Machine Learning Engineer

About 1001

AI is everywhere right now, but true value and real-world impact are still rare. 1001 builds AI-native systems that plug into real operations, where inputs are messy, impact is high, and reliability and correctness actually matter. We are a small, senior engineering team expanding carefully, with a deep focus on technical excellence and ownership. We’re building systems that run live, integrate with multiple sources of truth, and meet a high bar for customer value, performance, and product quality.

PS: We’re not another ChatGPT wrapper ;).

The Role

We are looking for a Machine Learning Engineer to experiment, select, and deploy machine learning models that operate directly inside production workflows.

This is a pure Individual Contributor (IC) role with no people management expectations. You will work closely with software and infrastructure engineers to run meaningful experiments, identify the best-performing approach for a given problem, and then take that model all the way into production.

The scope of this role spans classical machine learning, deep learning, and GenAI / LLM-based systems. Success in this role comes from balancing rigorous experimentation with strong engineering discipline.

Note: This is a 100% onsite role. Due to the nature of our work and the environments we support, you will be required to work from our physical offices and travel occasionally across the Middle East.

What You’ll Work On

• Design and run structured experiments across different model families (classical ML, deep learning, GenAI / LLMs) to determine the best solution for specific tasks.
• Implement models that are designed from day one to operate inside live, production workflows.
• Own the full ML lifecycle - data exploration, feature engineering, training, evaluation, deployment, monitoring, and iteration.
• Work with time-series data, event streams, structured inputs, and unstructured data used by LLM-powered components.
• Build and optimize inference pipelines with real constraints around latency, reliability, and cost.
• Define offline and online evaluation strategies, monitor model performance over time, detect drift, and manage regressions.
• Collaborate closely with software engineers to integrate model outputs into backend services and user-facing systems.
• Ensure models are versioned, reproducible, observable, and safe to operate in production environments.

What We’re Looking For

Must Haves:

• Strong experience as a Machine Learning Engineer working on applied ML systems that were shipped to production.
• Hands-on experience experimenting with and deploying classical ML, deep learning, and/or GenAI / LLM-based systems.
• The ability to design experiments that lead to clear decisions about model choice, tradeoffs, and next steps.
• Fluency in Python with solid software engineering fundamentals.
• Proven experience taking models from experimentation through deployment and long-term operation.
• The ability to reason about failure modes, monitoring, and reliability - not just model accuracy.
• The ability to work 100% onsite and travel within the Middle East region as needed.
• Comfortable operating with ambiguity and taking ownership of high-impact ML components.

Nice to Haves:

• Experience with time-series forecasting, anomaly detection, or sequential decision-making.
• Experience with mathematical optimizations, including MILPs, LPs, CP-SAT, and related solver/optimizer variants.
• Published ML research papers in top conferences (e.g., NeurIPS, COLT, EMNLP, AAAI), with bonus if tied to time sequence predictions and optimizers.
• Experience deploying ML or LLM inference on Kubernetes or in hybrid/on-premise environments.
• Familiarity with prompt design, evaluation, and orchestration for LLM-based systems.
• Experience in early-stage startups or small, high-talent density teams.

Why This Role Is Special

• You will run real experiments, choose the winning approach, and deploy it - end to end.
• You will work across classical ML, deep learning, and GenAI / LLMs in real production systems.
• Models here directly power live workflows, not offline demos.
• Your work influences how complex systems behave in the real world.
• High ownership and direct influence on how ML is practiced at 1001.

Compensation & Benefits

• Above-market rates in MENA with London pay parity across all locations.
• Meaningful ownership in the company.
• Full visa and residency sponsorship included.
• Comprehensive health insurance provided.

If you’re excited by the idea of building production-grade ML systems - spanning classical ML, deep learning, and GenAI - inside serious software platforms, and want your work to actually matter, we’d love to hear from you.