Applied Science Machine Learning Engineer (ML)

TL;DR

Applied Science Machine Learning Engineer (ML): Developing high-quality algorithms and ML software that power hirify.global’s HD maps for ADAS with an accent on end-to-end algorithm/ML system design, data pipelines, and production rollout. Focus on improving recall/precision/latency/cost at scale while solving noisy sensor signals, geospatial geometry, ground-truth quality, and large-scale evaluation pipelines.

Company

hirify.global builds real-time HD maps and ADAS technology used by major car manufacturers and technology companies.

What you will do

• Develop algorithms and ML software for hirify.global’s HD maps for ADAS, working with engineers and applied scientists.
• Lead design, implementation, and integration of algorithms, ML systems, and data pipelines from problem framing through experimentation to production rollout.
• Drive measurable improvements in output quality (recall, precision, latency, cost) against customer-facing targets.
• Own well-scoped components across processing pipelines, from upstream input data to validated outputs for downstream consumers.
• Solve complex technical problems at scale, including noisy upstream signals, geospatial geometry, ground truth quality, and large-scale evaluation pipelines.
• Use agile methodologies, document outcomes, and mentor junior engineers and interns with code reviews and interview participation.

Requirements

• 4+ years of professional applied science, machine learning, algorithm development, or related experience.
• Bachelor’s degree minimum in Computer Science, Machine Learning, Computer Vision, Geospatial Science, Statistics, or a related quantitative field (Master’s or PhD is a plus).
• Strong fundamentals in algorithm design and analysis (data structures, complexity reasoning) for geospatial and signal-processing problems.
• Strong fundamentals in machine learning (model training and evaluation, statistics, experimental design).
• Proficiency in Python; experience with at least one ML framework (PyTorch, TensorFlow, or equivalent) and at least one large-scale data processing framework (Spark, Databricks, or equivalent).
• Proficient written and verbal communication in English.

Culture & Benefits

• Hybrid work model: office attendance two days per week; remaining three days can be worked from home or office.
• Personal development budget, paid leave for learning days, and paid access to e-learning resources (e.g., O’Reilly, LinkedIn Learning).
• Enhanced parental leave plus paid leave to care for loved ones and volunteer in local communities.
• Home office setup budget and monthly allowance.
• Option to work from home country and abroad for a set number of days each year.
• Competitive holiday plan, extra day off for birthday, and participation in events like Hackathon and DevDays.

Hiring process

• Application screening followed by assessments and interviews, with thorough follow-up through onboarding.