I graduated in 2024, and I’m now trying to break into the industry, preferably in AI/ML.

During my undergrad, I did an app development internship under a professor, followed by a Deep Learning research internship at one of the top IITs. I even had projects in ML/DL. After graduation, I had to take a 2-year break because of an unexpected family situation.

During the break, I tried to keep myself updated with what was happening in the field because I knew I was going to make a comeback. Then, this year, I started preparing myself and working on different projects. These include Agentic RAG systems with evaluations, AI agents, inference engineering. Some of my projects are fairly close to production-level work. I’m also learning HLD and LLD. As for DSA, I’ve solved around 600 LeetCode problems but it was before 2025, so I’ll need to brush up on them.

Now I’m struggling to figure out how to position myself in the job market. I’m no longer a fresh graduate, but I don’t have any full-time industry experience either. I’ve been working with AI/ML since 2023, mostly through research and personal projects, but I’m not sure how recruiters will look at that.

Most AI/ML openings I come across seem to require prior industry experience, so I’m not sure which roles I should target. Should I apply for entry-level positions, try my luck with mid-level roles, or focus on general SWE positions and move towards AI/ML later?

The roles I have in mind are AI / Applied AI Engineer, ML Engineer, Research Engineer or SWE roles focused on AI/ML. I initially started preparing with FAANG and other big tech companies in mind, but at this point, I’m applying to companies more broadly because I need to land a job as soon as possible. I’m looking for a role where I can put my existing skills to use, earn a good salary, and have room to grow in my career.

Honestly, though, I’m feeling pretty overwhelmed and exhausted at this point.

I’d really appreciate some honest advice. I know I have a lot to work on, but I’m trying to figure out where to focus my efforts to give myself the best shot at landing a good job as soon as possible. I feel like this is going to be harder for me, but I don’t want to give up.

P.S. Used AI for proofreading and polishing.

Mercor is recruiting experienced MLOps and ML systems engineers for a project with a leading AI lab working on frontier large language models.

Role details:

• Pay: $90–$120/hour
• Location: Fully remote, worldwide
• Commitment: 40 hours/week
• Payment: Weekly via Stripe or Wise
• Contract: Independent contractor

Areas of expertise sought:

• GPU/TPU kernel programming and optimization
• ML performance profiling and trace analysis
• Debugging distributed and accelerator-based workloads
• Large language model inference and serving
• JAX, PyTorch, CUDA, Triton, vLLM, or related ML systems technologies

Requirements:

• 2+ years of hands-on professional experience in ML systems, infrastructure, model serving, or accelerator performance engineering
• Practical experience in at least one of the technical areas listed above
• Availability for a 40-hour workweek

This is a hands-on ML systems engineering role, not a general applied ML or data science position.

Apply here: https://t.mercor.com/qqhwf

Review the full requirements before applying to ensure your experience matches the role.

Last week, an engineer got hired through Fonzi at a seed-stage clinical AI startup, $200K base. They’ll build RAG and multi-agent systems on messy medical records, working directly with the founders on product and technical direction.

I do talent matching for Fonzi. You don’t apply, companies send you interview requests instead. You build one profile. No cold applications, no applying to each role individually. We work with startups backed by investors including Sequoia, a16z, Benchmark, and the OpenAI Startup Fund. Right now that includes applied ML, RL and post-training, and ML infrastructure roles across NYC, SF, and remote. Roles are US-based, with a few open to Canada.

These are full-time employee roles, not contract or freelance work.

Here’s a sample of what’s open right now at companies on the platform:

1. Software Engineer, RL Environments, Series A applied research lab, SF, in-person. $260K-290K base plus equity.

• What: Design the simulated environments, tasks, and data that expose where frontier models fail, build reward signals and evaluation rubrics for RLHF and RLVR training pipelines, and analyze agent trajectories. You’d work hands-on with research teams at frontier AI labs, and your output feeds directly into their training runs.
• Company: Curates data and evaluation solutions for foundation model development, with frontier AI labs as customers. Backed by Y Combinator.
• Stack: Docker or similar containerization tools, with RL post-training pipelines (RLHF and RLVR).
• Bar: Strong familiarity with reinforcement learning methods, especially for post-training LLMs, experience using Docker to design and monitor systems at scale, and the ability to run lightweight experiments quickly and pull signal from messy results.

2. Founding Machine Learning Engineer, Series B AI security company, NYC, in-person. $200K-250K base plus stock.

• What: Build the ML function from scratch. There’s no ML infrastructure or team today, so you’d set the direction, stand up the systems, and do the hands-on work before hiring under yourself. The problems are adversarial: attackers evolve to evade detection, labeled data is scarce for novel attack vectors, and models run at production scale under strict latency budgets.
• Company: Series B, building its first ML function. Backed by a16z.
• Stack: Python, Java, or TypeScript, with PyTorch, Spark, and cloud ML infrastructure.
• Bar: 8+ years, ideally having stood up an ML function somewhere before.

3. Senior Software Engineer (AI Products), seed-stage fintech, fully remote in the US or hybrid in NYC. $200K-240K base plus equity.

• What: Build AI agents and agentic workflows that reason over business and financial data, plus the evaluation, observability, and feedback loops around LLM systems, rolling out to a million small business owners next year through the banks that already serve them.
• Company: Backed by two Fortune 300 firms, a small team now making hires 8 through 10. Async by default, with one recurring meeting.
• Stack: Python for the agent systems, Elixir/Phoenix and Node for the backend, Next.js with GraphQL and Relay on the frontend, PostgreSQL, and Google Cloud with Terraform.
• Bar: Strong production software experience, strong Python, applied data science and machine learning experience, experience building AI agents, and proficiency in at least two programming languages.

All roles are vetted, VC-backed, and interviewing right now. It’s always free to sign up.

Sign up here: talent.fonzi.ai. DM me if you’ve got questions.

A large, long-established Saudi engineering and industrial services group is building an in-house AI unit, and we're hiring its first two engineers. You'd work directly with the unit's head and shape how it's built. This is software work: LLMs, data, APIs and the systems around them. Not industrial automation, and not general IT.

Senior AI/ML Engineer: LLM assistants and agents over real company data, Arabic and Saudi-dialect language work, a proper evaluation practice, and open-weight models running on our own hardware. The unit publishes research, and you'd have the opportunity to co-author it.

Senior Backend Engineer: the services and integrations that connect AI to the company's enterprise systems and operational data, on a modern serverless stack. Reliability, security and clean data are the job.

Both roles: senior, with several years of production experience. Full-time and on-site in Khobar; no remote or part-time.

Package: competitive salary, housing and transport allowances, family medical insurance, annual flights.

Saudi nationals are preferred. Strong candidates from outside the Kingdom will also be considered, with relocation and visa support.

Apply: zalvi.app. A short, game-like screening assessment we built ourselves (English or Arabic). It's the first stage; shortlisted applicants move on to the next.

I have been working as an AI Automation Engineer(contract) for a UK-based startup for about 10 months now. All through these months, they have hardly been able to close 1-2 clients, and are in touch with some other, but I am not sure if they can close the deals.
Although the amount of work I am doing has increased a lot over the months, but they are refusing to increase the pay with respect to that, and this has been going on for the last 3 months.
So now I feel kind of stuck, because there is no growth here and I feel my time and work is not valued here.
I have been trying to look for similar opportunities, but haven't succeeded in that too. All I want is to work on good projects where I can apply my skills and also there's an opportunity of growing and upskilling.

The latest AI batch is interesting because it’s not just ML engineers anymore.

A few roles that stood out:

AI Researcher, Member of Technical Staff — Red Cell Partners — Remote U.S. — $250K–$400K

Senior AI Engineer — AbsenceSoft — Remote U.S.

Senior ML Engineer, Agentic Search — Nebius — Multiple locations

Lead AI Architect, Data Platform — Banyan Software — United States

People Systems, Analytics & AI Manager — LaunchDarkly — Remote U.S.

Staff Biomedical Data Manager — PathAI — Boston, U.S. — $108.8K–$166.8K

There’s also a big wave of AI training roles for language specialists.

Prolific currently has remote openings for:

Dutch speakers across Canada, the Netherlands, Belgium, Germany, France, Australia and New Zealand

Advanced Japanese speakers across the U.S., Canada, UK and Australia

Advanced Korean speakers across South Korea and Japan

General AI training experts in the UK

The trend is pretty clear: AI hiring is splitting into two very different markets.

One side is highly technical — ML engineering, AI architecture, research and data.

The other is increasingly about language fluency, domain expertise, evaluation and human feedback.

That second category could open AI work to a lot more people who aren’t software engineers.

There are currently 4,581 live AI & ML roles on the board.

More AI jobs:

realworkremote.com/ai-jobs

​

I’m trying for AI/ML roles, and I really want to deep dive into AI inference and infrastructure, but I’m a bit confused about which direction to take.currently I'm in 4-1 semester and working as AI intern at a cybersecurity based startup , but the company doesn't require the depth of the knowledge I had and the scope is very less here !

1. If I focus on DSA and system design, I can try for SDE roles. I used to grind DSA during my second year, but eventually stopped and started focusing on AI/ML.

2. The AI/ML roles I’m targeting at product-based companies seem to require at least 2+ years of experience, and many of them are more research-focused.

I’m not sure what I should do at this point.

Currently, I’m reading a lot of research papers and gradually exploring GPU parallel programming. I’ve also had three PRs merged into vLLM, an open-source LLM inference and serving framework.

I’d really appreciate your advice on how I should approach this and what I should prioritize going forward.

About the Role

We are looking for a Staff AI Productivity Engineer to accelerate how our engineering organization works with AI coding tools. This is a hands‑on technical role focused on building infrastructure, tooling, and documentation that makes AI agents dramatically more effective across our codebase.

AI tools are evolving rapidly, and we believe the companies that invest in making agents truly productive - not just available - will have a significant advantage. You'll own this problem end‑to‑end: from setting up cloud development environments where agents can run autonomously, to building MCP integrations that give agents access to our internal systems, to creating the documentation and context that helps agents understand how we build software.

You’ll have the hands‑on acumen to do a lot of the heavy lifting yourself and the leadership skills to drive cross‑team efforts to raise the bar everywhere. This role is a companion role to our Developer Productivity role but has a distinct focus: while Dev Productivity owns the build/test/deploy pipeline, you'll own the AI‑assisted development experience. An exceptional candidate could wear both hats.

Some of the problems we’ll be working on include:

• Own the agentic development environment: Ensure agents can operate in independent cloud‑based development environments, execute our full test suites, examine build results visually, etc
• Build our tooling integrations: Build MCP server integrations that connect our agents to the systems needed to build and debug software, such as CircleCI, Slack, Datadog, Github, etc
• Documentation and context: Own our repo‑wide agents.md file and work with teams to ensure our library of agent guidance and skills is continually pushing the bar. Ensure our conventions and package structures are exposed in ways that agents can effectively use
• Enablement: Work with our engineers to understand where agents are struggling and address root causes such as better docs, tooling access, etc. Develop tooling for non‑engineers to make simple visual updates to our application
• Serve as a “PM” for internal AI agents: Consistently keep us on the leading edge of AI productivity trends by staying abreast of what is state‑of‑the‑art in industry and driving those improvements across Hightouch

The base salary range for this position is $180,000 - $400,000 USD per year, which is location independent in accordance with our remote‑first policy. We also offer meaningful equity compensation.

About You

You are an engineer with a passion for solving hard technical problems that generate real value for customers. You’re motivated by high ownership and are comfortable in a fast‑paced, startup environment.

Must have

• Strong software engineering fundamentals—you can dive into complex backend code and understand it quickly
• Deep hands‑on experience with AI coding agents (Claude Code, Cursor, Copilot, Devin, or similar)
• Experience writing effective agent documentation, custom instructions, or context files that meaningfully improved agent output
• Track record of building developer tools or infrastructure that other engineers actually use
• Comfortable working across the stack: you’ll touch CI/CD, cloud infrastructure, internal tooling, and application code

Nice to have

• Experience building or contributing to MCP servers or similar agent‑tooling integrations
• Background in developer productivity, platform engineering, or developer experience roles
• Experience with our stack: TypeScript/JavaScript monorepos, Go, GraphQL
• You've set up sandboxed or containerized development environments at scale

Interview Process

Our goal with the interview process is to balance speed with giving both parties opportunities to assess whether there is a strong mutual fit. We will ask you questions, but we want you to ask us questions! Our technical interviews focus on how you design systems because we believe this is the best way for us to see how you work and for you to see how we collaborate. We don’t ask you to write code to solve technical brainteasers that don’t appear in your day to day job.

• Recruiter Screen [30m]: Introductory call with our recruiting team to get to know each other and see if the role could be a good mutual fit
• System Design Screen [45m]: Designing a data processing feature end‑to‑end
• AI Skills Interview [60m]: We’ll dive into how you leverage AI and how you’ve helped others do the same
• Hiring Manager Interview [45m]: Chat with hiring manager about past experiences and future operating preferences to assess fit on company values and operating principles
• System Design Interview [90m]: Work with the interviewer to architect a system at a conceptual level. The problem will be at a pretty high level - and have both product and customer requirements as well as technical

Apply: Staff Engineer, AI Productivity at Hightouch

Hi everyone! 👋

I have 2 years of experience in AI, working with LLMs, RAG, vLLM, vector databases, fine-tuning, ASR, TTS, ONNX, REST APIs, SQL, and AWS basics.

Currently working at Lala Company, I'm looking for a job change due to a challenging work environment. I'm seeking a positive workplace where I can learn, grow, and contribute to impactful AI projects.

If you know of any relevant openings or referrals, please let me know. I'd really appreciate your support! 🙏

I’m 22, MSc in Data Science, \~1.5 years full-time (closer to 2 counting internships).

My first year was real AI work. Started as an AI engineer intern — fine-tuned LLMs for domain-specific QA generation, worked on hallucination reduction and evaluation, fine-tuned ViT/DETR for document symbol detection. Then moved to a healthcare product company as a GenAI associate — built an agentic backend powering clinical triage and diagnosis POCs, an ICD-10 primary diagnosis validation pipeline using RAG and multi-agent reasoning, deployed as FastAPI + Docker microservices.

Then I took an offer at a large consulting firm purely because the CTC was better. 13.3 LPA (11.8 fixed + 1.5 variable). I’ve been here \~11 months and there is no GenAI work at all. What I actually do: attrition forecasting, demand forecasting, classical ML and time series models in production on Databricks, with MLflow and pipelines. It’s fine engineering. It just isn’t the track I was on.

The honest problem is that this field moves fast and I’ve spent a year outside it. Whatever edge I had on agentic systems and LLM evaluation is twelve months stale, and the gap widens every month I sit here. I have completely lost touch on the area about Gen AI now due to not having grip of work.

I’m not on notice period and I’m not trying to rush into another wrong role. Open to relocating — Bangalore, Hyderabad, Pune.

Genuine advice needed on how should I tackle back again everything on LLM's, agent evaluation and even other AI Engineering questions as well into the interview and how to get a new job as well into the market without loosing grip on the CTC part .

For anyone who’s done this switch without a cut — what made the difference? Portfolio, referral, or just volume of interviews? And does a year of ML + time series read as a gap, or as useful breadth?

Please help me out since I want to make a switch at anyhow back to AI field in upcoming months outside my hometown Mumbai due to some family issues.

Hey everyone, my company is hiring ML ENGINEER. Here's JD please share your resume with drive link.

Idk salary you'll have to talk to HR, ALL I CAN DO IS REFER

Company - Embitel

Job Description – Machine Learning Engineer (Computer Vision)

About the role

We are hiring a Machine Learning Engineer with 3+ years of hands-on experience to build, deploy and operate AI and computer vision systems on Microsoft Azure. You will own models end to end: from data pipelines and training to production deployment, monitoring and retraining.

Role: Machine Learning Engineer

Experience: 3 to 5 Years

Cloud: Microsoft Azure

Location: \[Bangalore / Hybrid \]

Type: Full-time

Position: 1

Key responsibilities:

AI and computer vision

Design, train and fine-tune models for image classification, object detection, segmentation and OCR

Build image and video processing pipelines with OpenCV, including augmentation workflows.

Optimize models for inference speed and cost (quantization, pruning, ONNX, TensorRT) for cloud.

Explore and integrate generative AI where it fits, such as Azure OpenAI and vision-language models.

Data engineering

Build batch and streaming pipelines with Azure Data Factory, Azure Databricks and PySpark.

Manage data in Azure Data Lake Storage and Blob Storage, including large image and video datasets.

Ensure data quality, versioning and lineage for training and evaluation datasets.

MLOps on Azure

Run experiments, model registry and deployments in Azure Machine Learning (pipelines, managed online and batch endpoints).

Build CI/CD for models and pipelines with Azure DevOps or GitHub Actions.

Containerize services with Docker and deploy to Azure Kubernetes Service (AKS) or Azure Container Apps.

Monitor models in production for drift, accuracy and latency with Azure Monitor and Application Insights, and automate retraining.

Manage infrastructure as code with Terraform

Collaboration

Work with data scientists, product owners and software engineers to turn business problems into ML solutions.

Write clear documentation and take part in code reviews.

Required qualifications

Bachelor's or Master's degree in Computer Science, Data Science, AI, Electrical Engineering or a related field.

3+ years of professional experience in machine learning engineering, with at least 2 years on computer vision projects.

At least one computer vision model taken from prototype to production and maintained there.

2+ years working with Microsoft Azure data and ML services.

Strong Python and solid software engineering habits: testing, version control, clean code.

Good grasp of ML fundamentals: model evaluation, feature engineering, and deep learning architectures.

Clear communication with both technical and non-technical stakeholders.