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

See more and apply here!

We are looking for a senior or staff level AI Context & Harness engineer to help us build the future of AI software development. We are a great team and have hundreds of thousands of customer building real apps and businesses!

More info and link to apply: https://hercules.app/careers/ai-context-harness-engineer

I am looking to add more ML projects (if not any other would be fine) and improve the quality of my resume overall. Any suggestions and advice would be heavily appreciated.

Hey everybody,

I applied to 600+ AI/ML internship roles in the USA and have not received a single interview, not even many rejection emails. I tailor my resume for each job, add keywords from the posting, message recruiters after applying, and ask people for referrals when I can. Still, nothing is working.

I want honest feedback specifically from AI/ML hiring managers, ML engineers who interview interns, data science managers, and technical recruiters who hire for AI/ML roles in the USA. Can you please look at my resume and tell me where I am going wrong? I want to know if my resume looks too buzzword-heavy, if I am applying to the wrong roles, or if my strategy is bad.

Please be blunt. I am not looking for generic advice. I am looking for real advice from professionals who have hired, interviewed, or recruited AI/ML interns before. What would you change first if this was your resume?

Thank you so much for your time.

Hey everyone!
I’m planning to create a small group for people interested in learning and exploring:
DSA
AIML
Python
Projects
Hackathons
Problem solving

The goal is to stay consistent, learn together, share resources, discuss concepts, and build projects collaboratively. Beginners and intermediate learners are both welcome.
If you’re serious about learning and growing together, feel free to join! 🙌

https://discord.gg/GqecEqGQW

Need for professional developers

I need a developer who is familiar with at least one of these areas;

Full-stack.

Backend.

Machine-Learning.

Java.

Pyton.

AI/ML

C#

And have complete fluency in English.

[HIRING][Tampa, Florida, Machine-Learning, Onsite]

🏢 KORE1 Technologies, based in Tampa, Florida is looking for a VP of Product – AI-Driven TEM Platform

⚙️ Tech used: Machine-Learning, AI, Support, Machine Learning

💰 $180,000 - 225,000 / year

📝 More details and option to apply: https://devitjobs.com/jobs/KORE1-Technologies-VP-of-Product--AI-Driven-TEM-Platform/rdg

Just put together a quick roundup of ML jobs that are still open. Hope this helps someone land something great!

• AI Research Engineer (Applied AI) @ Bright Vision Technologies

• R&D Engineering Intern @ Technergetics

  • 💰 $22 – $28/h

• Senior Compiler Engineer - AI @ IL Mellanox Technologies, Ltd.

  • 💰 $184–288K/y

• Machine Learning Developer (Freelance) @ Mindrift

• Sr. Agentic AI Software Engineer @ Mango Languages

• AI/ML Software Engineering - Talent Pipeline @ Fullsteam Personnel, LLC

• Software Engineer III, Core Experiences @ Wikimedia Foundation

  • 💰 $92–144K/y

• Software Engineer - AI SDK @ Temporal Technologies

  • 💰 $160–200K/y

• AI Engineering - Senior/Lead Engineer @ Fair Isaac Mexico S.A. de C.V.

  • 💰 $140–220K/y

• Staff / Senior Staff Backend Engineer, B2B Flywheel @ OpenAI

  • 💰 $293–385K/y

• Security Engineering Intern - Summer 2026 @ Lambda

  • 💰 $47 – $63/h

• AI Engineering Intern @ Phil

  • 💰 $30 – $50/h

• Senior Machine Learning Engineer @ General Motors LLC

  • 💰 $223–227K/y

• Senior Application Developer @ Highmark Health

• Sr. Backend Software Engineer (Video Engineering) @ Philo

• Software Engineer @ VSHP Volunteer State Health Plan, Inc

• Full-Stack Engineer (Cleared) @ US Guidehouse Inc.

  • 💰 $80–133K/y

• Engineering Manager, Model Library @ Baseten

  • 💰 $165–330K/y

• Senior Machine Learning Engineer, Rich Media Experiences @ ZINC Zillow, Inc.

• Senior Backend Software Engineer @ Cloudbeds

  • 💰 $145–165K/y

• Senior Backend Software Engineer @ Cloudbeds

  • 💰 $145–165K/y

• Signal Processing Developer – American Football (C++/Python) @ TrackMan A/S

• Signal Processing Developer – American Football (C++/Python) @ TrackMan A/S

• AI/ML Software Engineer @ Booz Allen Hamilton_United States

  • 💰 $87–198K/y

• Software Engineer (AI-Driven Billing Automation) @ Artera

  • 💰 $120–180K/y

Like the post if I should keep doing more of these, Cheers!

[Project] VEDA - I built an autonomous ML platform with 140+ agents that takes any data source and a plain English goal, then builds and deploys the model itself

I've been working on this for a few months and finally launched it. Wanted to share it here and get some feedback from people who actually know ML.

What it does:

You connect a data source and describe your goal in plain English. VEDA figures out the rest.

Supported data sources:

- CSV, Excel, JSON, Parquet

- SQL databases

- REST APIs

- Cloud storage (S3, GCS)

- PDFs and documents

- Real-time streams

The pipeline runs 11 sequential agents:

Ingest → Clean → Profile → Feature Engineering → Feature Selection → Scaling → Training → Evaluation → Hyperparameter Tuning → Model Selection → Report

The ML stack:

- Optuna for Bayesian hyperparameter optimization (50 trials via TPE sampler)

- XGBoost, LightGBM, Random Forest benchmarked automatically

- SHAP explainability on every prediction

- KS-test + PSI drift detection on live predictions

- A/B testing with chi-square significance testing

- Hash-based data versioning with full lineage tracking

The AI layer:

- Groq LLM (Llama 3.3 70B) for natural language goal interpretation

- Claude AI for agent reasoning and decision-making

- LangGraph for multi-agent orchestration

Production engineering (the part most ML projects skip):

- FastAPI backend with async SQLAlchemy + PostgreSQL

- Celery + Redis task queue — jobs persist across server restarts

- Circuit breakers per agent with CLOSED/OPEN/HALF-OPEN state transitions

- Alembic database migrations

- Rate limiting (5/min login, 10/min workflow creation)

- Brute force protection — 5 failed attempts → 15 min lockout

- Secrets management with Vault/AWS/env backends

- Full docker-compose stack with Nginx + TLS

Numbers:

- 140+ agents across 12 domains

- 35 REST endpoints

- 7,000+ lines of Python

- Deployed live on HuggingFace Spaces

Links:

- Live demo: https://keshav1838-veda-ml-platform.hf.space

- GitHub: https://github.com/keshavloma1081-ctrl/VEDA--Auto-DS

- API docs: https://keshav1838-veda-ml-platform.hf.space/docs

Honest limitations:

- Currently optimized for tabular data (classification + regression)

- Celery/Redis features require local setup — HuggingFace deployment uses BackgroundTasks fallback

- Some advanced agents (GNN, RL, CV) are scaffolded but not fully wired into the main pipeline yet

Happy to answer any technical questions. Roast it if you want — genuine feedback is more useful than likes.

Company Description

We design and build Agentic science-based diagnostic and development systems that sit at the intersection of AI, Science and Psychology. We digital twins, multi-agent orchestration systems and Ai driven diagnostics assessment systems to measure human psychological states, model individual development trajectories, and deliver hyper-personalised interventions grounded in validated theory and psychometric evidence. Every system we build is designed for auditability, EU AI Act compliance, and real-world clinical and professional use.

We work with major financial services, technology and health-care enterprise clients. Our team spans applied AI engineering, psychometrics, psychology, and data science.

Role Description

We are looking to expand our team with two additional AI Engineers (Agentic AI systems) to help build the next generation of psychological assessment systems. The systems we are building uses a graph-based orchestration architecture with a governed state store, retrieval-grounded intervention delivery, and a digital twin layer that models individual psychological profiles through non-intrusive measures over time. We are at an advanced stage of development. You will not be starting from scratch.

We have two positions open. Both are engineering roles with significant depth requirements. One position has a stronger emphasis on the assessment and intervention delivery layer. The other is focused on the digital twinning architecture. You will work closely with AI systems psychologists. Day-to-day tasks will include designing, implementing and evaluating multi-agent architectures, assisting with fine-tuning machine learning models, collaborating with multidisciplinary experts, and utilizing cutting-edge AI techniques to create impactful, science-based psychological assessment and development tools.

What You Will Do

• Build graph or state-machine orchestrator agents that enforces a structured multi-step workflow with persistence and resumability for long-running interactions and handoffs.
• Implement RAG pipeline for a curated content and intervention library, including ingestion, chunking, metadata design, hybrid retrieval, reranking, and provenance.
• Design scoped context layer that exposes only policy-approved, banded user state to the model while keeping the user experience seamless.
• Implement safety controls and escalation flows aligned to modern LLM threat models, including prompt injection and sensitive data leakage risks.
• Instrument the system for evaluation, monitoring, and regression testing so changes do not degrade safety or retrieval quality.
• Fine-tune Llama-based models for psychological state, and trait classifications
• Additional engineering tasks appropriate to the nature and scope of work from our clients

Qualifications

• Master’s degree or higher in Computer Science, AI, ML, NLP, Data Science, Statistics, or equivalent
• Demonstrable experience building multi-agent or tool-using LLM systems with an orchestrator, ideally graph-based tool calling, structured outputs, and testing
• Strong skills in Machine Learning, Artificial Intelligence, and Natural Language Processing
• Proficiency in programming languages such as Python, R, and experience with deep learning frameworks like TensorFlow or PyTorch as well as Langchain/Langraph
• Proficiency in API design and datamodelling (SQL)
• Hands-on experience with developing RAG systems (using LlamaIndexing) and vector retrieval systems (pgvector, Qdrant, Pinecone, Weaviate, or similar)
• Experience in fine tuning and deploying generative AI models like RoBERTA and Llama 4.2
• Ability to create data-driven solutions with expertise in data preprocessing, analysis, and feature engineering
• Strong communication and collaboration skills for working in interdisciplinary teams
• Familiarity with ethical AI principles and responsible AI development practices
• Llama Guard style classifier pre and post generation, plus hard-coded handoff flows, designed against OWASP LLM risk
• Strong security mindset for LLM applications, including practical mitigations for prompt injection and data exfiltration risks.
• Fully Proficient in English.
• Meticulous attention to detail.
• NOTE: MUST BE LEGALLY ALLOWED TO WORK IN EUROPE

Nice to have

• Familiarity with psychometrics, assessment design, or measurement concepts
• Some understanding of psychology, organizational psychology, or applied behavioural science
• Data science experience, especially experimentation, evaluation, and monitoring
• Familiarity with GenAI risk management practices such as NIST’s GenAI profile guidance

Contract Details

• Initial 12 month contract, with a strong likelihood of permanent employment thereafter.
• Remote, Europe-based, priority for strong overlap with CET and EET working hours
• Full-time
• Salary: Between € 4000 to €6000 pm depending on experience

Application Process

The selection process has three stages:

• Portfolio and CV review. We will assess your portfolio and application materials before scheduling any conversations.
• Technical interview. A structured conversation with our Chief Solutions Architect. Expect questions on architecture decisions, failure modes, and how you think about safety in psychologically sensitive contexts.
• Technical project and assessments. Shortlisted candidates will complete a time-limited technical exercise and a set of assessments relevant to the role.

Portfolio submission requirements

Applications must include a portfolio (e.g. Github, Kaggle, Huggingface) with at least two of the following:

• An orchestrated agent workflow (graph or state machine)
• A production RAG system (retrieval design, metadata, evaluation)
• A concrete example of an LLM safety mitigation you implemented (threat model, controls, testing)
• Describe a graph-based orchestration you implemented and how you handled retries, persistence, and evaluation

How to Apply

Submit your CV (with three contactable references), portfolio links, a short note describing one relevant system you built (architecture, what failed, what you changed), and your availability and rate expectations. Please send it to [hello@psynalytics.com](mailto:hello@psynalytics.com) and do not use the easy apply function.

We read every application personally. If your portfolio is strong and your experience aligns with what we are building, we will be in touch within five working days.

I am applying for the jobs of many portal still i am not getting any call provide any suggestions to improve the resume

I applied this resume for many interns but never got shortlisted. Help me understand problems in my resume.

my resume:

Processing img hbo2238ymx1h1...

We are looking for a highly curious and technically skilled Junior AI Agent Engineer to join our growing AI R&D team.

If you love building with LLMs, experimenting with AI agents, and exploring cutting-edge AI workflows, this role is for you.

🔹 Role: Junior AI Agent Engineer
🔹 Department: AI Research & Development
🔹 Experience: Freshers / Early-career candidates with strong practical skills are welcome
🔹 Work Type: [Remote/Hybrid/On-site – add your preference]
🔹 Location: [Add Location]

💡 What You'll Work On:
• Designing and implementing agentic workflows using LangChain, CrewAI, or AutoGen
• Integrating APIs, Python scripts, search tools, and external functions with LLMs
• Experimenting with advanced prompting techniques like Chain-of-Thought, ReAct, and Reflexion
• Building and supporting RAG (Retrieval-Augmented Generation) pipelines
• Testing AI agents for reliability, reasoning quality, and hallucination reduction
• Rapidly developing PoC AI agents and innovative workflows

🛠 Skills We’re Looking For:
• Strong Python programming skills
• Understanding of LLMs and AI agent frameworks
• Familiarity with APIs, vector databases, and RAG concepts
• Curiosity for AI research and experimentation
• Problem-solving mindset and willingness to learn fast

✨ Bonus Points:
• Hands-on projects with LangChain, CrewAI, AutoGen, OpenAI APIs, or similar tools
• Experience with prompt engineering and AI evaluation methods
• GitHub projects, demos, or personal AI experiments

📩 Interested?
Share your resume, portfolio, GitHub, or project links via DM or email at: [kritika.d@tayanasolutions.com](mailto:kritika.d@tayanasolutions.com)

Let’s build the next generation of AI agents together 🚀

I’m feeling stuck and honestly confused about whether I should stay at my current company or leave. I was hired as an AI/ML Engineer, but after joining and spending two months here, I realized the company itself isn’t very clear about what they actually want. What they really seem to expect is someone with a strong full-stack “vibe coding” mindset who can quickly build and ship products instantly.

At first, I thought I could manage it while continuing to search for a proper AI/ML role. So I kept applying to different opportunities, but I’ve faced a lot of rejections so far. That’s made me start questioning where exactly I’m falling short and what skills I need to improve.

The thing is, AI/ML is genuinely what I’m passionate about. I really want to work on meaningful AI/ML projects, contribute seriously, and grow in the field instead of feeling disconnected from the role I originally signed up for. Right now, I’m trying to figure out whether I should continue here for stability while upskilling, or move on and focus fully on finding the right opportunity.

micro1 is hiring a Member of Technical Staff to build and deploy AI systems for high-security, mission-critical environments.

Location: Hybrid - Washington, D.C.
Type: Full-time
Openings: 1
Compensation: $350,000 - $500,000/year

What you’ll build:

• AI data pipelines and inference systems
• Multi-agent and RAG-based LLM workflows
• RL environments and production AI infrastructure

Strong fit if you have:

• Strong Python engineering experience
• Experience with LangChain, LangGraph, or modern LLM stacks
• Background in distributed systems or ML infrastructure

Preferred: Security, regulated environments, Vertex AI, Bedrock, or high-reliability systems experience.

APPLY HERE - https://jobs.micro1.ai/forward-deployed-member-tech-staff

This role combines LLM systems, infrastructure engineering, data pipelines, and forward-deployed partner work.

(Disclosure: Shared as an independent member of the micro1 referral program)

I’m building a free open AI cohort, and I just published Lesson 01.

The lesson is called Hugging Face Beyond Upload.

Most beginner tutorials treat Hugging Face like this:

download model → run notebook → move on

I wanted to teach it more like an AI engineering skill.

The lesson covers:

- how to navigate Hugging Face model repos properly

- how model files are structured

- how config.json connects to the actual model class

- how to move from a model page to the relevant Transformers code

- how to understand model files instead of treating them as magic blobs

- why small models like Qwen3-0.6B are useful for learning

- why Markdown matters in AI workflows: model cards, README files, GitHub issues, Discord, Cursor/Claude Code planning files

- how to think of open models as infrastructure / supply chain

The biggest section is on datasets.

I show 3 ways to inspect Hugging Face datasets:

1. Croissant metadata endpoint

2. Data Studio / browser dataset viewer

3. load_dataset with Python, pandas, and plots

We inspect columns, categories, response lengths, short examples, long examples, distributions, and how to make an early judgment about dataset quality before using it for training or fine-tuning.

The lesson also sets up the next part, where we run Qwen3 directly in C, so learners can understand what libraries like Transformers are doing behind the scenes.

I think this is an important skill for people trying to get into AI/ML jobs.

Not just “I used an LLM API”.

But:

Can you open a model repo and understand what is inside?

Can you inspect a dataset before training?

Can you connect model files to actual source code?

Can you reason about the quality of data before fine-tuning?

Video:

https://youtu.be/MjZio-A9oUY

Lesson page:

https://cohort.bubblnet.com/lessons/lesson-1-huggingface-beyond-upload

I’d genuinely appreciate feedback from people here:

- Is this the right level for learners trying to move from “using AI tools” to understanding models/datasets?

- What would you expect juniors applying for AI/ML roles to know about Hugging Face?

- Should I go deeper into model internals first, or datasets/training pipelines first?

Been sitting on this question for a while. I've got a decent ML background (Python, some NLP, basic model training) and I've been doing content marketing and SEO work for the past couple years. The overlap feels obvious but I'm struggling to figure out which skills actually translate to real value in a marketing context. Things like predictive lead scoring, churn modelling, and personalisation pipelines seem like solid use cases, but most marketing teams, I've talked to either don't know what to do with that or are just using off-the-shelf AI tools anyway. Content creation is probably the most visible use case right now but honestly that feels more like prompt engineering than actual ML. I reckon the more interesting opportunities are in stuff like trend detection, A/B testing at scale, or building, smarter segmentation systems, but I'm not sure how much demand there actually is for that from marketing teams vs. just wanting someone who knows how to run ChatGPT workflows. Has anyone here successfully made that pivot or combined both skill sets in a role? Curious whether you've found it easier going in as an ML person learning marketing or the other way around.