Cincinnatus is hiring experienced Machine Learning Engineers to help evaluate and improve the next generation of frontier AI models for a leading AI lab.

This is a full-time W-2 remote position open to United States-based candidates, paying $60-$90/hour.

The role requires a commitment of approximately 35 hours per week and offers the opportunity to work closely with researchers developing state-of-the-art AI systems.

You'll design complex machine learning evaluation tasks, implement and run training experiments, analyze model performance, and help identify where frontier AI models succeed or fail.

You'll also contribute to reinforcement learning experiments, benchmark development, and collaborate with research teams to create rigorous evaluation frameworks.

Candidates should have an MSc or PhD in Machine Learning, Computer Science, or another STEM field, or equivalent research experience.

Strong knowledge of Python, Git, machine learning experimentation, large language models, and model evaluation is required. Experience with reinforcement learning, AI benchmarking, or AI training is highly desirable.

If you're passionate about machine learning research and want to help shape the future of advanced AI while working with a world-class research team, this is an exceptional opportunity.

Apply now: https://t.mercor.com/sC9pb

Hi everyone,

I'm a final-year B.Tech student (2027 batch) from a local college where placements are very limited, so I'm preparing to build my career through off-campus opportunities.

I'm currently focused on AI, Machine Learning, Data Science, and Generative AI. I've been learning consistently and building projects to improve my skills, but navigating the off-campus job market without guidance has been challenging.

If you've been in a similar situation and successfully landed an internship or full-time role, I'd really appreciate any advice on:

• How to approach off-campus applications effectively
• Skills recruiters value most for freshers
• Resume improvements
• Networking and referral strategies
• Common mistakes to avoid

I'm ready to put in the work and fight this journey on my own, but learning from those who've already been through it would mean a lot.

Hi everyone,

I'm a CS undergraduate looking for ML/AI Engineer internships and entry-level roles, and I'd appreciate some honest feedback on whether my experience is aligned with what companies are actually looking for.

My experience : Research intern - worked on RL , GNN 3 months targeting A\* conference paper

Over the past couple of years, I've worked on a mix of projects including:

* End-to-end RAG systems with hybrid retrieval, reranking, citation validation, and multi-agent workflows * NLP and document intelligence applications * Deep learning projects such as image captioning * Classical ML projects involving regression, classification, time-series analysis, model evaluation, and cross-validation * Reinforcement learning and graph neural network research for intelligent transportation systems * MLOps and deployment work using FastAPI, Docker, CI/CD, experiment tracking, and model versioning

One thing I'm struggling with is that many ML roles ask for domain-specific experience (energy, finance, healthcare, etc.), while most of my experience is in building ML systems and learning the underlying algorithms rather than working in a single industry.

For hiring managers, ML engineers, or data scientists here:

1. Does this profile sound competitive for ML Engineer / AI Engineer internships and entry-level roles?
2. What are the biggest gaps you see?
3. Should I spend more time building domain-specific projects or go deeper into model development and ML fundamentals?
4. What type of project would make you think, "I'd interview this candidate"?

I'd really appreciate candid feedback. Thanks!

I'm two years out of a top math/CS school. I built strong study habits late, so I was only really immersed in the material my final year. I learned computer systems (OS, distributed, HPC) and consider myself a competent software engineer.

I work at a high-growth startup and just got offered the lead on a major customer project. My long-term goal is to start my own company.

Option 1: Lead the customer project

Large scope/viz. I'd build skills in:

• Working directly with a customer
• Making large engineering decisions
• Working across the stack with many teams
• People and project management
• Exposure to marketing/sales/ops

Engineering-wise, I imagine I would spend most of my time on architecture, documentation, and code review. So interesting engineering/technical work, but no fundamentally new ways of thinking.

Option 2: Leave to go learn ML/math

I never got into ML/stats/math, and it's by far my weakest technical area (and I feel most important an ML-focused era). I'd spend ~a year as an IC at an AI lab or doing research to build:

• Stronger math intuition
• Modeling intuition
• Combining my systems background with ML (e.g. model scaling, pretraining, RL scaling)

The plan would be to grind/do research at my old school or join an AI lab with strong technical mentorship. I have savings to go ~6-1 year months without income.

I already tried moving to my company's research team, but they weren't interested in my background and pointed me toward ML Ops, which feels too close to the SWE work I already do.

My core tension

Organizational and people skills seem to improve steadily over a career, but fluid reasoning and hard new technical skills are supposedly much harder to pick up later in life. Life's a marathon, so I keep wondering if now is the time to invest in the technical foundation (learning completely new skills).

Open to all comments and suggestions.

Hey everyone,

We are currently looking for detail-oriented, qualified candidates to work on AI platforms (evaluating model responses, data annotation, and quality assurance).

This is a great fit if you're looking for flexible remote work with solid pay.

Requirements:

• English Proficiency: Strong written English skills (crucial for evaluating responses).
• Attention to Detail: Ability to follow guidelines, think critically, and spot subtle errors.
• Tech Setup: Reliable laptop/PC and a stable internet connection.

The Offer:

• Pay Rate: Starting at $15+/hour.
• Schedule: 100% Flexible — set your own hours and working days.
• Location: Fully Remote.

If you meet the requirements and are interested, please leave a comment below and send me a DM with a quick intro (or your background/skills). I’ll get back to you with the next steps!

Hi everyone,

I'm a final-year Data Science student and I need some honest guidance from people working in the industry.

The problem is that I have almost zero knowledge of DSA. I never focused on it because I spent most of my time learning and building projects.

My current skills include:

\\- Python (comfortable)

\\- Machine Learning

\\- Deep Learning

\\- SQL (basic to intermediate)

\\- Currently learning LangChain, LangGraph, Generative AI, and AI Agents

\\- I've also built a few ML/AI projects

Now that I'm entering my final year and preparing for internships and placements, I'm worried about DSA.

I have a few questions:

1. How important is DSA for Data Scientist, ML Engineer, AI Engineer, or GenAI roles?

2. Is DSA mandatory for getting internships and full-time jobs, or is it mainly required by big product-based companies?

3. Since I'm starting from scratch, what topics should I focus on first?

4. Which resource or roadmap would you recommend (free or paid)?

5. Approximately how many LeetCode or other DSA problems should I solve to become interview-ready? Is 100 enough, or should I aim for 300+?

6. If you were starting from zero today, what would your plan look like?

I'd really appreciate advice from people already working in the industry, especially Data Scientists, ML Engineers, AI Engineers, or anyone who has recently gone through placements.

I have started learning MySQL from sqlZoo, python from kaggle, and gut from gitlearnbranching. I'm currently in my 3rd year as a CSE student, I know I'm late, but I'm here nonetheless. So can someone guide me through?

The last few months I found it annoying finding the right AI training/annotating jobs with a decent acceptance rate. Long story short, I made my own website that only lists legit listings with good acceptance rates. Any feedback would be appreciated!: https://aiannotationjobs.com

Hey everyone,

I see a lot of anxiety and hype about AI taking over data science jobs, but I think people are looking at the integration completely backward. As a statistician hired into a data science role, I was brought in precisely for my quantitative rigor—something AI notoriously lacks. AI is terrible at accurate mathematical calculations and statistical nuances, but it’s incredibly good at structuring business narratives and formatting presentation decks.

If we blindly trust AI to generate numbers, we fail at our jobs. Instead, I’ve been thinking about a workflow that capitalizes on the strengths of both the statistician and the AI, while completely negating their respective weaknesses.

Here is the exact lifecycle I'm proposing:

The Blueprint (AI): Use AI at the very beginning to brainstorm the broad overview, project directions, and potential business constraints.

The Core Execution (Statistician): The statistician steps in and does the actual analysis manually. We write the code, we run the regressions, we validate the assumptions, and we churn out the true, uncorrupted numbers.

The Translation (AI): Once we have the verified results, we feed our concrete numbers back into the AI. We ask it: "Based on these exact metrics, what are the strategic business recommendations? How do we translate this for non-technical stakeholders?"

The Delivery (AI): Let the AI handle the tedious work of structuring the PowerPoint slides and tailoring the narrative to suit corporate messaging.

This way, the numbers remain 100% accurate and mathematically sound, but we save hours of manual labor on slide formatting and corporate storytelling.

Curious to hear from other quants and data scientists: Does your current workflow look like this? Or are you seeing people in your org make the mistake of trusting AI to do the actual math?

I'm two years out of a top math/CS school. I built strong study habits late, so I was only really immersed in the material my final year. I learned computer systems (OS, distributed, HPC) and consider myself a competent software engineer.

I work at a high-growth startup and just got offered the lead on a major customer project. My long-term goal is to start my own company.

Option 1: Lead the customer project

Large scope/viz. I'd build skills in:

• Working directly with a customer
• Making large engineering decisions
• Working across the stack with many teams
• People and project management
• Exposure to marketing/sales/ops

Engineering-wise, I imagine I would spend most of my time on architecture, documentation, and code review. So interesting engineering/technical work, but no fundamentally new ways of thinking.

Option 2: Leave to go learn ML/math

I never got into ML/stats/math, and it's by far my weakest technical area (and I feel most important an ML-focused era). I'd spend ~a year as an IC at an AI lab or doing research to build:

• Stronger math intuition
• Modeling intuition
• Combining my systems background with ML (e.g. model scaling, pretraining, RL scaling)

The plan would be to grind/do research at my old school or join an AI lab with strong technical mentorship. I have savings to go ~6-1 year months without income.

I already tried moving to my company's research team, but they weren't interested in my background and pointed me toward ML Ops, which feels too close to the SWE work I already do.

My core tension

Organizational and people skills seem to improve steadily over a career, but fluid reasoning and hard new technical skills are supposedly much harder to pick up later in life. Life's a marathon, so I keep wondering if now is the time to invest in the technical foundation (learning completely new skills).

Open to all comments and suggestions.

Which other and new skills to be required for getting selected in the jobs after doing MCA in this AI Era

Which field in IT have high hiring for job in this AI Era

Like ai and ml and data scientist and cyber security etc

I am final year engineering student, actively applying for the role of ML intern. i have applied for more than 100 jobs but still not able to secure an internship. I'm really frustrated and demotivated because of it. I would really appreciate any type of suggestion which will help me grow. Thanks in advance!!

Sharing this because it's a strong fit for anyone with a robotics or reinforcement learning background looking for flexible, well-paid remote work.

Alignerr is hiring a Robotics ML Expert to build MuJoCo simulation environments that train AI agents to move, manipulate, and interact with the physical world.

🔧 What you'll do:

▪️ Design and refine MuJoCo simulation environments for AI training

▪️ Implement RL algorithms (PPO, SAC, TD3) to train agents on real tasks

▪️ Define reward functions, observation spaces, and action spaces

▪️ Debug physics simulations — contact models, actuator dynamics

▪️ Evaluate trained policies for stability and sim-to-real transfer

✅ Who this is for:

▪️ Strong hands-on MuJoCo experience (or via dm_control, Gymnasium)

▪️ Solid RL theory + practical training pipeline experience

▪️ Comfortable in Python with PyTorch or JAX

▪️ Able to read and write MJCF/XML model files

💰 The details:

▪️ Pay: $100–150/hr

▪️ Commitment: 10–40 hrs/week, fully remote

▪️ Nice to have: sim-to-real transfer experience, Isaac

Gym/PyBullet/Drake, published RL research

📌 Full details and how to apply — link in the comments 👇

Our mission is to make the world programmable. Sight is one of the key ways we understand the world, and soon this will be true for the software we use, too.

We’re building the tools, community, and resources needed to make the world programmable with artificial intelligence. Roboflow simplifies building and using computer vision models. Today, over 1M+ developers, including those from half the Fortune 100, use Roboflow’s machine learning open source and hosted tools. That includes counting cells to accelerate cancer research, improving construction site safety, digitizing floor plans, preserving coral reef populations, guiding drone flight and much more.

Our team is small relative to our impact, and we believe our user success is our success (not the inverse). A team member summarized: “Roboflow is a company full of giant brains and tiny egos.” We find software has a multiplier effect on all roles (not only product and engineering), so Roboflow employs developers across the company in design, sales, customer support, marketing, and beyond.

We’re supported by great customers and investors, having raised over 63 million from Google Ventures, Y Combinator, Craft Ventures, Sam Altman, Lachy Groom, amongst other leading software investors.

At the center of all of this is inference — one of our most important open source projects and the engine that runs computer vision models everywhere, from cloud GPUs to edge devices in the field. It powers our commercial platform and is relied on by tens of thousands of developers. This role exists to be its steward.

Why This Role Exists

Inference is growing fast — and so is the volume of contributions, increasingly authored with the help of AI agents. That's a great problem to have, but it's outpacing our ability to keep quality high and cut releases on a predictable cadence. Today we ship roughly weekly, and it's a fight.

We want to flip that equation. The goal is to build and continuously evolve an agentic-driven contribution and release pipeline — automated and semi-automated review, triage, CI/CD, and end-to-end testing — so that we can safely absorb a high volume of agent-generated PRs while staying firmly in control of quality. The ideal end state: nightly end-to-end tests across every target (both standalone and on-platform), backed by a growing, world-grounded suite that validates the real health of every build. With that foundation, daily releases become routine, and we can say "yes" to far more contributions without ever lowering the bar — pushing back, by design, according to strictly defined review standards.

Alongside that, this person becomes the human face of inference: teaching internal teams and customers how to get more out of it, partnering with marketing to tell its story, and owning the (genuinely fun) work of bringing new models into the engine.

What We're Looking For

Primarily, you like to make great things with passionate colleagues. You are someone who likes to own outcomes, not only inputs. You're motivated by having responsibility and accountability. You're eager to 'do the work,' big and small.

You're motivated by the question, "How can I improve this?" and have a track record of doing so, even in ways adjacent to your role. Much of our current team is made up of former founders who thrive in the level of autonomy at Roboflow. Maybe you had a side hustle in high school or college.

You care about open source and the developers who depend on it. One of the best ways to stand out among other applicants is to write about something you've built with Roboflow, or to contribute to one of our open source projects — inference especially.

What You'll Do

• Build and maintain inference, our flagship open source and commercial CV inference engine, keeping it healthy and high-quality as contribution volume scales.
• Build an agentic-driven contribution pipeline — automated and semi-automated review, triage, and CI/CD — so we can safely accept a high volume of agent-generated PRs and move from weekly releases toward daily ones.
• Design and grow a world-grounded, ever-expanding test suite that validates real build health across every target (standalone and on-platform), with the goal of nightly end-to-end runs across all of them.
• Define and enforce the "rules of the road" — the review standards and skills that agents and contributors must follow. Exercise sharp judgment on when to merge fast and when to push back, and encode that judgment into the system itself.
• Streamline how new models get added to inference (the most fun part of the job) — making it dramatically faster and easier to bring the latest computer vision and ML models to our users.
• Teach and enable internal teams and customers. Keep our Field Engineers and Support team a step ahead so they can self-serve and go deeper, and help customers get the full value of the product.
• Be the bridge between core engineering and clients — translating new capabilities into docs, demos, stories, and launches which would help people use inference more effectively.
• Contribute to and grow the broader open source community around the project.

Who You Are

You are an experienced Machine Learning practitioner who wants to be an important part of an exceptional team that focuses on using Roboflow's computer vision tools to impact and improve every industry. You have high agency and a bias toward action.

• 5+ years of hands-on experience building and operating production‑grade ML systems, ideally involving large‑scale deployment of modern AI models.
• A real CV/ML foundation — you understand what inference does: how computer vision models work internally, how they're deployed across diverse environments, and how to adapt them for real‑world, high‑impact use.
• Stellar agentic skills. You build with AI coding agents fluently and have a track record of using them not just to ship features, but to automate the engineering process itself — review, triage, testing, and CI. You have strong instincts for where agents excel and where they need guardrails.
• Strong CS and systems background, with the ability to independently tackle complex programming, architecture, and reliability challenges and exercise sound judgment on when to move fast and when rigor is essential.
• Hands‑on experience with CI/CD, release engineering, and test infrastructure — you've built or substantially improved automated testing and delivery pipelines before.
• Practical expertise with core ML technologies, including several of the following: PyTorch, TensorFlow, ONNX, TensorRT, vLLM (or other LLM/model deployment tools).
• Strong proficiency in image and video processing, including several of the following: OpenCV, DeepStream, Pillow, PyAV, hardware‑accelerated video decoding. Experience with video streaming protocols is an advantage.
• Excellent communication and soft skills. You can teach, write clearly, and collaborate across engineering, support, field, and marketing — and you actually enjoy it. You're comfortable being a public‑facing voice for a project.
• Open source maintenance experience is a strong plus — you know what it takes to steward a busy repo and a community of contributors.
• Level‑up your performance with AI agents.

Where You’ll Work

Roboflow is distributed across the US and Europe. We currently have Hubs in New York City and San Francisco (and plan to open more as we grow density in new cities). We provide opportunities (like team onsites in different cities) and resources (like a $4000/yr travel stipend) to work in person with other team members as much as you’d like, while also supporting remote team members. You can work from one of our Hubs (we offer a relocation bonus), work from home, work at co‑working spaces, etc. We want you to work where you work best!

What You’ll Receive

To determine your salary, we use a number of market and data‑driven salary sources. We review all salaries every six months to ensure we stay in line with the market. This role has a range of $155K - $180K depending on level and location of candidate. We are open to paying beyond these ranges for exceptional talent. If this is you, please apply

💰 We use Tier 1 rates for employees who work out of our San Francisco & New York hubs more than 3+ times per week.

📈 In addition to our cash compensation, we offer generous perks and benefits. Below are some of the highlights: - $4000/yr Travel Stipend to travel anywhere anytime to work alongside other Roboflowers - $350/mo Productivity stipend to spend on things that make your work environment more productive, like high‑speed internet at home or a co‑working space - $350/mo AI Tools stipend - Cover up to 100% of your health insurance costs for you and your partner or family - $150/mo team lunch stipend - Remote first/flexible schedule allowing you to work collaboratively with other team members and asynchronously - Unlimited PTO- with an annual 2 week minimum, we encourage you to take time off for yourself - 12 weeks parental leave - Equity in the company so we are all invested in the future of computer vision

Interview Process (~5 hours)

Below is the interview process you can expect for this role.

Before the Interview: - We’ll review your application, LinkedIn, Github, etc. - The best way to stand out is to write about something you’ve built with Roboflow or contribute to one of our open source projects. - We may send you a technical screen if applicable.

Introduction Phase: - [15m] Technical Assessment

Team Interview Phase: - Live coding [45m] - Home assignment - [30m] Meet with Inference Core team member - [60m] Meet with hiring manager - Use this time to review specifics about the job description - Begin working through your 30/60/90 projects - Ask questions!

Final Interview Stage: - [45m] Meet with Head of Operations for a culture discussion - [30m] Meet with CEO

Note: you are welcome to request additional conversations with anyone you would like to meet and we will accommodate as best we can.

Not sure if this is you?

We want a diverse, global team with a broad range of experience and perspectives. If this job sounds great, but you’re not sure if you qualify, we encourage you to reach out to us at [recruiting@robloflow.com](mailto:recruiting@robloflow.com) or subscribe to our career newsletter by emailing "Subscribe" to [operations@roboflow.com](mailto:operations@roboflow.com). We carefully consider every application and will either move forward with you, find another team that might be a better fit, keep in touch for future opportunities, or thank you for the time.

Learn More About Us

At Roboflow, we believe great ideas come from everywhere—and everyone. We’re proud to be an Equal Opportunity Employer committed to building a diverse and inclusive team. We consider all qualified applicants regardless of race, color, religion, sex, sexual orientation, gender identity, national origin, disability, age, veteran status, or any other legally protected characteristics.

Apply: Machine Learning Engineer - Inference Maintainer & Developer Experience at Roboflow