Post an ML Engineer job.
Hire in days, not months.

Click "Post a job — it's free" at the top of this page — no subscription or credit card required. In the form, describe the ML problem you are solving: the model type (classification, recommendation, generative), the data environment, and whether the role is research or production-focused. List required skills (Python, PyTorch, TensorFlow), set the experience level and compensation range, then publish. Your job goes live immediately. Teams that describe a specific ML problem — instead of a generic title — consistently receive better-matched applications within 24–48 hours. Specificity is the single most effective hiring lever here.

The talent section on this page is pre-filtered for ML and AI engineering specialists. Each profile shows the candidate's headline, core skills, experience level, and a description of their background. Click any profile to see their full portfolio, GitHub projects, work history, and specific model work. Filter further by experience level (junior, mid, senior) and availability (full-time, part-time, contract). For ML roles, prioritize profiles that mention production deployments and specific architectures over those that simply list frameworks — the difference signals real delivery experience versus academic familiarity.

RemoteAI covers the full ML technical spectrum: Python, TensorFlow, PyTorch, JAX, scikit-learn, XGBoost, LightGBM, deep learning, computer vision, NLP, reinforcement learning, and Bayesian modeling. On the infrastructure side: MLflow, Weights & Biases, DVC, Docker, Kubernetes, and cloud ML platforms including AWS SageMaker, GCP Vertex AI, and Azure ML. When posting, distinguish required from preferred skills — this helps candidates self-qualify accurately and reduces applications from engineers who keyword-matched but do not fit your actual stack or problem domain.

Yes — completely free, with no conditions. You can post your first ML job today without entering a credit card or choosing a subscription plan. There are no per-post fees, no time limits, and no feature walls. RemoteAI is free for companies because the business model is built around talent, not employer charges. You get access to the full candidate pool, messaging, application management, and interview coordination — all included at no cost. If you hire, there is no placement fee. The model is straightforward: free to post, free to hire.

Most ML job posts receive their first applications within 24–48 hours. Unlike general job boards where candidates passively scroll, RemoteAI's pool consists of ML engineers actively looking for their next remote role — they check new postings regularly. Posts with a specific problem description, realistic compensation range, and clear experience requirements attract more applications and better-matched candidates faster. If your post is not generating traction within 72 hours, refine the job title and problem statement — vague ML listings consistently underperform specific ones.

Yes. Every profile on RemoteAI is reviewed before being listed publicly. Vetting covers profile completeness, experience consistency, and skill verification. You see a real headline, detailed project descriptions, GitHub or portfolio links, and a stated work history — not a keyword-stuffed summary with no substance. Profiles that do not meet quality standards are not shown in search or on category pages. That said, vetting eliminates noise — it does not replace your technical assessment. Use profiles as a first filter based on domain fit and depth, then validate with your own ML interview or take-home project.

RemoteAI has ML engineers across all seniority levels. Junior engineers (1–2 years) are strong on fundamentals — Python, model training, data pipelines — and motivated to grow in a structured environment. Mid-level (3–5 years) are typically comfortable owning a full training and evaluation cycle independently. Senior engineers (5–10 years) can design systems end-to-end and mentor others. Principal or staff-level engineers (10+ years) are strategic hires who shape ML architecture across teams. Specify the seniority you need in your post — candidates self-qualify based on it, which significantly reduces mismatched interviews.

Yes. RemoteAI has ML engineers from India, the United States, the United Kingdom, Canada, Germany, France, Brazil, Ukraine, and 40+ additional countries — all experienced with remote-first collaboration. Many have worked in distributed teams across multiple timezones and are comfortable with async documentation, video interviews, and global team workflows. If you have a timezone overlap requirement — for example, 4 hours of shared window with US East Coast or European business hours — specify it directly in the job post. Candidates confirm availability before applying, so you only interview engineers who fit your working schedule.

The highest-performing ML job posts on RemoteAI share four things: a specific problem description (not just a title), a realistic compensation range, clear experience requirements (level and years), and an honest account of the tech stack and team stage. Avoid buzzword lists without context. Instead of "must know deep learning and NLP", write "building a RAG system over internal documents using GPT-4o and PGVector — we need someone who has done retrieval pipeline work before." That level of specificity attracts engineers who have actually solved the problem, not just ones who recognize the keywords.

Free plan: create a company account and applications stay inside RemoteAI — dashboard, messaging, AI matching, no email or external link involved. The $199 option: no account, no dashboard — candidates apply straight to your own email or link instead, and your listing is reviewed and live within one business day.