Junior Machine Learning Engineer

You’ll be working alongside world-leading experts to develop solutions that tackle real-world challenges. We’re looking for passionate individuals eager to grow in an ethics-driven environment that values sustainability and diversity.

We’re committed to building a truly inclusive culture—come and join us.

As a Machine Learning Engineer, you will

In general: Build data and model pipelines end‑to‑end: create, source, augment, and validate datasets; stand up training/fine‑tuning/evaluation flows; and ship models that meet product and customer requirements.

Design rigorous evaluation frameworks to verify task competence and alignment; implement statistical testing, reliability checks, and continuous evaluation.

Scale training and inference: make effective use of distributed compute, optimize throughput/latency, and identify opportunities for algorithmic or systems‑level speedups.

Improve models post‑training: apply SFT and reinforcement learning methods to enhance model capabilities and safety.

Optimize and specialize models: apply compression techniques to meet performance and footprint targets.

Collaborate across research and engineering and contribute to our shared codebase; uphold engineering standards.

Required Qualifications

This role requires a Bachelor’s degree in ML, Computer Science, Math, Physics, or related field.

Upto 2 years of hands-on experience. Strong programming skills in Python and the modern ML stack and basic software practices (git, unit tests, CI).

Solid grounding in language modelling concepts around training, evaluation, model architecture, and data.

Experience using GPU resources and familiarity with containerized workflows (e.g., Docker) and job schedulers or cloud orchestration.

Ability to read research papers, prototype ideas quickly, and turn them into reproducible, production‑ready code.

Familiarity with AI / agentic workflows, and task automation.

Preferred Qualifications

PhD in ML, Computer Science, Math, Physics, Data Science, or related field, or equivalent industry experience in machine learning, data science, or related roles, with demonstrated experience with NLP or LLMs.

Experience building foundational LLMs from the ground up.

Preferred qualifications by focus area:

Model Evaluation: track record building task‑grounded evals for LLMs, implementing or extending evaluation harnesses; deep understanding of LLM capabilities and quirks, and their ties to architecture and training dynamics.

Distributed Training: Hands‑on experience debugging multi‑node training, profiling/optimizing throughput and memory, and extending training frameworks like to new architectures or optimizers; comfort diagnosing flaky cluster issues.

Model Compression: Strong mathematical background and experience with pruning, quantization, and NAS; ability to formulate and solve constrained optimization problems for accuracy/latency/footprint trade‑offs and to integrate results into production.

Post‑Training: Theoretical and practical familiarity with RL theory, frameworks, and monitoring.

Perks & Benefits

Indefinite contract.

Equal pay guaranteed.

Variable performance bonus.

Signing bonus.

We offer work visa sponsorship (If applicable).

Relocation package (if applicable).

Private health insurance.

Flexible remuneration: hospitality and public transportation.

Eligibility for educational budget according to internal policy.

Hybrid opportunity.

Flexible working hours.

Language classes and discounted lunch options

Working in a high paced environment, working on cutting edge technologies.

Career plan. Opportunity to learn and teach.

Progressive Company. Happy people culture

As an equal opportunity employer, Multiverse Computing is committed to building an inclusive workplace. The company welcomes people from all different backgrounds, including age, citizenship, ethnic and racial origins, gender identities, individuals with disabilities, marital status, religions and ideologies, and sexual orientations to apply.