new-grad-unity-8363

Data Scientist
Waymo
Mountain View, San Francisco
$170,000 - $216,000

https://www.moaijobs.com/job/data-scientist-waymo-340

Senior Engineer (ML Engineer)
Graphcore
Cambridge, United Kingdom
$180,000 - $300,000

https://www.moaijobs.com/job/senior-engineer-ml-engineer-graphcore-7025

Data Engineer - Wrapped - Fixed Term
Spotify
Remote, New York, NY, Stockholm
-

https://www.moaijobs.com/job/data-engineer-wrapped-fixed-term-spotify-1954

Software Engineering LMTS- ML Engineer
Salesforce
India
-

https://www.moaijobs.com/job/software-engineering-lmts-ml-engineer-salesforce-4031

Machine Learning Engineer - TikTok Search Ranking
TikTok
Singapore
-

https://www.moaijobs.com/job/machine-learning-engineer-tiktok-search-ranking-tiktok-6754

Lead Data Engineer
Mastercard
Austin, Texas
$122,000 - $207,000

https://www.moaijobs.com/job/lead-data-engineer-mastercard-2739

Machine Learning Engineer Specialist– Recommendation Systems
Mutt Data
Remote, AR
-

https://www.moaijobs.com/job/machine-learning-engineer-specialist-recommendation-systems-mutt-data-3802

C++ Software Engineer - Autonomy/Physical AI
Applied Intuition
Ann Arbor, Michigan
$100,000 - $200,000

https://www.moaijobs.com/job/c-software-engineer-autonomy-physical-ai-applied-intuition-491

Machine Learning Engineer (Training Optimization)
Canva
Beijing, Beijing, China
-

https://www.moaijobs.com/job/machine-learning-engineer-training-optimization-canva-1675

Data Scientist, Platform Product
Anthropic
New York City, NY, Seattle, WA, San Francisco, CA
$275,000 - $370,000

https://www.moaijobs.com/job/data-scientist-platform-product-anthropic-1571

Senior Machine Learning Engineer, Agentic
Robinhood
Bellevue, WA, Menlo Park, Canada
$209,000 - $245,000

https://www.moaijobs.com/job/senior-machine-learning-engineer-agentic-robinhood-7332

Principal Machine Learning Engineer- LLM Fine-tuning and Optimization
Airbnb
United States
$292,000 - $365,000

https://www.moaijobs.com/job/principal-machine-learning-engineer-llm-fine-tuning-and-optimization-airbnb-421

Software Engineer, ML Systems & Training Architecture
OpenAI
San Francisco, CA
$295,000 - $380,000

https://www.moaijobs.com/job/software-engineer-ml-systems-training-architecture-openai-6269

AI Engineer, Special Programs - Top Secret Clearance
SpaceX
Palo Alto, CA
$125,000 - $220,000

https://www.moaijobs.com/job/ai-engineer-special-programs-top-secret-clearance-spacex-1277

Machine Learning Scientist
Roblox
San Mateo, CA
$263,670 - $322,820

https://www.moaijobs.com/job/machine-learning-scientist-roblox-819

We’re a small engineering team building applied AI systems across evaluation infrastructure, ML pipelines, self-hosted LLMs, and research tooling.

We can actually build reliable AI systems around it.

We work with:
• Startups building AI SaaS products
• Research labs needing ML infra support
• Teams struggling with evaluation / QA workflows
• Founders wanting to self-host or fine-tune open-source LLMs
• Companies trying to reduce AI API costs and improve reliability

Some systems we’ve built:

→ LLM evaluation + QA infrastructure (700+ users)

• Multi-model review pipelines
• Deterministic + LLM-based validation
• Schema validation & structured evaluation
• REST APIs + dashboards + CLI tooling

→ Token-efficient IDS/data transformation pipelines

• RAW → IDS conversion systems
• Cached schema slices
• Validation-first architecture
• Reduced token usage + repeated context costs

→ Applied ML systems

• Anomaly detection for chromatography systems
• LSTM forecasting + monitoring dashboards
• AI-assisted diagnostic workflows
• Security and threat-detection PoCs

→ Local/offline AI assistant

• Low-latency conversational systems
• Tool routing
• Offline inference workflows

Tech stack:
Python, FastAPI, PyTorch, Transformers, Streamlit, OpenRouter, Docker, MLflow, Vector DBs, self-hosted LLM infra, evaluation systems, and custom automation pipelines.

If you're building something in:
AI infra, MLOps, evaluation systems, agentic workflows, research tooling, or applied ML — feel free to DM.

You can learn more about us here at horaizon .tech (remove space between horaizon and .tech)

if you have anything for us feel free to DM

Our team is a small growing international software team that partners with clients across the US and EU.

We are currently seeking one experienced Software Developers with strong English communications skills and solid technical expertise. As a our team representative, you will engagae directly with internatinal clients and participate in collaborative technical discussions and interviews on behalf of our engineering team.

I will pay you $30-$50 per hour

Payment will be made immediately after the interview.

If you are interested in this collaboration, DM me and discuss more detail about your role.

Thanks

We are hiring experienced Technical Architects with strong Generative AI expertise for an enterprise AI architecture role based in Taguig City, Philippines.

- Open to international applicants
- Visa sponsorship + relocation package available
- Onsite role – 5 days/week
- Midshift to Night Shift
- Headcount: 2

Requirements:
• 10+ years overall IT experience
• 5+ years in Technical/Solution Architecture
• 5+ years in Generative AI architecture & implementation
• Experience productionizing AI-driven solutions
• Strong enterprise application architecture background
• Python, TypeScript, Node.js, ReactTS, SQL
• Experience with AI/ML, Agile, DevOps, and MLOps
• Strong experience leading technical projects

Looking for candidates with hands-on architecture and development experience — not purely people management.

Interested candidates may send:
• Updated CV
• Total years of AI/Architecture experience
• Current salary
• Expected salary
• Earliest availability

\[FOR HIRE\] Data Scientist / ML Engineer | 4+ YOE

Data Scientist / ML Engineer with 4+ years of experience in Machine Learning, NLP, GenAI, SQL, Python, and MLOps.

Skills: • Python, SQL, Spark • Machine Learning & Deep Learning • LLMs, RAG, LangChain • FastAPI, Docker, AWS • Data Pipelines & Analytics

Open to: • Remote roles • Freelance/contract work • AI/ML consulting • Product-based companies

Available for immediate joining. Feel free to DM.

Can anyone honestly review my resume and tell me what might be wrong with it? I’ve been applying for AI/ML, Data Science, and Backend-related fresher roles but I’m barely getting interview calls.

TL;DR: Early-stage YC-backed healthcare AI startup. Own ML systems end to end alongside the founders. SF hybrid, contract. Email [founders@pelica.com](mailto:founders@pelica.com) or apply on LinkedIn.

About Pelica Health

Pelica Health (pelica.com) is the operating system for value-based care. We unify claims, EHR, pharmacy, lab, and ADT data into one live record per member and put an AI copilot next to every team that depends on it, across risk adjustment, Quality and Stars, pharmacy, provider network, and care management. Founded by former Google and YouTube engineers and backed by Y Combinator.

What you'll do

• Build and own production ML systems end to end, from data modeling and feature engineering to training, evaluation, deployment, and monitoring
• Design and implement data pipelines that turn raw, messy real-world healthcare data into reliable features for ML models
• Train and evaluate models for ranking, prioritization, and prediction problems (e.g., identifying high-risk or high-priority cases)
• Deploy models into production as reliable services or batch jobs, with clear versioning, monitoring, and rollback
• Collaborate directly with the founders and engineers to translate product and operational needs into scalable ML solutions

What we're looking for

• 3+ years building and deploying ML systems in production
• Strong foundation in ML for structured data: feature engineering, regression/classification, and ranking/prioritization
• Experience with the full ML lifecycle: data prep, evaluation, deployment, retraining, and monitoring
• Solid backend engineering skills and comfort in a fast-paced startup environment with high ownership

Why join

• Learn from seasoned Google and YouTube engineers who have operated at massive scale
• High impact and real ownership on a small, ambitious team
• Bring modern AI to the hardest problems in healthcare, helping the teams closest to patients improve outcomes

How to apply

Email [founders@pelica.com](mailto:founders@pelica.com) or apply on LinkedIn: Pelica Health

Hello everyone,

So I'm a 12th pass rn(18M)(from India) and my background is PCB (PHYSICS CHEMISTRY BIOLOGY) so I'm planning to do BCA(BACHELORS IN COMPUTER APPLICATIONS) to enter the IT field and then go for MASTERS and do specialization in AI/ML and become a ML engineer through it so all I wanna know is

1)Is ML engineer a safe career choice or should I go for Physiotherapy? as I also have that as my option and physiotherapy is a pretty good earning career in western countries and countries outside India

2)What skills do I need for a good job which I can work upon ?(I'm multilingual and am learning japanese and chinese for companies to see me as an asset in future)

3)Someone please brief me upon the market currently and how it can change in 7-8 yrs till which I'll finish my studying and enter the job market

4)I want to connect to people who can guide me further so I'd appreciate people who're open to connecting with me and guiding me forward

5) I wanna also connect with aspiring ML Engineers who plan to take advantage of this AI boom

6)I'm open to you all's advice and want opinions

7) I wanna settle abroad so please keep that in mind cus Indian careers placements are facing oversaturation

Also I'd appreciate it if you comment for more reach even if you can't help me

We’re looking for a reliable Software Developer for a long-term, part-time role. This is a great opportunity for someone who wants consistent work and values clear communication in a remote environment.

💼 Requirements

Strong English proficiency (C1–C2 level required)

Availability to work in EST time zone hours

Responsive and able to communicate promptly during working hours

1–5 years of software development experience

⭐ Bonus Skills

Experience with modern frameworks and development tools

Exposure to AI-related technologies or projects

Stable internet connection and a reliable remote setup

💰 Compensation

$30–$60/hour, depending on experience

Weekly payments available (based on agreement)

📩 How to Apply

Please send:

Your English proficiency level

Your country

A brief overview of your experience (optional but recommended)

Hey everyone, I'm a QT/QR at one of the well-known firms in the space. Math/Physics background, no research experience however. I'm a bit iffy on how I feel about finance and feel that my technical talents might be better applied elsewhere. How feasible is it to transition from a quant role into a AI/ML Research role? Will I be able to get interviews with big labs/interesting startups? What can I do to be a meaningfully more desirable candidate besides going back to school?

I’ve completed the experiments, identified the research gaps, and obtained results for an ML research project.

Due to time constraints, I’m looking for an experienced ML researcher/writer (preferably with prior publications or arXiv papers) to collaborate on converting the work into a well-structured research paper.

Looking for help with:

- academic writing

- positioning the contribution

- related work

- paper structuring

- polishing for submission

Open to co-authorship for meaningful contribution.

If interested, please DM with your background/publications.

EverAI is hiring a remote Mid/Senior AI Cinematic Video Editor. Category: Artificial Intelligence 📍Location: Remote (Worldwide)

See more and apply here!

1. Software Engineer, RL Training Infra

• Date Posted: 05/23/2026
• Company: OpenAI
• Location: San Francisco, CA
• Salary: $295K–$445K
• Tech stack: Python, reinforcement learning, distributed training, ML infrastructure
• Why it's cool: You're the engineer keeping frontier RL training runs alive — your fixes ship inside the next GPT release, and the comp band tops out at $445K base.

2. Researcher, Computer Use — Agent Post-Training

• Date Posted: 05/22/2026
• Company: OpenAI
• Location: San Francisco, CA
• Salary: $250K–$380K
• Tech stack: LLMs, RL, post-training, evals
• Why it's cool: Direct hand on the post-training dials that decide whether GPT-class agents can actually drive a computer — research taste + engineering execution, not just paper-writing.

3. Senior Software Engineer, AI Platform

• Date Posted: 05/23/2026
• Company: Mixpanel
• Location: San Francisco, CA
• Salary: $226K–$306K
• Tech stack: LLMs, multi-agent orchestration, evaluation systems, model serving
• Why it's cool: Greenfield AI platform team at a profitable analytics company — you design the eval and serving primitives every other Mixpanel product team will build agents on top of.

4. Staff AI/ML Engineer

• Date Posted: 05/25/2026
• Company: Sigma Computing
• Location: San Francisco, CA
• Salary: $240K–$270K
• Tech stack: Python, foundation models, AWS, SQL
• Why it's cool: Real foundation-model adaptation work (not just prompt-stuffing) shipped into a BI product millions of analysts already use — rare combo of model depth and product reach.

5. Senior Machine Learning Engineer

• Date Posted: 05/22/2026
• Company: Calendly
• Location: Remote (US)
• Salary: $238K–$288K
• Tech stack: Python, GenAI, Scala, SQL
• Why it's cool: Senior ML role you can do from anywhere in the US, at top-of-market remote comp, owning GenAI features end-to-end at a product nearly every knowledge worker already has open in a tab.

6. Agentic AI Engineer

• Date Posted: 05/22/2026
• Company: Benchling
• Location: San Francisco, CA
• Salary: $176K–$265K
• Why it's cool: You define the agentic-AI architecture from day one at the company that already runs most of biotech R&D — your designs end up wired into actual drug-discovery workflows.

7. Sr. Software Engineer, Backend (ML Pipelines)

• Date Posted: 05/25/2026
• Company: Carbon Robotics
• Location: Seattle, WA
• Salary: $190K–$225K
• Tech stack: Go, AWS, Kubernetes, gRPC
• Why it's cool: The ML you ship powers a fleet of laser-shooting autonomous tractors that zap weeds in real time — physical-world impact you can literally watch on YouTube.

8. Generative AI Engineer

• Date Posted: 05/25/2026
• Company: Dataiku
• Location: New York, NY
• Salary: $160K–$240K
• Tech stack: Python, LangGraph, RAG, Vue.js
• Why it's cool: Hands-on agentic + RAG building at a company whose entire product is enterprise AI — you ship internal multi-agent systems and the patterns feed back into Dataiku's external platform.

9. Machine Learning Engineer II

• Date Posted: 05/25/2026
• Company: Affirm
• Location: Remote (US)
• Salary: $142K–$210K
• Tech stack: Python, XGBoost, LightGBM, MLflow
• Why it's cool: Mid-level remote ML role with clear, measurable impact — every model you ship either saves Affirm money on chargebacks or gets a real customer their refund faster.

10. Senior AI Engineer

• Date Posted: 05/25/2026
• Company: GitLab
• Location: Remote (US)
• Salary: $139.2K–$218.4K
• Tech stack: Python, TypeScript, LLM APIs, GraphQL
• Why it's cool: Fully remote, all-handbook-public engineering culture, and your job is literally "find bottlenecks in the business and ship LLM solutions" — wide autonomy and ship-to-impact loop.

AfterQuery is hiring Chip Design Machine Learning Experts to work on advanced AI projects combining machine learning with VLSI and electronic design automation (EDA).

Type: Contract
Location: Remote
Industry: Data Analysis
Commitment: 10 hrs/week
Pay: $150-$200 per/hr

What you’ll do:

• Apply ML techniques to chip design workflows like placement, routing, and synthesis
• Build models for design optimization, prediction, and verification tasks
• Work on graph neural networks, reinforcement learning, and ML-for-EDA systems

Requirements:

• Master’s or PhD in EE, CE, CS, or related field
• Research background in both ML and chip design/EDA
• Published work in venues like DAC, ICCAD, NeurIPS, or ICML

Preferred experience: Cadence, Synopsys, OpenROAD, AlphaChip, or ML-for-EDA research experience is a plus.

Apply now: https://experts.afterquery.com/apply/chip-design-ml

Ideal for ML researchers, VLSI engineers, EDA specialists, and hardware AI researchers.

Company : Pure Technology Position- AI Developer Intern Location (City)-Pune Work Style - WFO Internship: 6 months Stipend:5k per month PPO: Based on internship performance No Of Opening- 02 Interview Round- 03 Interview Mode - AI Interview + Face-to-face Position Overview: We are seeking a talented AI Developer Intern to join us. You will work on cutting-edge projects involving generative AI, LLMs, transformers, and AI agents while developing production-ready applications using Django/Flask/FastAPI . Key Responsibilities:

• Develop generative AI applications using transformer-based models
• Work with large language models (LLMs) and fine-tune them for specific use cases
• Design and build autonomous AI agents
• Develop backend APIs using Django or Flask
• Implement prompt engineering and optimization
• Deploy and manage local models in production
• Test, debug, and optimize AI models
• Collaborate with AI engineers and developers on end-to-end features Required Qualifications:
• Strong Python proficiency
• Hands-on experience with Django or Flask or Fast API
• Understanding of machine learning and transformers
• Familiarity with LLM frameworks (Hugging Face, LangChain, or similar)
• Problem-solving skills and attention to detail
• Ability to work independently and collaboratively Preferred Qualifications:
• Generative AI project experience
• Knowledge of agentic AI frameworks
• Experience with prompt engineering and fine-tuning
• Familiarity with vector databases (FAISS, Chromadb, Pinecone)
• Experience with RAG (Retrieval-Augmented Generation) systems
• Knowledge of local model deployment (Ollama, LM Studio)
• Git experience
• Various AI Tools Knowledge Technical Stack:
• Core: Python, Django/Flask, PyTorch or TensorFlow
• AI/ML: Hugging Face Transformers, LangChain, LlamaIndex
• Deployment: LLM inference (Ollama, LM Studio), Docker
• Data: Vector databases, SQL, Git

Does anybody know any people who got hired in the past couple of months? Is the on ground hiring happening in the job market or are we all going round and round on a wild goose chase?

i made a lot of github repos for machine learning mostly in the trading finance universe. stuff like volatility forecaster and other stuff, can these stuff be notices by a recruiter by chance? my github name is lexicalmaze3 https://github.com/lexicalmaze3 id be glad for any tips please

Came across an Outlier project recruiting radiologists in the US/UK/Canada for an AI diagnostics reasoning project involving MRI review and complex clinical scenarios. Seems interesting for anyone working at the intersection of medical imaging. Happy to share a referral link if relevant to anyone here.

[HIRING][Plano, Texas, Machine-Learning, Onsite]

🏢 Optimum, based in Plano, Texas is looking for a Software Engineer III

⚙️ Tech used: Machine-Learning, AI, Git, Support, JavaScript, Machine Learning, NodeJS, Python, microservices

💰 $133,661 - 219,586 / year

📝 More details and option to apply: https://devitjobs.com/jobs/Optimum-Software-Engineer-III/rdg

Research Engineer(Computer Vision & Deep Learning)

Got the interview call from Robotics company in India(Less ML+CV+DL+RL opportunity) for Research Engineer, can anyone give me interview experience for research position. (Solve assignment in just 5 hours)

My preparation is I revise my projects, revise cs231n, some deep learning fundamental also mostly aware of modern days tech, paper, research, PyTorch concepts and practice.

Hello all,

I have been joining interviews for a VERY long time, and I still have not received any offer. One of the main reasons behind that is the unstandardized interview process. Everybody tries to assess me on completely different topics. The topics that were part of my interviews so far were:

• Online LeetCode
• Online SQL
• Take-home assignment for a RAG application and using Spark for data processing
• Take-home assignment for developing and deploying a multi-agent chatbot (including its backend)
• Designing a recommendation system in system design
• Classical machine learning (e.g., random forest, XGBoost, linear regression, decision tree, etc.)
• Deep learning models (e.g., attention mechanisms, transformers, LLMs, CNNs, RNNs, GANs, autoencoders, diffusion models)
• Online debugging
• Writing a K-Means clustering model from scratch without using Google or AI (probably because the interviewer did not know shit about any other ML processes. All he knew was K-Means clustering, and he used that to evaluate me for a role that requires developing agentic systems lol)
• How to build a RAG process step by step and scale it
• Distributed training mechanisms (e.g., data parallelism, pipeline parallelism, tensor parallelism), when to use which, and how they work
• Difference between LangChain and LangGraph
• Statistics & Probability
• Software engineering principles (because there was no AI/ML engineer in the department, and they did not have knowledge of AI/ML)
• 12 leadership principles, coming up with 2 stories for each principle (for my Amazon interview)
• All the details of projects I did 4–5 years ago. Why I specifically chose method X, what challenges I experienced during the process, and how I handled them
• Decorators
• Multi-threading
• Online PyTorch coding

I didn't move forward until ML system design part until now. But even if I did, I'm sure they would try to use ask from a wide range of topics such as recommendation systems, text classification, image classification, RAG, agentic systems, real-time ML, high-availability systems, low-latency systems, etc.

So my question is, how can you be able to pass interviews exactly?

Hi folks, I am preparing for ML based roles. I have 4 years of experience in software development, mainly in Java.

So I don't have any ML or Python or Data related work experience but I love the field, I love to build models which gives excellent predictions. Currently I have ML fundamental knowledge(Linear, Logistic regression, Decision Trees, Random Forest, KNN, K-Means, Gradient Boosting, AdaBoost), with ANN(don't know CNN, RNN, LSTM yet), ARIMA, basic NLP(don't know Transformers yet) and some Statistics and Python.

I have done 2 projects in ML,

1. A forecasting project using ARIMA, also created APIs in FastAPI to train the model and get forecast and used docker to containerize it.

2. SMS spam classifier using CBOW and ANN.

In Development I know Coding, DSA, System Design, REST APIs, SQL.

I am not sure which roles I will be fitting into if I want to work in ML, is it Data Scientist, or ML Engineer, or Software Engineer in ML, or Analyst(Business or Data). I have been unemployed for over an year now due to many confusions.

Can you tell me which roles should I target and for that which skills should I focus? Also which projects should I do to have a better chance to get shortlisted?

Hi everyone,

I'm currently a sophomore (2nd-year CS student) based in Vietnam. Over the past 6 months, I've been diving deep into CUDA programming. I’ve started getting my hands dirty with NVIDIA's ecosystem, specifically using CuTe and CUTLASS to design optimized custom kernels.

I’m really interested in the hardware acceleration side of things. I spend my time studying new architectures like DeltaNet v2, Mamba3, etc., and trying to implement/optimize their components from scratch in CUDA.

However, I'm having a bit of an "imposter syndrome" crisis and career doubt:

1. The Local Market Reality: From what I can see, the tech market here in Vietnam (and perhaps globally for entry-level?) is heavily focused on Applied AI. Companies just want to wrap APIs (OpenAI/Anthropic), build RAG systems, or use out-of-the-box libraries like fla, FlashAttention, or deploy using vLLM/Ollama. There seems to be almost zero demand for someone writing custom low-level kernels at the junior level.
2. Self-Taught Doubts: Since I'm entirely self-taught using public GitHub repos, papers, and AI tools, I don't have a mentor. I use Nsight Compute to profile, but I constantly worry if I'm learning the "right" way or just building bad habits.

I have a few questions and I hope someone can explain them to me:

• Is my effort misplaced? Should I pause this deep dive into CUDA/AI Infra and focus on standard Applied AI/MLOps just to get my first job/internship?
• How do I prove myself? I know I can't contribute to massive repos like vLLM right now (it's too overwhelming after only 6 months of CUDA). What kind of "micro-projects" or portfolio pieces would actually impress a hiring manager for an infrastructure role, especially for remote internships?
• Remote potential: For those working in AI Infra, is it feasible for a junior/intern from Southeast Asia to land remote roles in this specific niche?

Any harsh truths, guidance, or project ideas would be greatly appreciated. Thanks!
I apologize for using AI to summarize my question, my English isn't very good and I didn't know how to express the question more clearly.