SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Halter is a venture-backed agtech company transforming livestock farming through IoT collar technology. The company enables farmers and graziers to manage herds at scale without traditional infrastructure (quad bikes, dogs, fences), improving productivity and sustainability. Halter is backed by tier-1 investors including Founders Fund, Bessemer Venture Partners, DCVC, and others.
As Device Performance Engineer, you will analyze and optimize the performance of Halter's fleet of millions of IoT collars operating in real-world farm conditions. You'll work at the intersection of firmware, hardware, communications, and data analytics to extract maximum performance and reliability from physical devices deployed on live animals.
Key responsibilities:
- Analyze collar performance at fleet scale: power consumption, battery life, GPS and sensor behavior across tens of millions of devices in diverse real-world conditions
- Build ground-truth understanding of actual product usage on farms versus design assumptions
- Wrangle large, messy real-world datasets (fleet telemetry, logs, field data) to identify signal in noise and distinguish genuine effects from data artifacts
- Apply first-principles reasoning about physical limitations of sensors, radios, and batteries to diagnose performance gaps
- Inform product and hardware design decisions with field-reality insights, not just bench testing
- Communicate technical findings clearly to firmware, hardware, and product teams in actionable terms
- Drive improvements from insight through implementation, validation, and production confirmation
You'll need deep curiosity about how systems actually work, genuine first-principles thinking, and a bias toward ownership and follow-through. The role requires an engineering science, physics, or similarly quantitative technical background with a proven track record applying it to real, messy physical-world problems. Strong data analysis skills are essential—you must be comfortable working with large real-world datasets in Python or similar, including data cleaning, filtering, and validation before drawing conclusions. Excellent communication skills are required to explain technical findings across engineering disciplines.
Bonus qualifications: genuine curiosity about farming, animal welfare, or outdoor hardware; experience in statistics or applied mathematics; experience with optimization problems.
Requirements:
- Engineering science, physics, or similarly quantitative technical background
- Track record applying technical knowledge to real, messy physical-world problems
- Strong data analysis skills; proficiency with Python or similar for large dataset work
- Ability to clean, filter, and validate noisy data before drawing conclusions
- Strong communication skills to explain technical findings to cross-disciplinary engineering teams
- Deep curiosity about how systems work and patience to understand them thoroughly
- First-principles reasoning approach to system analysis
- Ownership mindset and commitment to follow-through on improvements
